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. 2020 Aug 13;11(3):419–467. doi: 10.1007/s13167-020-00215-0

Integration of quantitative phosphoproteomics and transcriptomics revealed phosphorylation-mediated molecular events as useful tools for a potential patient stratification and personalized treatment of human nonfunctional pituitary adenomas

Dan Liu 1,2,3,4, Jiajia Li 2,3,4, Na Li 2,3,4, Miaolong Lu 2,3,4, Siqi Wen 2,3,4, Xianquan Zhan 2,3,4,5,6,✉
PMCID: PMC7429628  PMID: 32849927

Abstract

Background

Invasiveness is a very challenging clinical problem in nonfunctional pituitary adenomas (NFPAs), and currently, there are no effective invasiveness-related molecular biomarkers. The post-neurosurgery treatment is much different as for invasive and noninvasive NFPAs. The aim of this study was to integrate phosphoproteomics and transcriptomics data to reveal phosphorylation-mediated molecular events for invasive characteristics of NFPAs to achieve a potential tool for patient stratification, and prognostic/predictive assessment to discriminate invasive from noninvasive NFPAs for personalized attitude.

Methods

The 6-plex tandem mass tag (TMT) labeling reagents coupled with TiO2 enrichment of phosphopeptides and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were used to identify and quantify each phosphoprotein and phosphosite in NFPAs and controls. Differentially expressed genes (DEGs) between invasive NFPA and control tissues were obtained from the Gene Expression Omnibus (GEO) database. The overlapping analysis was performed between phosphoprotiens and invasive DEGs. Gene Ontology (GO) enrichment, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein–protein interaction (PPI) analyses were used to analyze these overlapped molecules.

Results

In total, 1035 phosphoproteins with 2982 phosphorylation sites were identified in NFPAs vs. controls, and 2751 DEGs were identified in invasive NFPAs vs. controls. Overlapping analysis of these phosphoproteins and DEGs exposed 130 overlapped molecules (phosphoproteins; invasive DEGs). GO enrichment and KEGG pathway analyses of 130 overlapped molecules revealed multiple biological processes and signaling pathway network alterations, including cell–cell adhesion, platelet activation, GTPase signaling pathway, protein kinase signaling, calcium signaling pathway, estrogen signaling pathway, glucagon signaling pathway, cGMP–PKG signaling pathway, GnRH signaling pathway, inflammatory mediator regulation of TRP channels, vascular smooth muscle contraction, and Fc gamma R-mediated phagocytosis, which were obviously associated with tumor invasive characteristics. For 130 overlapped molecules, PPI network-based molecular complex detection (MCODE) identified 10 hub molecules, namely SLC2A4, TSC2, AKT1, SCG3, ALB, APOL1, ACACA, SPARCL1, CHGB, and IGFBP5. These hub molecules are involved in multiple signaling pathways and represent potential predictive/prognostic markers in NFPA patients as well as they represent potential therapeutic targets.

Conclusions

This study provided the first large-scale phosphoprotein profiling and phosphorylation-related signaling pathway network alterations in human NFPA tissues. Further, overlapping analysis of phosphoproteins and invasive DEGs revealed the phosphorylation-mediated signaling pathway network changes in invasive NFPAs. These findings are the precious resource for in-depth insight into the molecular mechanisms of NFPAs, as well as for the discovery of effective phosphoprotein biomarkers and therapeutic targets for invasive NFPAs.

Electronic supplementary material

The online version of this article (10.1007/s13167-020-00215-0) contains supplementary material, which is available to authorized users.

Keywords: Nonfunctional pituitary adenomas, Invasiveness, Tandem mass tag (TMT) labeling, TiO2 enrichment, Quantitative phosphoproteomics, Transcriptomics, Differentially expressed genes, Overlapped molecule (phosphoprotein, invasive DEG), Signaling pathway, Patient stratification, Prognostic/predictive assessment, Personalized treatment

Introduction

Pituitary adenoma is a common intracranial tumor that occurs in the anterior lobe of the pituitary gland, which seriously impacts the human endocrine systems [1–4]. Pituitary adenomas are classified into functional pituitary adenomas (FPAs) and nonfunctional pituitary adenomas (NFPAs) [5]. FPAs have the clinically increased levels of the corresponding blood hormones, whereas NFPAs do not have any clinically increased levels of blood hormones to cause the difficulty in its early diagnosis. NFPA is often diagnosed when it grows up to compress its surrounding tissues and organs. Further, NFPAs are classified into invasive and noninvasive NFPAs [6–8]. Noninvasive NFPA is easily treated with neurosurgery. However, the treatment of invasive NFPAs is a big challenge because its invasive behavior injures or damages tumor-surrounding structures, which cannot be completely removed with neurosurgery and causes a risk of recurrence after neurosurgery. Thus, patients with invasive NFPAs are often treated with radiation therapy after neurosurgery. The clinical diagnosis of invasive NFPAs is mainly derived from nuclear magnetic resonance (NMR) image changes and tumor morphological changes observed by neurosurgery, which actually is not fully correct because it is not easy to determine its invasiveness when this tumor is at its early stage with small size [9, 10]. Currently, there are no effective invasiveness-related biomarkers used in clinical practice. Characterizing any invasiveness-related molecular events in NFPAs may benefit the patient stratification and prognostic assessment to discriminate invasive from noninvasive NFPAs for personalized medical procedures. There is an urgent need to discover the changed molecular events for invasive NFPAs.

It is well-known that invasive NFPA is a multicause, multiprocess, and multiconsequence disease, with a series of molecular changes at the levels of genome, transcriptome, proteome, and metabolome, and those molecules interact mutually in a molecular network system [4, 11, 12]. It is driving one to shift the previous single factor model to multiparameter systematic model. Multiomics is an effective approach to realize this multiparameter systematic model shift [13–15]. The proteome and transcriptome are the functional performer of genome, and the proteome and transcriptome are regulated by extensive post-translational modifications (PTMs) [16, 17].

Among those PTMs, phosphorylation is an important and extensively studied PTM with the addition of phosphorus group (–HPO3 to –OH or –H3PO4 to –NH2) to residues such as serine (Ser, S), threonine (Thr, T), and tyrosine (Tyr, Y) in a protein, which plays crucial roles in signaling pathways and many pathophysiological processes [18]. Phosphorylation and dephosphorylation are reversibly dynamic reactions that are catalyzed by kinases and phosphatases, respectively, which regulate the basic biological functions [19, 20]. Phosphorylation in a protein promotes the conformational changes through interacting with other hydrophilic and hydrophobic residues [19]. Human genome sequencing identifies 107 human phosphatase genes and 518 human protein kinase genes including 90 known tyrosine kinases that include 58 receptor tyrosine kinases. These kinases and phosphatases are the potential targets of anticancer drugs, and tyrosine kinases accounting for 0.3% of genome contribute to 30% of 100 known dominant oncogenes [18, 21]. In eukaryotic cells, protein phosphorylation is a low abundance event, and serine phosphorylation accounts for ~ 90%, threonine phosphorylation for ~ 10%, and tyrosine phosphorylation for ~ 0.05% [18]. Identification and characterization of the altered phosphorylation and functional activities of phosphoproteins in different types of cancers have directly assisted in the discovery of protein kinase inhibitors to treat a tumor [22, 23]. Therefore, it emphasizes the scientific merits of investigating phosphoproteins in pituitary adenomas.

Tandem mass tag (TMT) labeling/TiO2 phosphopeptide enrichment-based quantitative phosphoproteomics [24, 25] is an effective method to identify phosphoprotein amino acid sequence and phosphorylation sites, and quantify the level of phosphorylation in cancers compared to controls. Briefly, the proteins from cancer and control tissues, respectively, are digested with trypsin, followed by TiO2 enrichment of phosphopeptides, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The MS/MS data are used to determine the amino acid sequence and phosphorylation sites, and the TMT reporter ions were used to quantify the level of phosphorylation. The studies on phosphorylation in single molecules have been extensively carried out in pituitary adenomas with documented 267 publications when the key words “phosphorylation and pituitary adenoma” were used to search the PubMed database, and those studies are mainly involved in phosphorylation-involved signaling pathways. However, only five publications were found in the PubMed database that are about phosphoproteome or phosphoproteomics in pituitaries or pituitary adenomas [26–30]. Of these publications, Carretero et al. studied the phosphorylation of ERK (extracellular signal–regulated kinase) and RSK (ribosomal S6 kinase) for thyrotropin-releasing hormone (TRH)-induced inhibition of rat ether-à-go-go-related (r-ERG) channel potassium currents in rat pituitary growth hormone 3 (GH3) cells [26]. Zhao et al. used proteomics to reveal abnormally phosphorylated AMPK (AMP-activated protein kinase) and ATF2 (activating transcription factor 2) involved in glucose metabolism and tumorigenesis of growth hormone (GH)-secreting pituitary adenomas [27]. Delcout et al. found the role of phosphorylation in pituitary adenylyl cyclase activating polypeptide (PACAP) type I receptor transactivation for insulin growth factor-1 (IGF-1) receptor signaling and antiapoptotic activity in neurons [28]. Beranova-Giorgianni et al. performed phosphoproteomics analysis of the human pituitary and identified 28 phosphoproteins [29]. Zhan et al. used PTMScan experiment that combined immunoaffinity enrichment and LC-MS/MS to analyze a total of 1006 unique phosphorylation sites within 409 proteins in more than 19 signaling pathways in human NFPAs relative to control pituitary tissues [30], and found that lots of hub molecules in many signaling pathways such as mTOR (mammalian target of rapamycin), PI3K/Akt (phosphatidylinositol 3 kinase/protein kinase B), NFκB (nuclear factor kappa-B), Wnt, p38, ERK/MAPK (extracelluar signal-regulated kinase/mitogen-activated protein kinase), and JNK signaling pathways in NFPAs were phosphorylated in NFPAs, and that mTOR, PI3K/AKT, NFκB, Wnt, p38, ERK/MAPK, and JNK signaling pathways were excessively activated in NFPAs [30]. Those publications clearly demonstrated the important roles of protein phosphorylations in pituitary adenoma pathophysiological processes. However, the large-scale global phosphoproteomics analysis has not been carried out in human NFPA tissues. The large-scale profiling of phosphoproteome in human NFPA tissues has important scientific value to understand in-depth the molecular mechanism of NFPAs and discover effective phosphoprotein biomarkers in NFPA patients.

Transcriptome is another level of functional performer of genome. In-depth investigation of transcriptome alterations in invasive NFPAs will directly benefit for the discovery of invasiveness-related molecular events. The public Gene Expression Omnibus (GEO) dataset includes the transcriptomics data between invasive NFPAs and controls, which can be directly extracted to identify differentially expressed genes (DEGs), followed by integration with phosphoproteomics data for comprehensive consideration of invasiveness-related molecular events in invasive NFPAs, from the view point of the multiparameter systematic model.

This study used quantitative phosphoproteomics based on TMT labeling in combination with TiO2 enrichment of phosphopeptides to identify the large-scale phosphoprotein profile in human NFPA relative to control pituitary tissues, followed by bioinformatics analysis to determine the functional characteristics of phosphoproteins in NFPAs. Further, these identified phosphoproteins and DEG data identified between invasive NFPAs and controls were integrated to determine the overlapped molecules (phosphoproteins; invasive DEGs). Gene ontology (GO) enrichment and the Kyoto Encyclopedia Gene and Genome (KEGG) pathway analysis were performed to determine the functional roles of invasiveness-related phosphoproteins (the overlapped molecules) in NFPAs. These overlapped molecules (phosphoproteins; invasive DEGs) were the precious resource to understand the molecular mechanisms of invasive NFPAs and discover invasiveness-related phosphoprotein biomarkers for potential prognostic/predictive assessment, and patient stratification to discriminate invasive from noninvasive NFPAs, for personalized attitude in medical services of invasive NFPAs. Figure 1 shows the experimental flowchart to identify phosphoproteins in NFPAs and the integrative analysis of phosphoproteomics and transcriptomics data of invasive NFPAs.

Fig. 1.

Fig. 1

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Experimental flowchart to identify phosphoproteins in NFPAs. Thanks to the 6-plex TMT labeling, it is possible to run six samples at one MS analysis. T1, T2, and T3 were three equal amounts of NFPA protein samples. C1, C2, and C3 were three equal amounts of control pituitary protein samples. The amount of T1, T2, and T3 was equal to that of C1, C2, and C3. Each protein sample was reduced with dithiothreitol (DTT), alkylated with iodoacetamide, and digested with trypsin. NFPA, nonfunctional pituitary adenoma; TMT, tandem mass tag; LC, liquid chromatography; MS/MS, tandem mass spectrometry; GO, gene ontology; KEGG, Kyoto Encyclopedia Gene and Genome; DEG, differentially expressed gene

Methods

Pituitary adenoma and control tissue samples

Control post-mortem pituitary tissues (n = 4; tissues from 3 white and 1 black patients) were from the Memphis Regional Medical Center (n = 4), which were approved by the University of Tennessee Health Science Center Internal Review Board. Pituitary adenoma biopsy tissues (n = 4; tissues from 4 Chinese patients) were from the Department of Neurosurgery of Xiangya Hospital, China, and were approved by the Xiangya Hospital Medical Ethics Committee of Central South University (Table 1). Written informed consent was obtained from each patient for pituitary adenoma biopsy tissues or the family of each control post-mortem pituitary subject after full explanation of the purpose and nature of all experimental procedures. Quantitative phosphoproteomics was performed with the four mixed NFPA samples and the four mixed control samples.

Table 1.

Clinical information of NFPA and control tissue samples

Group Sex Age Clinical information Immunohistochemistry Experiments
NFPA Male 58 Chinese, NFPA in sellar region. Sellar floor bone destruction, enriched blood supply in tumor, and tumor size 4.5 × 3 × 3 cm3 ACTH (−), hGH (−), PRL (−), FSH (−), LH (−), TSH (−) Proteomicst
Male 53 Chinese, NFPA in sellar region. Sellar floor bone thinning and tumor size 3 × 3 × 2.5 cm3 ACTH (−), hGH (−), PRL (−), FSH (−), LH (−), TSH (−) Proteomics
Female 43 Chinese, NFPA in sellar region. Sellar floor bone thinning, enriched blood supply in tumor, and tumor size 4 × 3 × 3 cm3 ACTH (−), hGH (−), PRL (−), FSH (+), LH (−), TSH (−) Proteomics
Female 43 Chinese, NFPA in sellar region. Adhesion of surrounding tissues, and tumor size 4.5 × 4 × 6 cm3 ACTH (−), hGH (−), PRL (−), FSH (+), LH (−), TSH (−) Proteomics
Control Male 36 White American, multiple toxic materials. Blood alcohol = 0.5 g/L. Blood: HepB (+), HepC (−), HIV (−) DNT Proteomics
Female White American, 15 h gunshot wound to head. No drugs or alcohol. Blood: HepB (−), HepC (−), HIV (−) DNT Proteomics
Female 34 Black African American, gunshot wound to chest. Blood alcohol = 0.3 g/L; no drugs. Blood: HepB (+), HepC (−), HIV (−) DNT Proteomics
Female 40 White American, multiple toxic compounds. Blood: HepB (+), HepC (+), HIV(−) DNT Proteomics
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DNT, did not test, which means hormone expressions were not tested in each control pituitary tissue with immunohistochemistry; ACTH, adrenocorticotropic hormone; hGH, human growth hormone; PRL, prolactin; FSH, follicle-stimulating hormone; LH, luteinizing hormone; TSH, thyrotropin-stimulating hormone or thyrotropin; −, negative; +, positive

Protein extraction and quantification

A volume (1 mL) of urea pyrolysis solution [9 M urea, 2.5 mM sodium pyrophosphate, 20 mM 2-hydroxyethyl (HEPES), 1 mM β-glycerophosphate, and 1 mM sodium orthovanadate, and pH 8.0] was added to each tissue sample (100 mg), which was treated with an ultrasonic ice bath (100 W, 10 s; interval 10 s; 10 times), and then followed by centrifugation (18,000×g, 10 min, 4 °). The supernatant was the sample of extracted proteins, whose protein content was measured with a Bradford protein quantification kit (YEASEN, Cat# 20202ES76).

Protein digestion with trypsin

Four control protein samples were equally mixed as control protein sample (1.5 mg/sample × 4 = 6 mg), and four NFPA protein samples were equally mixed as NFPA protein sample (1.5 mg/sample × 4 = 6 mg). An amount (300 μg) of each mixed sample (control; NFPA) was mixed with a volume of 1 M dithiothreitol (DTT) (the final DTT concentration was 10 mM) (control: n = 3; tumor: n = 3), followed by incubation (37 °C; 2.5 h) and cooling to room temperature, and then mixed with a volume of 1 M iodoacetamide (the final iodoacetamide concentration was 50 mM), followed by incubation (in the dark; 30 min). The urea concentration was diluted to 1.5 M with five volumes of water. Finally, the mixture was mixed with trypsin (2 μg/μL) at 1:50 (v:v) and then was digested (37 °C; 18 h), followed by desaltation and lyophilization using an SPE C18 column (Waters WAT051910).

TMT labeling of tryptic peptides and enrichment of phosphopeptides

For each sample (control and NFPA, respectively), a total of 300 μg tryptic peptides was equally divided into three parts (100 μg/part) (control: n = 3 parts; NFPA: n = 3 parts). The 6-plex TMT sixplexTM isobaric label reagent set (Thermo Scientific) was used to label those six parts of tryptic peptides, respectively. The six labeled tryptic peptide samples were equally mixed and lyophilized in vacuum. The lyophilized TMT-labeled peptide was resuspended in 1× DHB buffer that was mixed (1:4) with one volume of 5× DHB [3% DHB, 80% acetonitrile (ACN), and 0.1% trifluoroacetic acid (TFA)] and four volumes of water. The TiO2 beads were added to the resuspended TMT-labeled peptide mixture and shaken for 40 min, followed by centrifugation (5000×g, 1 min) to remove the supernatant. The TiO2 beads with phosphopeptides were washed with 50 μL of washing buffer I (30% ACN and 3% TFA) (3×) and then with 50 μL of washing buffer II (80% ACN and 0.3% TFA) (3×) to remove the remaining nonbinding peptides. The phosphopeptides were eluted with 50 μL of elution buffer (40% ACN and 15% NH3·H2O) (3×) and then lyophilized. The dried TiO2-enriched phosphopeptides were redissolved in a volume (30 μL) of 0.1% TFA for LC-MS/MS analysis.

LC-MS/MS of enriched phosphopeptides

A volume (20 μL) of TiO2-enriched phosphopeptides was analyzed with LC-MS/MS. First, phosphopeptides were separated with a high-performance liquid chromatography (HPLC) system EASY-nLC1000 at nanoliter flow rate. The enriched phosphopeptide sample was automatically loaded onto Thermo Scientific EASY column (2 cm × 100 μm; 5 μm C18) that was balanced in 95% of liquid A (0.1% formic acid aqueous solution), then the enriched phosphopeptides were separated by an analytical column (75 μm × 250 mm 3 μm C18 at a flow rate of 250 nL/min) with a linear gradient of solution B (0.1% formic acid in 84% ACN aqueous solution): from 0 to 55% during 0–220 min, 55 to 100% during 220–228 min, and maintaining at 100% during 228–240 min. The LC-separated peptides were online input into a Q-Exactive mass spectrometer (Thermo Finnigan) for MS/MS analysis. The MS parameters were set as follows: positive-ion scan mode, precursor ion scan range 350–1800 m/z with a resolution 70,000 at m/z 200, automatic gain control (AGC) target 3e6, maximum inject time 20 ms, number of scan ranges 1, and dynamic exclusion 30.0 s. For each MS scan, the 10 most abundant precursor ions were selected for MS/MS analysis. The MS/MS parameters were set as follows: high-energy collision dissociation (HCD) ion fragmentation, isolation window 2 m/z, resolution 17,500 at m/z 200, maximum injection time 60 ms, normalized collision energy 29 eV, and underfill ratio 0.1%. The MS/MS spectra were used to search the protein database (Uniprot_human_154578_20160815.fasta; 154,578 human sequences; downloaded on 15 August 2016) with MASCOT engine (Matrix Science, London, UK; version 2.2) embedded into Proteome Discoverer 1.4 (Thermo Scientific).

Database searching and functional characteristics of phosphoproteins

Phosphoproteins and phosphosites were identified with MS/MS data using the MASCOT software. The searching parameters were set as follows: MS/MS ion search, trypsin, 2 max missed cleavages, fixed modifications (carbamidomethyl at residue C, TMT 6 plex at the N-terminal, TMT 6 plex at residue K), variable modifications (oxidation at residue M, phosphorylation at residues S, T, and Y), ± 20 ppm for peptide mass tolerance, ± 0.1 Da for fragment mass tolerance, ESI-TRAP (electrospray ionization-ion trap) for instrument type, unrestricted protein mass, true for decoy database pattern, decoy for database pattern, and peptide FDR (false discovery rate) < 0.01. The MS/MS data were used to determine phosphoprotein amino acid sequences and phosphosites. The intensities of TMT-reporter ions were used to determine the phosphorylation level in NFPAs compared to controls. GO enrichments, including cellular components (CCs), molecular functions (MFs), and biological processes (BPs), were analyzed with Cytoscape ClueGO to reveal the functional characteristics of identified phosphoproteins. KEGG pathway enrichment analysis was used to obtain the statistically significant signaling pathways found on the basis of identified phosphoproteins. P value for GO enrichment analysis was obtained by two-sided hypergeometric test and corrected by Benjamini–Hochberg. P value for pathway enrichment analysis was obtained by two-sided hypergeometric test and corrected by Q value. The level of statistical significance was set as P < 0.05.

Transcriptomics data of invasive NFPAs relative to control pituitaries

The microarray gene data GSE51618 datasets of human pituitary adenomas were obtained from the public GEO database (http://www.ncbi.nlm.nih.gov/geo/) at the National Center for Biotechnology Information (NCBI). It contained 4 invasive NFPA tissue samples and 3 control pituitary tissue samples, which were analyzed with a gene chip human genome platform (Agilent-014850 Whole Human Genome Microarray 4x44K G4112F) in another laboratory. The R-software (The R Foundation for Statistical Computing, https://www.r-project.org/) was used to analyze DEGs between invasive NFPAs and controls. FDR < 0.05 and fold changes (FC) ≥ 2 were used to determine each DEG.

Overlapping analysis of phosphoprotein data and invasive DEG data

The gene name of each phosphoprotein was obtained from the UniProt human database. The overlapping analysis was carried out between the gene names of phosphoproteins in NFPAs and DEG data between invasive NFPAs and controls to obtain the overlapped molecules (invasive DEGs; phosphoproteins). DAVID GO and KEGG pathway enrichments were used to analyze those overlapped molecules, with statistically significant parameters (P < 0.05 and gene count > 3). Each P value was corrected with FDR for multiple testing.

Protein–protein interaction and hub molecules with molecular complex detection based on overlapped molecules (invasive DEGs; phosphoproteins)

To investigate their interactive associations, all overlapped molecules between phosphoprotein data and invasive DEG data were mapped to the STRING database. The protein–protein interactions were analyzed by Cytoscape software (version 3.2.1; National Resource for Network Biology) to obtain the protein–protein interaction (PPI) network. The criteria of hub molecule searching were set as a molecular complex detection (MCODE) score > 5 and a statistically significant difference (P < 0.05).

Results

Phosphorylation profiling in NFPAs

Totally, 2982 phosphorylation sites within 2076 phosphopeptides derived from 1035 phosphoproteins were identified with TMT–TiO2 enrichment–LC-MS/MS in NFPAs and controls, including 1207 (1207/2076 = 58%) quantified phosphopeptides with 1660 phosphorylation sites and 869 (869/2076 = 42%) qualified phosphopeptides with 1322 phosphorylation sites (Supplemental Table 1). A representative MS/MS spectrum was from the phosphorylated peptide 132ERADEPQWSLYPSDSQVS*EEVK153 ([M+3H]3+, m/z = 1039.83, S* = phosphorylated serine residue) derived from secretogranin-1 (Swiss-Prot No. P05060) (Fig. 2), with a high-quality MS/MS spectrum, excellent signal-to-noise (S/N) ratio, and extensive product-ion b-ion and y-ion series (b4, b5, b6, b7, b8, b9, b10, b11, y1, y2, y3, y4, y5, y6, y7, y8, y9, y10, y11, y12, and y16). The phosphorylation site was localized to amino acid residue Ser149, and the phosphorylation level was significantly decreased in NFPAs compared to controls (ratio of T/N = 0.29; P = 2.80E-05) (Supplemental Table 1). With the same method, each phosphopeptide and phosphorylation site was identified with MS/MS data (Supplemental Table 1). Among 2076 identified phosphopeptides, 1362 (1362/2076 = 66%) phosphopeptides only contained one single phosphorylation site, and 714 (714/2076 = 34%) phosphopeptides contained at least two phosphorylation sites. Among 2982 identified phosphorylation sites, 2591 (2591/2982 = 86.89%) phosphorylation sites occurred on residue Ser, 357 (357/2982 = 11.97%) phosphorylation sites on residue Thr, and 34 (34/2982 = 1.14%) phosphorylation sites on residue Tyr. Among 1035 phosphoproteins, 486 phosphoproteins were identified with only one phosphorylation site, 242 phosphoproteins with 2 phosphorylation sites, 133 phosphoproteins with 3 phosphorylation sites, 61 phosphoproteins with 4 phosphorylation sites, 39 phosphoproteins with 5 phosphorylation sites, and 74 phosphoproteins with over 5 phosphorylation sites (Supplemental Table 1), including CIC (n = 6 sites), C2CD2L (n = 6), ARHGEF11 (n = 6), EPB41L2 (n = 6), PHF2 (n = 6), POMC (n = 6), VIM (n = 6), NEFH (n = 6), BRAF (n = 6), HTT (n = 6), MECP2 (n = 6), MSH6 (n = 6), SRSF2 (n = 6), TJP1 (n = 6), EPB49 (n = 6), TCOF1 (n = 6), SAFB (n = 6), SMN1 (n = 6), CTR9 (n = 6), KIF21A (n = 6), ZC3H18 (n = 6), RSF1 (n = 6), IRF2BPL (n = 6), THRAP3 (n = 6), CTAGE5 (n = 7), EIF5B (n = 7), MAP4 (n = 7), SUB1 (n = 7), MFAP1 (n = 7), HNRNPUL2 (n = 7), AAK1 (n = 7), ZC3H13 (n = 7), TNKS1BP1 (n = 7), HNRNPC (n = 8), PRPF4B (n = 8), SMARCC2 (n = 8), LMNA (n = 8), PML (n = 8), ADD1 (n = 8), ADD2 (n = 8), PSIP1 (n = 9), ANK1 (n = 9), ANK2 (n = 9), TOP2B (n = 9), TRIM2 (n = 9), TGOLN2 (n = 10), AHNAK (n = 10), SPARCL1 (n = 10), MARCKS (n = 11), HIRIP3 (n = 11), SCG2 (n = 12), LEO1 (n = 12), ACIN1 (n = 12), SPTBN1 (n = 13), FAM169A (n = 13), ATRX (n = 14), AKAP12 (n = 14), EPB41L3 (n = 14), MAPT (n = 15), NEFM (n = 15), MAP2 (n = 15), BCLAF1 (n = 15), HTATSF1 (n = 17), CHGA (n = 19), NUCKS1 (n = 22), IWS1 (n = 23), MAP1A (n = 24), TP53BP1 (n = 26), FGA (n = 35), SRRM1 (n = 42), MAP1B (n = 42), CHGB (n = 50), and SRRM2 (n = 76). These highly phosphorylated proteins might play important roles in the NFPA pathogenesis.

Fig. 2.

Fig. 2

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A representative MS/MS spectrum of phosphopeptide derived from NFPAs. The identified phosphopeptide 132ERADEPQWSLYPSDSQVS*EEVK153 ([M+3H]3+, m/z = 1039.83, S* = phosphorylated serine residue) was derived from secretogranin-1 (Swiss-Prot No. P05060)

Functional characteristics of phosphoproteins

Those identified 1035 phosphoproteins between NFPAs and controls were analyzed with GO enrichment analysis, including CCs (Supplemental Table 2), MFs (Supplemental Table 3), and BPs (Supplemental Table 4).

CC analysis revealed those phosphoproteins were mainly distributed in the nucleoplasm, cytosol, cell–cell adherens junction, cytoplasm, nuclear speck, nucleus, membrane, focal adhesion, cytoskeleton, protein complex, nuclear matrix, spliceosomal complex, perinuclear region of cytoplasm, Golgi apparatus, chromatin, intracellular ribonucleoprotein complex, Z disc, extracellular exosome, actomyosin, secretory granule, postsynaptic density, costamere, spectrin, microtubule, cell cortex, nuclear heterochromatin, spindle, synaptic vesicle, nuclear membrane, microtubule-associated complex, T-tubule, chromosome, transport vesicle, nuclear envelope, transcriptional repressor complex, PcG protein complex, npBAF complex, Sin3 complex, cAMP-dependent protein kinase complex, soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex, and centrosome (Supplemental Table 2).

MF analysis revealed that the identified phosphoproteins were mainly involved in RNA binding, protein binding, cadherin binding involved in cell–cell adhesion, nucleotide binding, actin filament binding, structural constituent of cytoskeleton, chromatin binding, calmodulin binding, structural molecule activity, microtubule binding, mRNA binding, actin binding, protein kinase binding, 14-3-3 protein binding, spectrin binding, vinculin binding, nucleosomal DNA binding, protein domain–specific binding, translation initiation factor activity, helicase activity, protein binding bridging, protein serine/threonine kinase activity, ion channel binding, histone deacetylase binding, kinase activity, intramolecular transferase activity phosphotransferases, and GTPase activator activity (Supplemental Table 3).

BP analysis revealed that those identified phosphoproteins were mainly involved in multiple biological processes, including cell–cell adhesion, mRNA splicing via spliceosome, RNA splicing, mRNA processing, viral process, RNA processing, mRNA export from nucleus, RNA export from nucleus, microtubule cytoskeleton organization, mRNA 3′-end processing, regulation of alternative mRNA splicing via spliceosome, termination of RNA polymerase II transcription, regulation of cellular response to heat, negative regulation of mRNA splicing via spliceosome, ER to Golgi vesicle-mediated transport, protein sumoylation, membrane fusion, ATP-dependent chromatin remodeling, regulation of mRNA stability, chromatin remodeling, platelet aggregation, regulation of translational initiation, positive regulation of axon extension, negative regulation of transcription DNA-templated, protein phosphorylation, IRE1-mediated unfolded protein response, covalent chromatin modification, platelet activation, and actomyosin structure organization (Supplemental Table 4).

KEGG pathway network analysis identified 31 statistically significant signaling pathways involved in phosphoproteins (Table 2), including spliceosome, platelet activation, RNA transport, endocytosis, mTOR signaling pathway, vascular smooth muscle contraction, SNARE interactions in vesicular transport, proteoglycans in cancer, insulin signaling pathway, glucagon signaling pathway, cGMP–PKG signaling pathway, focal adhesion, estrogen signaling pathway, progesterone-mediated oocyte maturation, protein processing in endoplasmic reticulum, gap junction, gonadotropin-releasing hormone (GnRH) signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and mRNA surveillance pathway.

Table 2.

Statistically significant KEGG signaling pathways identified from 1035 phosphoproteins in NFPAs. Count means the number of genes enriched in this pathway

Category Pathway name Count % Fold enrichment P value Benjamini Gene name of phosphoprotein
KEGG_PATHWAY hsa03040:Spliceosome 32 3.41 4.57 8.32E-13 1.83E-10 SRSF1, NCBP1, CHERP, SRSF10, TRA2A, WBP11, CTNNBL1, SF3B1, DDX23, RBM8A, PCBP1, USP39, ACIN1, HNRNPC, PRPF40B, RBM25, PRPF40A, DDX42, SNW1, PRPF3, HNRNPA1, SF3A1, HNRNPU, SRSF2, SRSF7, SRSF6, SRSF9, SNRNP200, SLU7, PRPF38B, THOC1, RBM17
KEGG_PATHWAY hsa04611:Platelet activation 22 2.35 3.22 3.32E-06 3.65E-04 TLN1, LYN, TLN2, STIM1, ARHGAP35, MYL12B, PRKG1, ARHGEF12, ITPR1, ITPR2, AKT1, MAPK1, FGA, FGB, GP1BB, MAPK3, PPP1R12A, GP1BA, GNAS, PRKACB, SNAP23, ITGA2B
KEGG_PATHWAY hsa03013:RNA transport 24 2.56 2.65 2.89E-05 2.12E-03 CLNS1A, NCBP1, NUP98, EIF5B, RNPS1, NUP155, SMN1, PNN, PABPC4L, EIF4B, EIF4G1, EIF3CL, EIF4G2, EIF4G3, NUP214, EIF3B, EIF3G, RAE1, RBM8A, SRRM1, ACIN1, RANBP2, THOC1, EIF2B5
KEGG_PATHWAY hsa04144:Endocytosis 28 2.99 2.21 1.40E-04 7.70E-03 CHMP2A, USP8, ASAP2, PML, ASAP1, EPS15L1, ARFGEF2, CHMP2B, GBF1, VPS4B, DNAJC6, VPS4A, VPS35, EHD2, IQSEC1, GIT1, PARD6A, DNM3, FAM21A, EPS15, RABEP1, ARRB1, IGF2R, ACAP2, SH3KBP1, SNX12, BIN1, DNM1
KEGG_PATHWAY hsa04150:mTOR signaling pathway 12 1.28 3.93 1.75E-04 7.67E-03 PRKCA, EIF4B, AKT1, MAPK1, RPS6KA3, AKT1S1, BRAF, TSC2, MAPK3, PRKAA1, RRAGD, RPTOR
KEGG_PATHWAY hsa04270:Vascular smooth muscle contraction 16 1.71 2.60 1.11E-03 3.98E-02 PRKCA, BRAF, CALD1, MRVI1, PRKCE, ARHGEF12, PRKG1, PRKCD, ITPR1, ITPR2, ARHGEF11, MAPK1, MAPK3, PPP1R12A, GNAS, PRKACB
KEGG_PATHWAY hsa04130:SNARE interactions in vesicular transport 8 0.85 4.47 1.62E-03 4.98E-02 STX1A, STX4, SEC22B, VAMP4, BET1L, SNAP23, VAMP2, STX1B
KEGG_PATHWAY hsa05205:Proteoglycans in cancer 22 2.35 2.09 1.77E-03 4.75E-02 PRKCA, BRAF, ARHGEF12, PDCD4, FLNA, PXN, ITPR1, ITPR2, AKT1, EIF4B, MAPK1, CTTN, ANK1, ANK2, ANK3, SOS1, MAPK3, PPP1R12A, CAMK2D, PRKACB, SLC9A1, FN1
KEGG_PATHWAY hsa04910:Insulin signaling pathway 17 1.81 2.34 2.24E-03 5.34E-02 PHKA2, IRS2, BRAF, PHKB, ACACA, RPTOR, AKT1, PRKAR2B, MAPK1, PRKAR2A, SLC2A4, SOS1, PRKAR1A, MAPK3, TSC2, PRKAA1, PRKACB
KEGG_PATHWAY hsa04666:Fc gamma R-mediated phagocytosis 12 1.28 2.71 4.21E-03 8.86E-02 PRKCA, AKT1, MAPK1, LYN, MARCKSL1, MAPK3, ASAP2, ASAP1, MARCKS, PRKCE, BIN1, PRKCD
KEGG_PATHWAY hsa04114:Oocyte meiosis 14 1.49 2.40 5.18E-03 9.86E-02 ANAPC1, YWHAZ, CDC23, YWHAE, SMC3, ITPR1, ITPR2, MAPK1, RPS6KA3, SLK, MAPK3, CAMK2D, PPP3CB, PRKACB
KEGG_PATHWAY hsa04922:Glucagon signaling pathway 13 1.39 2.50 5.39E-03 9.44E-02 PHKA2, PHKB, PGAM1, ACACA, ITPR1, ITPR2, AKT1, SLC2A1, PPP3CB, CAMK2D, GNAS, PRKAA1, PRKACB
KEGG_PATHWAY hsa04720:Long-term potentiation 10 1.07 2.88 7.12E-03 1.14E-01 PRKCA, MAPK1, RPS6KA3, BRAF, MAPK3, PPP3CB, CAMK2D, PRKACB, ITPR1, ITPR2
KEGG_PATHWAY hsa04962:Vasopressin-regulated water reabsorption 8 0.85 3.46 7.37E-03 1.10E-01 DYNC1LI1, STX4, DYNC1LI2, AVP, GNAS, PRKACB, VAMP2, DYNC1I2
KEGG_PATHWAY hsa04022:cGMP–PKG signaling pathway 17 1.81 2.04 8.48E-03 1.17E-01 MEF2C, IRS2, SLC8A2, MRVI1, PRKCE, VDAC2, PRKG1, ITPR1, ITPR2, VDAC1, ATP2B1, AKT1, MAPK1, ATP2B4, MAPK3, PPP1R12A, PPP3CB
KEGG_PATHWAY hsa04510:Focal adhesion 20 2.13 1.84 1.15E-02 1.47E-01 PRKCA, TLN1, BRAF, TLN2, TNC, ARHGAP35, MYL12B, PXN, FLNA, VCL, AKT1, MAPK1, ARHGAP5, PAK2, SOS1, MAPK3, PPP1R12A, SPP1, FN1, ITGA2B
KEGG_PATHWAY hsa04730:Long-term depression 9 0.96 2.85 1.25E-02 1.50E-01 PRKCA, MAPK1, BRAF, LYN, MAPK3, GNAS, PRKG1, ITPR1, ITPR2
KEGG_PATHWAY hsa04970:Salivary secretion 11 1.17 2.43 1.41E-02 1.59E-01 PRKCA, ATP2B1, ATP2B4, SLC12A2, GNAS, PRKACB, VAMP2, PRKG1, ITPR1, SLC9A1, ITPR2
KEGG_PATHWAY hsa04915:Estrogen signaling pathway 12 1.28 2.30 1.42E-02 1.53E-01 HSP90AB1, AKT1, MAPK1, HSP90AA1, FKBP5, SOS1, MAPK3, GNAS, PRKACB, PRKCD, ITPR1, ITPR2
KEGG_PATHWAY hsa04530:Tight junction 11 1.17 2.40 1.52E-02 1.55E-01 PARD6A, TJP1, CTTN, MYH11, MYL12B, MYH14, PRKCE, MYH9, TJP2, TJAP1, MYH10
KEGG_PATHWAY hsa04914:Progesterone-mediated oocyte maturation 11 1.17 2.40 1.52E-02 1.55E-01 HSP90AB1, ANAPC1, AKT1, MAPK1, CCNB3, RPS6KA3, HSP90AA1, BRAF, MAPK3, CDC23, PRKACB
KEGG_PATHWAY hsa04141:Protein processing in endoplasmic reticulum 17 1.81 1.91 1.56E-02 1.52E-01 HSP90AB1, SEC31A, HSP90AA1, WFS1, RRBP1, NSFL1C, PDIA6, SEC62, CANX, STUB1, STT3B, HYOU1, DNAJB11, SIL1, DNAJC1, UBE2E2, SSR3
KEGG_PATHWAY hsa04540:Gap junction 11 1.17 2.38 1.64E-02 1.52E-01 PRKCA, MAPK1, TJP1, SOS1, MAPK3, GNAS, PRKACB, PRKG1, TUBA1B, ITPR1, ITPR2
KEGG_PATHWAY hsa04919:Thyroid hormone signaling pathway 13 1.39 2.15 1.70E-02 1.52E-01 PRKCA, AKT1, MAPK1, NCOA2, HDAC2, HDAC1, TSC2, MAPK3, SLC2A1, RCAN1, PRKACB, NCOR1, SLC9A1
KEGG_PATHWAY hsa05100:Bacterial invasion of epithelial cells 10 1.07 2.44 2.03E-02 1.71E-01 DNM3, CTTN, SEPT2, ARHGEF26, CTNNA1, DNM1, PXN, CTNNA2, FN1, VCL
KEGG_PATHWAY hsa04912:GnRH signaling pathway 11 1.17 2.30 2.03E-02 1.65E-01 PRKCA, MAPK1, MAP3K3, SOS1, MAPK3, CAMK2D, GNAS, PRKACB, PRKCD, ITPR1, ITPR2
KEGG_PATHWAY hsa05132:Salmonella infection 10 1.07 2.29 2.91E-02 2.21E-01 MAPK1, DYNC1LI1, TJP1, DYNC1LI2, MAPK3, PKN2, KLC4, KLC2, FLNA, DYNC1I2
KEGG_PATHWAY hsa04010:MAPK signaling pathway 21 2.24 1.58 4.26E-02 2.99E-01 PRKCA, MEF2C, BRAF, TAOK2, TAOK1, GNG12, FLNA, AKT1, MAPK1, RPS6KA3, PAK2, MAP3K3, ARRB1, SOS1, MAPT, MAPK3, PPP3CB, HSPB1, MAPK8IP3, STMN1, PRKACB
KEGG_PATHWAY hsa04068:FoxO signaling pathway 13 1.39 1.84 4.82E-02 3.22E-01 USP7, IRS2, SGK3, BRAF, RBL2, AKT1, MAPK1, CCNB3, SLC2A4, HOMER3, SOS1, MAPK3, PRKAA1
KEGG_PATHWAY hsa03015:mRNA surveillance pathway 10 1.07 2.09 4.84E-02 3.14E-01 PABPC4L, NCBP1, FIP1L1, CSTF3, RBM8A, SRRM1, RNPS1, ACIN1, CPSF2, PNN
KEGG_PATHWAY hsa04810:Regulation of actin cytoskeleton 18 1.92 1.63 4.90E-02 3.08E-01 GIT1, BRAF, ARHGEF6, ARHGAP35, MYL12B, GNG12, ARHGEF12, PXN, VCL, MAPK1, PAK2, SOS1, MAPK3, PIKFYVE, PPP1R12A, SLC9A1, FN1, ITGA2B
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Further, functional clustering analysis based on CCs, MFs, BPs, and KEGG pathways grouped those identified phosphoproteins into 8 functional clusters (Table 3). Cluster 1 mainly functions in cell–cell adherens junction, cadherin binding involved in cell–cell adhesion, and cell–cell adhesion. Cluster 2 mainly functions in SNARE interactions in vesicular transport, SNAP (soluble N-ethylmaleimide-sensitive fusion attachment proteins) receptor activity, and SNARE complex. Cluster 3 mainly functions in histone H2B ubiquitination, histone monoubiquitination, endodermal cell fate commitment, and Cdc73/Paf1 complex. Cluster 4 mainly functions in myosin II complex and myosin II filament. Cluster 5 mainly functions in oxygen transporter activity, oxygen transport, haptoglobin binding, haptoglobin–hemoglobin complex, and hemoglobin complex. Cluster 6 functions in cAMP-dependent protein kinase complex, protein kinase A catalytic subunit binding, and cAMP-dependent protein kinase regulator activity. Cluster 7 functions in ESCRT (endosomal sorting complex required for transport) III complex disassembly and positive regulation of viral release from host cell. Cluster 8 functions in eukaryotic 43S preinitiation complex. Those findings clearly demonstrate that protein phosphorylations are extensively involved in multiple biological processes and molecular functions in NFPAs.

Table 3.

Functional characteristics of 1035 phosphoproteins in NFPAs, clustered with GO and KEGG pathway enrichments. Count means the number of genes enriched in each item

Category Function characteristics Count % Fold enrichment P value Gene name of phosphoprotein
Annotation cluster 1
  GOTERM_CC_DIRECT GO:0005913~cell–cell adherens junction 71 7.57 4.44 2.23E-26 CAST, HSP90AB1, LIMA1, TLN1, SEPT2, ZC3HAV1, VAPB, H1FX, EIF2A, EPS15L1, VCL, LARP1, BZW1, CTTN, PAK2, SLK, PCBP1, ARHGAP1, FAM129B, AHNAK, GOLGA3, DAB2IP, BSG, LYN, PKN2, KTN1, MYH9, CTNNA1, FLNA, CTNNA2, RSL1D1, EIF4G1, EIF4G2, EPB41L1, PGM5, DHX29, SERBP1, USO1, EEF1D, CD226, DBN1, ADD1, ALDOA, HDLBP, YWHAZ, USP8, PPME1, CALD1, ASAP1, CTNND1, KLC2, ESYT2, TAGLN2, SCRIB, CHMP2B, MACF1, NDRG1, PLEC, ZC3H15, YWHAE, MPRIP, TNKS1BP1, EPS15, TJP1, LASP1, TMOD3, SPTBN1, NOP56, TMPO, TJP2, SPTAN1
  GOTERM_MF_DIRECT GO:0098641~cadherin binding involved in cell–cell adhesion 68 7.25 4.49 9.96E-26 CAST, HSP90AB1, LIMA1, TLN1, SEPT2, ZC3HAV1, VAPB, H1FX, EIF2A, EPS15L1, VCL, LARP1, BZW1, CTTN, PAK2, SLK, PCBP1, ARHGAP1, FAM129B, AHNAK, GOLGA3, DAB2IP, BSG, PKN2, KTN1, MYH9, CTNNA1, FLNA, CTNNA2, RSL1D1, EIF4G1, EIF4G2, EPB41L1, DHX29, SERBP1, USO1, EEF1D, DBN1, ADD1, ALDOA, YWHAZ, HDLBP, USP8, PPME1, CALD1, ASAP1, CTNND1, KLC2, ESYT2, TAGLN2, SCRIB, CHMP2B, MACF1, NDRG1, PLEC, ZC3H15, YWHAE, MPRIP, EPS15, TNKS1BP1, TJP1, LASP1, TMOD3, SPTBN1, NOP56, TMPO, TJP2, SPTAN1
  GOTERM_BP_DIRECT GO:0098609~cell–cell adhesion 60 6.40 4.33 6.80E-22 CAST, HSP90AB1, LIMA1, SEPT2, ZC3HAV1, VAPB, H1FX, EPS15L1, EIF2A, LARP1, BZW1, CTTN, SLK, PAK2, PCBP1, ARHGAP1, FAM129B, AHNAK, GOLGA3, DAB2IP, BSG, PKN2, KTN1, RSL1D1, EIF4G1, EIF4G2, EPB41L1, DHX29, SERBP1, USO1, EEF1D, DBN1, ADD1, ALDOA, YWHAZ, HDLBP, USP8, PPME1, CALD1, ASAP1, KLC2, TAGLN2, ESYT2, CHMP2B, MACF1, NDRG1, PLEC, ZC3H15, YWHAE, MPRIP, EPS15, TNKS1BP1, TJP1, LASP1, TMOD3, SPTBN1, TMPO, NOP56, TJP2, SPTAN1
Annotation cluster 2
  KEGG_PATHWAY hsa04130:SNARE interactions in vesicular transport 8 0.85 4.47 1.62E-03 STX1A, STX4, SEC22B, VAMP4, BET1L, SNAP23, VAMP2, STX1B
  GOTERM_MF_DIRECT GO:0005484~SNAP receptor activity 8 0.85 3.93 3.66E-03 STX1A, STX4, SEC22B, VAMP4, BET1L, SNAP23, VAMP2, STX1B
  GOTERM_CC_DIRECT GO:0031201~SNARE complex 9 0.96 3.43 4.28E-03 STX1A, STX4, SEC22B, VAMP4, BET1L, SNAP23, VAMP2, STX1B, STXBP5L
Annotation cluster 3
  GOTERM_BP_DIRECT GO:0033523~histone H2B ubiquitination 4 0.43 9.79 6.11E-03 LEO1, PAF1, RNF20, CTR9
  GOTERM_BP_DIRECT GO:0010390~histone monoubiquitination 4 0.43 7.12 1.61E-02 LEO1, PAF1, RNF20, CTR9
  GOTERM_BP_DIRECT GO:0001711~endodermal cell fate commitment 3 0.32 9.79 3.40E-02 LEO1, PAF1, CTR9
  GOTERM_CC_DIRECT GO:0016593~Cdc73/Paf1 complex 3 0.32 8.65 4.35E-02 LEO1, PAF1, CTR9
Annotation cluster 4
  GOTERM_CC_DIRECT GO:0016460~myosin II complex 4 0.43 11.53 3.64E-03 MYL12B, MYH14, MYH9, MYH10
  GOTERM_CC_DIRECT GO:0097513~myosin II filament 3 0.32 20.18 7.10E-03 MYH14, MYH9, MYH10
Annotation cluster 5
  GOTERM_MF_DIRECT GO:0005344~oxygen transporter activity 5 0.53 6.84 4.84E-03 IPCEF1, HBA2, HBA1, HBB, HBD
  GOTERM_BP_DIRECT GO:0015671~oxygen transport 5 0.53 6.52 5.85E-03 IPCEF1, HBA2, HBA1, HBB, HBD
  GOTERM_MF_DIRECT GO:0031720~haptoglobin binding 3 0.32 19.14 7.88E-03 HBA2, HBA1, HBB
  GOTERM_CC_DIRECT GO:0031838~haptoglobin–hemoglobin complex 3 0.32 15.14 1.37E-02 HBA2, HBA1, HBB
  GOTERM_CC_DIRECT GO:0005833~hemoglobin complex 4 0.43 6.73 1.90E-02 HBA2, HBA1, HBB, HBD
Annotation cluster 6
  GOTERM_CC_DIRECT GO:0005952~cAMP-dependent protein kinase complex 4 0.43 11.53 3.64E-03 PRKAR2B, PRKAR2A, PRKAR1A, PRKACB
  GOTERM_MF_DIRECT GO:0034236~protein kinase A catalytic subunit binding 4 0.43 5.47 3.35E-02 PRKAR2B, PRKAR2A, GSK3A, PRKAR1A
  GOTERM_MF_DIRECT GO:0008603~cAMP-dependent protein kinase regulator activity 3 0.32 8.20 4.80E-02 PRKAR2B, PRKAR2A, PRKAR1A
Annotation cluster 7
  GOTERM_BP_DIRECT GO:1904903~ESCRT III complex disassembly 4 0.43 7.83 1.21E-02 CHMP2A, VPS4B, VPS4A, CHMP2B
  GOTERM_BP_DIRECT GO:1902188~positive regulation of viral release from host cell 4 0.43 7.12 1.61E-02 CHMP2A, VPS4B, VPS4A, CHMP2B
Annotation cluster 8
  GOTERM_CC_DIRECT GO:0016282~eukaryotic 43S preinitiation complex 4 0.43 5.38 3.52E-02 EIF3CL, EIF3B, EIF3G, DHX29
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Integration of phosphoproteomics data and transcriptomics data in invasive NFPAs relative to controls

Microarray transcriptomic data between invasive NFPAs and controls were obtained from the GEO database, which identified 2751 DEGs, consisting of 1477 (53.69%) downregulated and 1274 (46.31%) upregulated DEGs (Supplemental Table 5). An overlapping analysis was performed between 1035 phosphoproteins identified in NFPAs relative to controls and 2751 DEGs identified in invasive NFPAs relative to controls, which identified 130 overlapped molecules (invasive DEGs; phosphoproteins) (Table 4). Those 130 invasiveness-related molecules (DEGs, phosphoproteins) were the precious resource to identify phosphorylation-mediated invasive characteristics in NFPAs for potential prognostic/predictive assessment, patient stratification, and personalized treatment of NFPA patients. Further, the functional characteristics of those 130 overlapped molecules (phosphoproteins; invasive DEGs) were revealed with GO and clustering enrichment analyses.

Table 4.

A total of 130 overlapped molecules between 1035 phosphoproteins and 2751 invasive DEGs

DEG name logFC AveExpr t P value Adjusted P value B Protein accession ID Protein name Phosphopeptide Phosphorylated amino acid residue Phosphorylated position Phosphorylated probabilities PEP Score Phosphorylated level (N) Phosphorylated level (T) Ratio (T/N) P value (t test)
ABCA2 1.9524 9.4927 8.1782 1.28E-05 1.26E-03 3.7214 Q9BZC7 ATP-binding cassette subfamily A member 2 VSEEDQS*LENS*EADVK S; S 1327; 1331 1; 1 3.62E-06 92 10,518 32,589 3.10 1.60E-04
ACACA 1.1822 6.8754 3.3664 7.62E-03 4.22E-02 − 2.8421 Q13085 Acetyl-CoA carboxylase 1 FIIGSVSEDNS*EDEISNLVK S 29 1 3.35E-32 154 2551 7604 2.98 6.08E-05
FIIGSVS*EDNS*EDEISNLVK S; S 25; 29 0.82; 0.892 7.89E-07 54
ADD2 1.6533 6.9033 5.2921 4.08E-04 7.59E-03 0.1686 P35612 Beta-adducin EAETKS*PLVS*PSK S 613; 617 0.885; 0.981 1.79E-04 88 11,989 36,075 3.01 1.28E-05
EAETKS*PLVS*PSK S 613; 617 0.877; 1 1.79E-04 88 11,989 36,075 3.01 1.28E-05
KLELDGEKETAPEEPGS*PAK S 592 1 8.36E-06 84 14,454 20,046 1.39 9.74E-04
S*APAS*PVQSPAK S; S 596; 600 1; 1 8.36E-06 70 1895 3649 1.93 1.08E-03
EAET*KSPLVS*PSK T 611 0.998 1.24E-02 52
SAPAS*PVQS*PAK S; S 599; 604 0.998; 1 4.07E-03 94
ADD3 1.0662 11.6287 4.4111 1.46E-03 1.57E-02 − 1.1500 Q9UEY8 Gamma-adducin IEEVLSPEGS*PSKS*PS*K S; S; S 677; 681; 683 0.993; 0.498; 0.498 4.80E-10 105 17,730 41,658 2.35 1.10E-04
IEEVLSPEGS*PS*KSPSK S; S 677; 679 0.906; 0.548 6.72E-04 69 10,380 17,649 1.70 1.34E-04
IEEVLS*PEGS*PSK S; S 673; 677 1; 0.986 3.87E-04 72
IEEVLSPEGS*PSKS*PS*K S; S; S 677; 681; 683 0.993; 0.498; 0.498 4.80E-10 105
IEEVLSPEGS*PSKS*PSK S 677; 681 0.522; 0.761 4.80E-10 105
AKAP12 − 4.1353 10.1654 − 4.9159 6.93E-04 1.03E-02 − 0.3808 Q02952 A-kinase anchor protein 12 GLAEVQQDGEAEEGAT*SDGEK T 597 0.932 7.78E-31 146 4946 11,387 2.30 9.59E-05
SPPS*PVER S 1331 0.999 4.18E-03 91 6418 4875 0.76 2.79E-04
ETCVSGEDPTQGADLS*PDEK S 483 1 1.91E-22 135 25,062 14,371 0.57 4.98E-04
GLAEVQQDGEAEEGATS*DGEK S 598 0.847 7.54E-39 156 15,714 22,991 1.46 1.77E-03
ADS*QDAGQETEK S 1587 1 1.69E-145 133 4482 8736 1.95 2.36E-03
RGS*S*S*DEEGGPK S; S; S 696; 697; 698 1; 1; 1 1.42E-03 87 52,712 37,364 0.71 3.64E-03
EDEKGDDVDDPENQNSALADT*DASGGLTKESPDTNGPK T; S 1717; 1720 0.221; 0.221 7.03E-11 68
EVSSLEGS*PPPCLGQEEAVCTK S 1395 0.927 1.21E-12 58
SAESPT*S*PVTSETGSTFK T; S 285; 286 0.749; 0.749 3.61E-04 50
VVGQTT*PESFEK T 1116 0.956 1.48E-02 84
AKT1 1.1801 6.8509 5.4900 3.11E-04 6.55E-03 0.4493 P31749 RAC-alpha serine/threonine-protein kinase S*GSPSDNSGAEEMEVSLAKPK S; S 122; 124 0.457; 0.457 1.16E-07 92
ALB 1.9536 6.1289 4.5055 1.27E-03 1.45E-02 − 1.0035 P02768-1 Serum albumin TCVADES*AENCDK S 82 1 1.24E-47 197 114,813 84,746 0.74 3.24E-04
ALDOA 1.2343 14.3796 5.4707 3.19E-04 6.64E-03 0.4222 P04075 Fructose-bisphosphate aldolase A GILAADES*TGSIAK S 36 0.97 2.14E-30 166
GILAADEST*GSIAK T 37 0.555 7.28E-05 95
GILAADESTGS*IAK S 39 0.983 1.65E-30 168
LQS*IGTENTEENR S 46 0.994 1.17E-03 66
ANAPC1 1.1050 8.8290 4.9940 6.20E-04 9.67E-03 − 0.2650 Q9H1A4 Anaphase-promoting complex subunit 1 NFDFEGSLS*PVIAPK S 688 0.839 1.86E-02 40
ANK1 2.2500 8.5296 3.7930 3.82E-03 2.75E-02 − 2.1377 P16157 Ankyrin-1 LGYIS*VTDVLK S 781 1 4.25E-15 146 47,985 171,943 3.58 4.13E-06
ELQFS*VEDINR S 1428 1 1.62E-03 107 7814 13,457 1.72 1.74E-05
LST*PPPLAEEEGLASR T 961 0.979 1.29E-08 106 18,331 29,532 1.61 4.31E-04
LS*T*PPPLAEEEGLASR S; T 960; 961 0.5; 0.5 1.29E-08 105 10,530 17,926 1.70 5.29E-04
LEGALS*EEPR S 1607 1 1.17E-03 105 10,600 8027 0.76 3.19E-03
IT*HSPTVSQVTER T 1684 0.745 2.33E-02 51
ITHS*PTVSQVTER S 1686 0.962 8.25E-04 76
ITHS*PT*VSQVTER S; T 1686; 1688 0.327; 0.327 2.14E-02 47
AP3B1 − 1.2102 11.2042 − 4.7116 9.33E-04 1.22E-02 − 0.6877 O00203 AP-3 complex subunit beta-1 NFYES*DDDQKEK S 276 1 1.03E-45 117 22,634 68,527 3.03 2.37E-05
APIP 1.1934 8.6558 4.5636 1.16E-03 1.38E-02 − 0.9140 Q96GX9 Probable methylthioribulose-1-phosphate dehydratase DISGPS*PSK S 87 0.962 4.65E-03 81
APOL1 − 1.1936 9.8189 − 3.2480 9.26E-03 4.79E-02 − 3.0397 A5PL32 Apolipoprotein L1 VTEPIS*AES*GEQVER S; S 352; 355 1; 1 2.53E-03 59 6305 4500 0.71 5.33E-03
ARHGEF11 1.5982 6.3788 7.6133 2.33E-05 1.74E-03 3.1111 O15085 Rho guanine nucleotide exchange factor 11 S*LENPT*PPFTPK S; T 663; 668 1; 1 1.93E-10 106 12,686 29,402 2.32 4.24E-04
NS*GIWESPELDR S 1295 1 6.04E-03 52 7700 11,195 1.45 1.11E-03
HQVLLEDPEQEGS*AEEEELGVLPCPSTSLDGENR S 1155 1 3.31E-47 146 1696 3062 1.81 1.02E-02
WTDGS*LS*PPAKEPLASDSR S; S 1478; 1480 0.48; 0.48 2.20E-03 38
ARHGEF12 1.0544 7.6127 5.9598 1.67E-04 4.70E-03 1.0924 Q9NZN5 Rho guanine nucleotide exchange factor 12 TDCSSGDASRPSSDNADS*PK S 309 1 1.82E-10 109 8015 23,551 2.94 9.07E-04
ARHGEF6 1.5519 6.6394 4.1475 2.19E-03 1.99E-02 − 1.5656 Q15052 Rho guanine nucleotide exchange factor 6 KDS*IPQVLLPEEEK S 684 1 1.39E-10 126
ASAP1 1.4735 6.5041 5.7914 2.08E-04 5.30E-03 0.8655 Q9ULH1 Arf-GAP with SH3 domain, ANK repeat and PH domain-containing protein 1 QEEIDES*DDDLDDKPSPIK S 717 1 1.20E-107 116 4217 8616 2.04 1.35E-04
ASAP2 − 2.6231 8.6901 − 4.5167 1.25E-03 1.43E-02 − 0.9862 O43150 Arf-GAP with SH3 domain, ANK repeat and PH domain-containing protein 2 LLHEDLDES*DDDMDEK S 701 1 2.71E-52 185 11,289 17,685 1.57 2.02E-03
ATP8A1 1.5729 8.3920 5.5795 2.75E-04 6.14E-03 0.5743 Q9Y2Q0 Probable phospholipid-transporting ATPase IA TDDVS*EKT*S*LADQEEVR S; T; S 25; 28; 29 0.966; 0.502; 0.502 5.82E-03 57 5114 8707 1.70 3.09E-05
TDDVS*EKT*SLADQEEVR S; T 25; 28 0.764; 0.563 2.73E-03 62 5114 8707 1.70 3.09E-05
TS*LADQEEVR S 29 0.767 2.73E-03 63 5114 8707 1.70 3.09E-05
AVP − 1.0570 7.0007 − 3.3755 7.51E-03 4.18E-02 − 2.8269 P01185 Vasopressin-neurophysin 2-copeptin CQEENYLPS*PCQSGQK S 83 0.996 1.57E-03 69 6536 4234 0.65 8.03E-03
BAIAP3 − 7.0454 10.8238 − 22.2592 1.46E-09 5.00E-06 11.8821 O94812 BAI1-associated protein 3 S*GSPAPPEPVDPSLGLR S 113 0.528 4.57E-03 58 10,732 21,332 1.99 6.38E-04
SGS*PAPPEPVDPSLGLR S 115 0.981 1.52E-16 133 1866 3885 2.08 3.13E-03
BASP1 − 2.4499 11.6391 − 3.2822 8.75E-03 4.61E-02 − 2.9825 P80723 Brain acid–soluble protein 1 S*DGAPASDSKPGSSEAAPSSK S 164 0.996 8.79E-03 42 7903 10,069 1.27 1.46E-02
AEGAATEEEGT*PK T 36 1 4.87E-24 90
ETPAATEAPS*S*T*PK S; S; T 194; 195; 196 0.333; 0.333; 0.333 1.91E-03 61
ETPAATEAPSST*PK T 196 0.683 3.52E-06 63
BCLAF1 1.2108 6.8562 6.4198 9.33E-05 3.40E-03 1.6913 Q9NYF8 Bcl-2-associated transcription factor 1 KAEGEPQEES*PLK S 177 1 8.49E-31 158 84,318 208,327 2.47 2.02E-07
AEGEWEDQEALDYFS*DKESGK S 385 1 5.55E-93 218 31,132 135,860 4.36 4.16E-07
DLFDYS*PPLHK S 512 1 8.04E-16 107 9441 20,570 2.18 3.11E-06
ETQS*PEQVK S 496 1 1.35E-19 173 70,549 155,420 2.20 2.09E-05
FNDS*EGDDT*EETEDYR S 397; 402 1; 1 3.19E-129 256 44,945 69,960 1.56 2.82E-05
KET*QSPEQVK T 494 0.567 2.69E-02 43 4370 8650 1.98 1.10E-04
QKS*PEIHR S 648 1 9.96E-03 65 797 2439 3.06 2.65E-04
YS*PSQNSPIHHIPSR S 285 0.907 2.15E-31 167 23,981 39,296 1.64 3.62E-04
YSPS*QNS*PIHHIPSR S; S 287; 290 0.908; 0.999 2.15E-31 167 20,077 36,892 1.84 4.90E-04
IDIS*PSTLR S 658 0.995 2.86E-13 162 36,901 49,609 1.34 8.48E-04
SSATSGDIWPGLSAYDNS*PR S 222 0.998 9.35E-121 119 16,902 13,214 0.78 7.70E-03
DTFEHDPSES*IDEFNK S 198 0.98 2.82E-35 94
SSATSGDIWPGLS*AYDNSPR S 217 0.528 3.48E-29 76
CALD1 1.1936 4.5492 3.8770 3.34E-03 2.54E-02 − 2.0009 Q05682 Caldesmon RGS*IGENQIKDEK S 207 1 1.37E-06 119 98,098 323,267 3.30 2.31E-06
CASC4 − 1.1339 10.5848 − 4.4287 1.42E-03 1.54E-02 − 1.1227 Q6P4E1 Protein CASC4 FFDENES*PVDPQHGSK S 366 1 1.16E-03 73
CASK 2.8830 11.3140 7.5385 2.53E-05 1.82E-03 3.0275 B7ZKY2 Calcium/calmodulin-dependent serine protein kinase (MAGUK family) TQSS*S*CEDLPSTTQPK S; S 570; 571 0.78; 0.739 5.24E-05 88 3606 7644 2.12 3.28E-04
TQS*SSCEDLPSTTQPK S 569 0.379 8.01E-16 79
CBX8 1.0985 6.6357 3.9595 2.93E-03 2.35E-02 − 1.8672 Q9HC52 Chromobox protein homolog 8 VDDKPS*SPGDSSK S 190 0.513 2.77E-04 83
CCDC48 − 3.3166 8.0575 − 9.1213 5.02E-06 7.68E-04 4.6616 Q9HA90 Coiled-coil domain-containing protein 48 TLGTS*EEEAELQQK S 480 0.841 1.19E-21 150 3504 2952 0.84 1.04E-01
T*LGTSEEEAELQQK T 476 0.504 2.94E-11 136
CCDC86 − 2.5059 12.1490 − 4.2785 1.79E-03 1.76E-02 − 1.3580 Q9H6F5 Coiled-coil domain-containing protein 86 LQQGAGLESPQGQPEPGAAS*PQR S 91 1 8.60E-16 91 11,730 18,273 1.56 5.06E-03
LGGLRPES*PESLTSVSR S 18 0.998 1.72E-03 61 9461 12,076 1.28 1.46E-02
ALVEFESNPEETREPGS*PPSVQR S 47 0.988 8.54E-17 121
CDC42EP4 2.2410 9.6096 3.4168 7.02E-03 4.01E-02 − 2.7581 Q9H3Q1 Cdc42 effector protein 4 AGPDLPSLPSHALEDEGWAAAAPS*PGSAR S 292 0.789 6.03E-10 41
CDK14 1.1778 11.3632 3.5667 5.50E-03 3.45E-02 − 2.5096 O94921 Cyclin-dependent kinase 14 VHS*ENNACINFK S 95 1 1.18E-02 75 2416 3471 1.44 5.63E-03
CENPA 1.5999 4.3832 4.5900 1.12E-03 1.35E-02 − 0.8733 P49450 Histone H3-like centromeric protein A RRS*PS*PTPTPGPSR S; S 17; 19 0.998; 0.984 7.74E-04 68 2107 18,118 8.60 3.01E-06
CHD3 1.1576 8.0852 4.7990 8.21E-04 1.13E-02 − 0.5558 Q12873 Chromodomain-helicase-DNA-binding protein 3 ELQGDGPPS*SPTNDPTVK S; S 712; 713 0.481; 0.481 1.47E-02 50
CHGA − 3.8263 14.4379 − 4.6354 1.04E-03 1.30E-02 − 0.8038 P10645 Chromogranin-A S*GELEQEEERLS*KEWEDSK S; S 322; 333 1; 1 1.06E-26 183 296,230 566,530 1.91 4.87E-05
S*GELEQEEERLS*K S; S 322; 333 1; 1 1.06E-26 147 207,873 320,807 1.54 2.39E-04
HSGFEDELSEVLENQS*S*QAELK S; S 112; 113 1; 1 0.00E+00 287 320,767 437,363 1.36 8.38E-04
YPGPQAEGDSEGLS*QGLVDR S 207 1 3.51E-32 154 7755 9573 1.23 1.97E-03
GEQEHS*QQKEEEEEMAVVPQGLFR S 300 1 1.45E-131 233 280,203 169,087 0.60 2.61E-03
S*GEATDGARPQALPEPMQESK S 142 1 2.90E-55 180 437,597 568,253 1.30 3.19E-03
EAVEEPS*SKDVMEK S 125 0.567 2.10E-05 93 18,890 23,102 1.22 7.15E-03
EAVEEPSS*KDVMEK S 126 0.873 1.02E-07 120 20,108 27,926 1.39 1.99E-02
REDS*KEAEK S 136 1 7.15E-06 108 126,907 149,643 1.18 3.08E-02
YPGPQAEGDS*EGLSQGLVDR S 203 1 3.97E-102 225 63,186 74,743 1.18 4.94E-02
GWRPS*S*REDS*LEAGLPLQVR S; S; S 397; 398; 402 0.543; 0.543; 0.914 8.64E-27 145 38,199 34,450 0.90 6.23E-02
LEGQEEEEDNRDS*SMK S 370 0.989 3.96E-61 200 119,240 151,717 1.27 6.51E-02
EDS*LEAGLPLQVR S 402 1 1.44E-38 185 123,567 116,303 0.94 7.89E-02
GLS*AEPGWQAK S 218 1 2.58E-25 180 37,874 34,815 0.92 1.12E-01
HSGFEDELS*EVLENQS*SQAELK S; S 105; 112 0.8261 3.88E-90 99 2083 2441 1.17 1.58E-01
RPEDQELESLS*AIEAELEK S 438 0.98 3.04E-120 119 11,305 10,366 0.92 2.55E-01
GWRPS*SREDS*LEAGLPLQVR S; S 396; 402 0.731; 0.99 8.64E-27 145 67,782 66,289 0.98 4.30E-01
KHS*GFEDELSEVLENQS*SQAELK S 98; 112 0.686; 0.843 3.26E-34 67
RPEDQELES*LSAIEAELEK S 436 0.997 1.77E-167 127
CHGB − 1.3288 14.5253 − 3.2997 8.51E-03 4.53E-02 − 2.9533 P05060 Secretogranin-1 SETHAAGHS*QEK S 225 1 4.87E-30 175 194,330 26,293 0.14 6.87E-07
GRGS*EEYR S 367 1 1.70E-07 146 85,298 10,118 0.12 7.46E-07
AS*EEEPEYGEEIK S 335 1 5.81E-71 223 553,550 73,696 0.13 2.05E-06
EKSS*QES*GEETGS*QENHPQESK S; S; S 236; 239; 245 0.531; 1; 0.965 4.20E-73 205 248,397 14,007 0.06 3.67E-06
ADEPQWSLYPSDS*QVS*EEVK S; S 146; 149 0.999; 0.999 1.30E-150 236 321,833 101,210 0.31 5.28E-06
SSQESGEET*GSQENHPQESK T 243 0.978 1.47E-45 161 143,047 2755 0.02 5.36E-06
GERGEDS*S*EEK S; S 182; 183 1; 1 1.80E-03 100 80,795 13,041 0.16 7.03E-06
AS*EEEPEY*GEEIK S; Y 335; 341 1; 1 2.35E-06 98 220,190 39,598 0.18 8.29E-06
SSQES*GEETGS*QENHPQESK S; S 239; 245 0.992; 1 4.20E-73 205 407,313 23,721 0.06 1.01E-05
S*QREDEEEEEGENYQK S 160 1 1.77E-17 140 50,520 12,595 0.25 1.17E-05
SAEFPDFYDS*EEPVSTHQEAENEKDR S 626 1 1.85E-99 214 339,777 89,045 0.26 1.18E-05
S*QEES*EEGEEDATS*EVDK S 259; 263; 272 1; 1; 0.857 1.51E-53 157 260,217 35,935 0.14 1.23E-05
SSQGGS*LPSEEK S 298 0.981 3.28E-14 142 88,621 25,139 0.28 1.31E-05
DPADASEAHES*SSR S 98 0.966 1.32E-30 169 16,941 1615 0.10 1.41E-05
NYPS*LELDK S 391 1 1.30E-04 112 209,063 29,849 0.14 1.44E-05
ADEPQWSLYPS*DS*QVSEEVK S; S 144; 146 0.941; 0.751 3.58E-98 136 19,345 10,016 0.52 1.53E-05
EKSS*QES*GEET*GSQENHPQESK S; S; T 237; 239; 243 0.819; 0.935; 0.598 1.47E-45 161 227,790 11,221 0.05 1.53E-05
SSQGGSLPS*EEK S 301 0.999 5.51E-20 128 85,508 21,593 0.25 1.88E-05
S*AEFPDFYDSEEPVSTHQEAENEKDR S 617 1 8.30E-152 246 312,090 68,957 0.22 2.13E-05
S*AEFPDFY*DSEEPVSTHQEAENEKDR Y 624 0.995 8.86E-43 152 68,357 15,724 0.23 2.23E-05
SQEESEEGEEDAT*SEVDK T 271 0.973 1.38E-88 219 322,700 69,163 0.21 2.23E-05
M#AHGY*GEES*EEER Y; S 401; 405 1; 1 2.41E-02 51 7666 1759 0.23 2.34E-05
ERADEPQWSLYPSDSQVS*EEVK S 149 1 2.83E-147 264 584,753 166,957 0.29 2.80E-05
PQS*EES*WDEEDKR S; S 377; 380 1; 1 4.73E-42 181 945,707 144,227 0.15 3.27E-05
SAEFPDFYDS*EEPVS*THQEAENEK S; S 626; 631 0.996; 0.863 3.45E-35 140 84,147 25,399 0.30 3.33E-05
SS*QGGSLPSEEK S 294 0.807 3.74E-09 133 23,609 5551 0.24 3.77E-05
S*SQGGSLPSEEK S 293 0.992 1.97E-30 180 104,456 26,549 0.25 4.48E-05
GHPQEES*EESNVSMASLGEK S 311 1 6.58E-209 232 930,043 206,513 0.22 5.91E-05
SAEFPDFYDSEEPVS*T*HQEAENEK S; T 631; 632 0.591; 0.586 3.45E-35 124 22,832 7174 0.31 5.94E-05
EKS*S*QES*GEET*GS*QENHPQESK S; S; S; T; S 235; 236; 239; 243; 245 0.643; 0.589; 0.589; 0.589; 0.589 1.47E-45 161 146,517 7750 0.05 7.50E-05
WQQQGDLQDT*KENR T 492 1 6.83E-05 85 92,934 34,420 0.37 9.99E-05
GHPQEES*EESNVS*M#ASLGEK S 317 0.988 1.45E-59 184 150,487 43,938 0.29 1.70E-04
ADEPQWSLY*PSDSQVS*EEVK Y; S 142; 149 0.981; 0.96 6.42E-108 134 22,916 11,918 0.52 1.87E-04
GHPQEES*EESNVS*MAS*LGEK S; S; S 314; 320; 323 0.739; 1; 1 1.83E-50 161 57,813 21,261 0.37 4.40E-04
WAEGGGHS*R S 130 1 4.86E-03 80 1039 214 0.21 5.96E-04
GHPQEES*EES*NVS*MASLGEK S; S; S 314; 317; 320 0.959; 0.959; 0.959 8.63E-27 145 41,766 12,932 0.31 6.63E-04
CIIEVLSNALS*K S 46 1 3.06E-04 94 994 401 0.40 1.63E-02
DHHS*THYRASEEEPEY*GEEIK S; T; Y 329; 330; 341 0.355; 0.355; 0.98 1.56E-02 43
DPADASEAHESS*S*RGEAGAPGEEDIQGPTK S; S 99; 100 0.651; 0.651 8.57E-07 62
LLRDPADAS*EAHESSSR S 93 0.989 5.88E-05 88
S*ETHAAGHS*QEK S 217 0.757 1.02E-02 78
CHMP2B − 1.5915 9.4237 − 9.3648 3.99E-06 6.71E-04 4.8895 Q9UQN3 Charged multivesicular body protein 2b ATIS*DEEIER S 199 1 3.83E-06 121 11,233 17,076 1.52 6.05E-03
CIC 1.0491 11.3820 6.0126 1.56E-04 4.52E-03 1.1627 I3L2J0 Protein capicua homolog KNS*TDLDSAPEDPTS*PK S; S 2303; 2315 0.697; 0.741 2.08E-04 75
KNST*DLDSAPEDPTS*PK S 2304; 2315 0.563; 0.831 2.08E-04 75
S*S*PPLPPPAEER S; S 2152; 2153 0.5; 0.5 1.43E-02 49
SS*PPLPPPAEER S 2153 0.795 1.43E-02 49
CLASP2 1.1382 9.5975 6.8774 5.38E-05 2.59E-03 2.2577 O75122 CLIP-associating protein 2 VLNT*GSDVEEAVADALK T 594 0.618 2.50E-06 99 7092 16,081 2.27 2.64E-04
VLNTGS*DVEEAVADALK S 596 0.848 7.26E-05 57
CTAGE5 − 1.5080 8.1531 − 6.2874 1.10E-04 3.78E-03 1.5219 O15320 Cutaneous T cell lymphoma-associated antigen 5 AFLS*PPTLLEGPLR S 536 0.954 4.01E-03 54 5494 8403 1.53 1.09E-03
APSDTGS*LS*PPWDQDRR S; S 594; 596 0.962; 0.997 2.71E-03 51 2316 3679 1.59 2.24E-03
S*NS*ELEDEILCLEK S; S 137; 139 0.5; 0.5 8.61E-34 89 2602 3220 1.24 3.50E-02
SNS*ELEDEILCLEK S 139 0.861 8.61E-34 89 2602 3220 1.24 3.50E-02
SFNMPS*LDK S 647 1 1.79E-02 59
CYBRD1 − 2.4812 7.6514 − 4.6620 1.00E-03 1.27E-02 − 0.7633 Q53TN4 Cytochrome b reductase 1 GSMPAY*SGNNMDK Y 252 0.662 1.47E-02 49
DCLK1 − 4.0867 9.7240 − 7.5568 2.48E-05 1.80E-03 3.0480 O15075 Serine/threonine-protein kinase DCLK1 SPSPSPTS*PGSLR S 337 0.911 9.87E-04 71
VCSS*MDENDGPGEEVSEEGFQIPATITER S 364 0.807 1.08E-03 48
DENR 1.0387 8.3264 3.5168 5.96E-03 3.62E-02 − 2.5921 O43583 Density-regulated protein LTVENS*PK S 73 1 4.03E-03 61
DMXL2 1.2471 8.1839 7.6581 2.22E-05 1.70E-03 3.1609 Q8TDJ6 DmX-like protein 2 KQS*EVEADLGYPGGK S 2640 1 3.59E-05 79
RQS*ENISAPPVLSEDIDK S 2399 0.839 2.63E-04 58
DOCK11 − 2.9596 8.1805 − 7.3717 3.05E-05 1.98E-03 2.8384 Q5JSL3 Dedicator of cytokinesis protein 11 ETVETAQDDET*SSQGK T; S; S 294; 295; 296 0.333; 0.333; 0.333 1.02E-02 45
DOCK7 − 1.0075 9.0073 − 3.4301 6.87E-03 3.96E-02 − 2.7360 Q96N67 Dedicator of cytokinesis protein 7 SLSNS*NPDISGTPT*SPDDEVR S; T 900; 909 0.9; 0.402 2.45E-06 47 1576 2651 1.68 9.54E-03
ELAVL4 2.1977 9.9185 4.3468 1.61E-03 1.66E-02 − 1.2507 P26378 ELAV-like protein 4 NCPS*PMQTGATTDDSK S 33 0.999 4.54E-04 80 6680 11,145 1.67 9.07E-04
EPB41L2 1.9271 8.3372 6.2524 1.15E-04 3.86E-03 1.4768 O43491 Band 4.1-like protein 2 EIS*PGSGPGEIR S 715 0.607 1.49E-02 56 4934 9864 2.00 7.50E-05
S*YTLVVAK S 87 1 5.02E-03 99 9974 25,384 2.54 1.55E-04
EISPGS*GPGEIR S 718 0.971 9.87E-03 81 4771 9142 1.92 4.81E-03
EVAENQQNQS*S*DPEEEK S; S 38; 39 0.5; 0.5 1.38E-02 50
EVAENQQNQSS*DPEEEK S 39 0.88 3.99E-81 111
EPB49 1.1990 8.2266 6.2015 1.22E-04 4.01E-03 1.4108 Q08495 Dematin GNS*LPCVLEQK S 333 1 4.66E-15 152 21,088 48,603 2.30 2.12E-05
RGAEEEEEEEDDDS*GEEMK S 226 1 2.45E-73 205 66,329 117,453 1.77 1.15E-04
STS*PPPS*PEVWADSR S; S 92; 96 0.734; 0.978 2.86E-03 56 4181 9523 2.28 3.97E-04
STS*PPPSPEVWADSR S 92 0.994 4.97E-08 103 27,533 42,218 1.53 5.58E-04
ESVGGS*PQTK S 156 1 6.79E-03 92 2009 4972 2.47 1.18E-03
SSS*LPAYGR S 289 0.998 3.78E-05 134 8120 15,700 1.93 1.35E-03
DSSVPGS*PSSIVAK S 26 0.927 5.87E-04 69
EPS15L1 1.0173 7.5204 5.6410 2.54E-04 5.89E-03 0.6596 Q9UBC2 Epidermal growth factor receptor substrate 15-like 1 TVFPGAVPVLPAS*PPPK S 229 1 1.50E-12 79 1597 2107 1.32 8.98E-02
STPSHGSVSSLNSTGSLS*PK S 255 0.759 1.29E-04 79
FKBP5 2.6927 9.7556 4.9688 6.43E-04 9.84E-03 − 0.3023 Q13451 Peptidyl-prolyl cis-trans isomerase FKBP5 NNEES*PTATVAEQGEDITSK S 13 0.91 9.79E-06 43
FLVCR1 − 1.0780 9.4860 − 5.7358 2.24E-04 5.54E-03 0.7898 Q9Y5Y0 Feline leukemia virus subgroup C receptor-related protein 1 AIPADS*PTDQEPK S 536 0.965 5.83E-04 83 5787 10,532 1.82 2.55E-04
FOXK1 − 1.0565 7.7540 − 4.9965 6.18E-04 9.66E-03 − 0.2613 P85037 Forkhead box protein K1 EGS*PI*DPEFGSK S 445 1 1.27E-06 87 3949 9911 2.51 1.37E-03
GAL − 8.5314 9.5656 − 12.9818 2.17E-07 1.45E-04 7.6993 P22466 Galanin peptides LLDLPAAAS*SEDIERS S 116 0.991 1.09E-82 227 161,450 28,762 0.18 1.20E-06
LLDLPAAAS*SEDIERS* S; S; S 116; 117; 123 0.658; 0.658; 0.684 3.21E-10 98 11,171 6908 0.62 4.26E-03
LLDLPAAAS*SEDIERS* S 116; 123 0.878; 0.716 2.09E-04 74 11,171 6908 0.62 4.26E-03
GATAD2A 1.0255 8.6559 4.3609 1.58E-03 1.64E-02 − 1.2285 Q86YP4 Transcriptional repressor p66-alpha RPPS*PDVIVLS*DNEQPS*S*PR S 100; 107; 113; 114 1; 0.87; 0.581; 0.548 3.91E-07 52
GH1 − 12.5607 10.1374 − 20.6323 2.98E-09 7.99E-06 11.3526 P01241 Somatotropin FDTNS*HNDDALLK S 176 1 3.20E-216 194 580,220 43,527 0.08 1.93E-06
FDT*NSHNDDALLK T 174 0.578 1.80E-03 70
SVFANSLVYGAS*DSNVYDLLK S; S 132; 134 0.428; 0.428 1.85E-10 51
GH2 − 11.4395 7.8750 − 30.7801 6.85E-11 5.16E-07 13.8468 P01242 Growth hormone variant S*HNDDALLK S 176 1 6.34E-05 131
YSFLQNPQT*SLCFSESIPTPSNR T; S 76; 77 0.481; 0.481 1.63E-02 37
GNAS − 2.6651 5.5317 − 5.7789 2.11E-04 5.36E-03 0.8486 Q5JWF2 Guanine nucleotide-binding protein G(s) subunit alpha isoforms XLas ISTAS*GDGR S 995 1 1.21E-04 115 11,879 32,931 2.77 2.32E-05
GPATCH8 1.0279 9.8805 7.4027 2.94E-05 1.96E-03 2.8738 Q9UKJ3 G patch domain-containing protein 8 GSEGDCS*PEDK S 1081 1 1.74E-02 60 2087 4025 1.93 5.01E-03
H1FX 1.2641 10.5253 5.0038 6.11E-04 9.60E-03 − 0.2505 Q92522 Histone H1x AGGSAALS*PSK S 31 0.996 1.62E-02 80 5113 13,633 2.67 4.62E-05
HIST1H1D 2.2738 9.1170 3.4293 6.87E-03 3.96E-02 − 2.7374 P16402 Histone H1.3 GTGASGS*FK S 105 0.969 7.22E-03 74 5296 22,242 4.20 8.93E-06
KAS*GPPVSELITK S 37 1 3.45E-10 128 57,689 15,516 0.27 2.58E-04
HNRNPA1 − 1.0812 8.4803 − 4.0578 2.51E-03 2.15E-02 − 1.7091 P09651 Heterogeneous nuclear ribonucleoprotein A1 SES*PKEPEQLR S 6 1 1.83E-13 144 59,421 186,063 3.13 7.49E-06
HTATSF1 − 2.5483 13.6709 − 11.7593 5.31E-07 2.15E-04 6.8570 O43719 HIV Tat-specific factor 1 LFEDDDS*NEKLFDEEEDSSEK S 702 1 2.14E-175 268 288,110 160,883 0.56 1.95E-04
EFDEDS*DEKEEEEDTYEK S 624 1 1.72E-69 202 204,193 125,980 0.62 2.23E-04
ESEDDLNKES*EEEVGPTK S 529 1 1.68E-56 192 88,307 60,457 0.68 2.99E-04
LFDEEEDS*S*EKLFDDSDER S; S 713; 714 1; 1 1.56E-26 142 44,719 23,576 0.53 4.06E-04
LFDDS*DER S 721 1 2.71E-03 131 34,573 22,836 0.66 1.16E-03
ELHENVLDKELEENDS*ENS*EFEDDGS*EK S; S; S 597; 600; 607 1; 1; 1 1.27E-106 208 20,769 11,906 0.57 2.31E-03
VFDDES*DEKEDEEYADEK S 642 1 1.66E-199 294 239,173 191,277 0.80 5.55E-03
DLDEEGS*EKELHENVLDK S 579 1 3.61E-80 210 202,810 176,810 0.87 3.93E-02
LFEES*DDKEDEDADGKEVEDADEK S 676 1 7.74E-152 254 366,127 394,907 1.08 1.47E-01
ESS*PEKEAEEGCPEK S 453 0.989 1.37E-31 165 93,516 101,601 1.09 2.04E-01
TEDGGEFEEGAS*ENNAKES*SPEKEAEEGCPEK S; S 445; 452 0.704; 0.857 8.12E-04 56 15,981 14,532 0.91 3.02E-01
VLDEEGS*ER S 616 1 1.23E-04 117 11,426 11,535 1.01 9.22E-01
ESEEDDSEKES*DEDCSEK S 548 0.997 4.34E-04 69
IGFBP5 − 2.6828 10.6639 − 4.1430 2.20E-03 1.99E-02 − 1.5728 P24593 Insulin-like growth factor-binding protein 5 EHEEPT*TSEMAEETYSPK T 123 0.346 2.19E-04 56
IPCEF1 1.0260 5.0367 4.7825 8.41E-04 1.15E-02 − 0.5806 Q8WWN9 Interactor protein for cytohesin exchange factors 1 AS*PAPDDTDDTPQELK S 411 1 6.48E-10 113 2761 8305 3.01 4.03E-05
ITGA2B 2.0755 7.4627 7.3744 3.04E-05 1.98E-03 2.8416 P08514 Integrin alpha-IIb AGSEPQALFCS*GVPK S 880 0.996 2.38E-03 60
ITPR1 − 1.2600 12.0938 − 5.0407 5.80E-04 9.29E-03 − 0.1962 Q14643 Inositol 1,4,5-trisphosphate receptor type 1 PQKHES*T*SS*Y*NYRVVK S; T; S; S; Y; Y 1177; 1178; 1180; 1181; 1183 0.843; 0.855; 0.896; 0.55; 0.55 2.27E-02 37
ITPR2 1.8582 7.8515 10.0154 2.22E-06 5.18E-04 5.4714 Q14571 Inositol 1,4,5-trisphosphate receptor type 2 DS*FVEEGNTLRK S 1687 0.966 8.73E-03 44 125,980 84,783 0.67 2.20E-03
KBTBD11 − 3.5584 10.5689 − 4.7908 8.31E-04 1.14E-02 − 0.5681 O94819 Kelch repeat and BTB domain-containing protein 11 AGS*RPQS*PSGDADAR S; S 310; 314 0.989; 0.838 1.26E-03 57 2029 5855 2.89 1.34E-04
KIF13B − 1.0053 12.0356 − 5.8909 1.82E-04 4.94E-03 1.0001 Q9NQT8 Kinesin-like protein KIF13B SIS*SPNVNR S 1381 0.98 5.62E-03 78 6201 20,094 3.24 4.31E-05
SISS*PNVNR S 1382 0.654 7.49E-03 74
KIF16B − 1.3826 8.3241 − 5.2223 4.49E-04 8.02E-03 0.0682 Q96L93 Kinesin-like protein KIF16B S*KT*T*IT*NLK S; T; T; T 31; 33; 34; 36 0.25; 0.25; 0.25; 0.25 2.62E-02 47
KIF1A 1.6590 7.9552 6.7867 5.98E-05 2.74E-03 2.1477 Q12756 Kinesin-like protein KIF1A SDS*LILDHQWELEK S 1370 0.98 1.89E-33 131 20,803 21,162 1.02 9.30E-01
LIMA1 1.5971 11.6365 8.0040 1.53E-05 1.41E-03 3.5371 Q53GG0; Q9UHB6 LIM domain and actin-binding protein 1 ET**SPGVEDAPIAK T 487 0.577 5.99E-04 75 2533 3843 1.52 2.94E-02
LIM domain and actin-binding protein 1 QQS*PQEPK S 698 1 9.90E-03 79 4890 18,709 3.83 1.00E-05
ET*S*PGVEDAPIAK S 490 0.983 1.43E-10 119 6800 11,621 1.71 3.83E-05
EGHSLEMENENLVENGADS*DEDDNSFLK S 686 1 0.00E+00 141 3725 5941 1.59 1.17E-02
S*QDVELWEGEVVK S 726 1 2.48E-04 57
MAP4 1.0684 8.1047 4.7053 9.42E-04 1.22E-02 − 0.6974 P27816 Microtubule-associated protein 4 DVT*PPPETEVVLIK T 521 1 3.18E-40 153 66,008 110,437 1.67 1.71E-04
VGS*LDNVGHLPAGGAVK S 1073 1 6.49E-06 91 11,125 28,354 2.55 2.17E-04
DM#ES*PTKLDVTLAK S 280 0.981 5.00E-05 82 30,879 33,386 1.08 1.59E-01
DMESPT*KLDVTLAK T 282 0.605 7.94E-06 109
DMS*PLSETEMALGK S 507 0.999 8.64E-07 115
KCS*LPAEEDSVLEK S 636 1 8.51E-06 77
LATNTS*APDLK S 928 0.975 1.89E-03 105
MARCKS − 1.2368 12.4473 − 4.0541 2.53E-03 2.16E-02 − 1.7149 P29966 Myristoylated alanine-rich C-kinase substrate GEAAAERPGEAAVASS*PSK S 27 0.96 1.32E-26 143 9538 82,612 8.66 3.86E-06
GEAAAERPGEAAVAS*SPSK S 26 0.878 8.86E-22 126 10,104 57,201 5.66 8.79E-06
AEDGATPSPSNET*PK T 150 0.998 2.15E-16 140 7219 27,996 3.88 1.52E-05
AEDGATPS*PSNETPK S 145 0.959 1.58E-04 79 2120 3439 1.62 8.15E-03
AEDGATPSPS*NETPK S 147 0.936 5.11E-04 73
EAPAEGEAAEPGS*PTAAEGEAASAASSTSSPK S; T 118; 120 0.499; 0.499 2.84E-58 66
EAPAEGEAAEPGS*PTAAEGEAASAASSTSSPK S; T; S 128; 130; 138 0.323; 0.323; 0.323 3.44E-05 53
EAPAEGEAAEPGS*PTAAEGEAASAASSTSSPK S; T 118; 120 0.499; 0.499 2.84E-58 66
GEPAAAAAPEAGAS*PVEK S 101 1 7.07E-58 115
MPP2 2.5276 8.8393 8.7985 6.85E-06 8.95E-04 4.3503 Q14168 MAGUK p55 subfamily member 2 TYET*PPPS*PGLDPTFSNQPVPPDAVR T; S 141; 145 0.833; 0.998 1.77E-68 80 4500 5589 1.24 1.22E-03
T*YETPPPS*PGLDPTFSNQPVPPDAVR T; S 138; 145 0.537; 0.988 6.46E-35 63
MYH14 − 1.1390 8.6919 − 7.3970 2.96E-05 1.97E-03 2.8674 Q7Z406 Myosin-14 LEEGVAS*DEEAEEAQPGSGPSPEPEGSPPAHPQ S 1969 1 5.84E-67 84 249 413 1.66 2.86E-01
NBEAL1 − 1.0097 7.8731 − 4.4773 1.32E-03 1.47E-02 − 1.0471 H7C3C8 Neurobeachin-like protein 1 (Fragment) SSDQTNYETEGASIQS*RK S 24 0.805 1.12E-02 35
NCOA7 − 1.4212 7.9855 − 5.1172 5.21E-04 8.71E-03 − 0.0842 H0UI55 Nuclear receptor coactivator 7 DALPS*PGEWEDLASEK S 295 0.99 3.21E-03 45
VLS*ST*S*EEDEPGVVK S; T; S 208; 210; 211 0.87; 0.475; 0.475 6.29E-06 61
VLSS*TS*EEDEPGVVK S; S 209; 211 0.77; 0.849 1.45E-04 79
NDRG1 − 3.0949 12.3453 − 3.9310 3.07E-03 2.42E-02 − 1.9133 Q92597 Protein NDRG1 TASGSS*VTS*LDGTR S; S 333; 336 0.728; 0.85 1.03E-02 50 3618 7439 2.06 3.36E-04
NGEF − 3.0287 7.9883 − 4.7608 8.68E-04 1.17E-02 − 0.6133 Q8N5V2 Ephexin-1 SVPIIS*HS*RWLLK S; S 485; 487 0.914; 0.914 1.29E-02 44 12,102 18,559 1.53 1.83E-03
NPDC1 1.0179 15.1785 5.9750 1.63E-04 4.64E-03 1.1127 Q5SPY9 Neural proliferation differentiation and control protein 1 APGS*PAAPR S 307 1 9.47E-04 96 4714 20,392 4.33 4.33E-05
IS*PGDQR S 314 1 1.93E-02 86 4139 5242 1.27 4.50E-02
NUP155 1.1429 8.4281 5.2846 4.12E-04 7.62E-03 0.1579 O75694 Nuclear pore complex protein Nup155 AAPQS*PSVPK S 992 0.997 5.58E-06 120 6885 16,363 2.38 7.92E-05
AAPQSPS*VPK S 994 0.602 5.58E-06 120 6885 16,363 2.38 7.92E-05
OGFR − 2.1155 15.1596 − 4.0398 2.59E-03 2.19E-02 − 1.7379 Q9NZT2 Opioid growth factor receptor SQGDEAGGHGEDRPEPLS*PK S 378 1 7.10E-04 61
OTUB1 1.4872 9.2341 8.6065 8.28E-06 9.95E-04 4.1600 F5GYJ8 Ubiquitin thioesterase OTUB1 QEPLGSDS*EGVNCLAYDEAIMAQQDR S 18 0.48 7.04E-03 34
OXR1 1.1946 5.7526 3.3704 7.57E-03 4.20E-02 − 2.8353 Q8N573 Oxidation resistance protein 1 VVSSTS*EEEEAFTEK S 204 0.889 2.36E-07 100
PDLIM4 − 3.5737 9.5064 − 5.5832 2.74E-04 6.13E-03 0.5795 P50479 PDZ and LIM domain protein 4 IHIDPEIQDGS*PTTSR S 112 0.923 3.16E-04 75 9266 15,989 1.73 2.03E-03
PEA15 1.1346 10.2397 4.5028 1.27E-03 1.45E-02 − 1.0077 Q15121 Astrocytic phosphoprotein PEA-15 QPS*EEEIIK S 116 1 8.87E-06 138 18,264 123,410 6.76 1.32E-05
PGM5 − 1.0253 5.8422 − 3.2170 9.75E-03 4.94E-02 − 3.0915 Q15124 Phosphoglucomutase-like protein 5 AAGGIILTAS*HCPGGPGGEFGVK S 122 0.54 2.66E-64 85
PKM2 1.1738 12.2601 6.1513 1.31E-04 4.15E-03 1.3453 P14618 Pyruvate kinase LDIDS*PPITAR S 37 0.912 1.63E-02 47
PLEC 1.1685 10.5703 3.6270 4.98E-03 3.25E-02 − 2.4100 Q15149 Plectin SDEGQLS*PATR S 720 0.995 1.49E-02 82 6049 13,023 2.15 3.16E-04
RAS*FAEK S 1721 1 1.18E-02 55 3016 5604 1.86 9.12E-04
TQLAS*WSDPTEETGPVAGILDTETLEK S 4406 0.909 1.61E-15 97
PLXNC1 1.8664 7.1756 5.5181 2.99E-04 6.40E-03 0.4886 O60486 Plexin-C1 KQS*QQLELLESELR S 978 1 3.76E-03 47 2450 4156 1.70 3.71E-03
POMC − 7.4874 13.4887 − 3.8110 3.71E-03 2.70E-02 − 2.1083 P01189 Pro-opiomelanocortin ACKPDLS*AETPMFPGNGDEQPLTENPR S 55 0.997 1.64E-56 167 499,933 41,941 0.08 7.79E-07
REDVS*AGEDCGPLPEGGPEPR S 108 1 7.15E-166 277 379,447 90,008 0.24 3.01E-06
ACKPDLSAET*PM#FPGNGDEQPLTENPR T 58 0.759 1.26E-45 159 75,285 1928 0.03 8.83E-06
VYPNGAEDES*AEAFPLEFK S 168 1 3.56E-34 127 17,243 8453 0.49 8.78E-04
EGDGPDGPADDGAGAQADLEHS*LLVAAEK S 208 1 1.38E-05 58
S*DGAKPGPR S 125 1 1.04E-02 54
PRKAR2B − 1.0157 9.9746 − 3.4835 6.29E-03 3.75E-02 − 2.6473 P31323 cAMP-dependent protein kinase type II-beta regulatory subunit RAS*VCAEAYNPDEEEDDAESR S 114 1 4.14E-31 139 37,944 26,707 0.70 6.75E-04
PRKCD 1.9942 9.0704 7.0482 4.40E-05 2.33E-03 2.4620 Q05655 Protein kinase C delta type ASTFCGTPDY*IAPEILQGLK Y 514 0.511 4.36E-02 33
PRKG1 − 2.0409 5.5427 − 4.3580 1.58E-03 1.65E-02 − 1.2331 Q13976 cGMP-dependent protein kinase 1 T*WTFCGTPEYVAPEIILNK T 515 0.543 4.49E-15 123
PXN 1.0331 7.0094 3.5188 5.94E-03 3.61E-02 − 2.5888 P49023 Paxillin S*SPGGQDEGGFMAQGK S; S 302; 303 0.5; 0.5 1.30E-03 55 1185 1696 1.43 2.16E-02
TGSSS*PPGGPPKPGSQLDSMLGSLQSDLNK S 322 0.702 5.72E-19 89 4056 7646 1.88 4.60E-02
T*GS*SSPPGGPPKPGSQLDSMLGSLQSDLNK T; S 318; 320 0.259; 0.259 4.20E-06 54
T*GSSSPPGGPPKPGSQLDSMLGSLQSDLNK T 318 0.291 4.31E-19 96
QSOX2 − 1.2837 6.3713 − 4.1443 2.20E-03 1.99E-02 − 1.5708 Q6ZRP7 Sulfhydryl oxidase 2 DNLLDTYSADQGDS*SEGGTLAR S 578 0.821 1.21E-12 81
DNLLDTYSADQGDSS*EGGTLAR S 579 0.556 5.21E-33 72
RCAN1 − 1.0836 9.4397 − 3.4404 6.75E-03 3.91E-02 − 2.7189 P53805 Calcipressin-1;Calcipressin-3 QFLIS*PPAS*PPVGWK S; S 163; 167 1; 1 1.21E-25 100 5957 12,269 2.06 1.21E-03
RRBP1 − 1.3657 10.4681 − 3.9869 2.81E-03 2.30E-02 − 1.8230 Q9P2E9 Ribosome-binding protein 1 NTDVAQS*PEAPK S 615 0.996 3.41E-04 55
SCG3 1.2843 10.4326 4.3052 1.72E-03 1.72E-02 − 1.3159 Q8WXD2 Secretogranin-3 ELS*AERPLNEQIAEAEEDKIK S 37 1 2.80E-38 149 54,239 25,082 0.46 1.50E-04
LFPAPS*EKSHEETDSTK S 359 0.468 1.85E-02 36
SET − 1.7108 7.9953 − 8.2175 1.23E-05 1.23E-03 3.7624 Q01105 Protein SET LNEQAS*EEILK S 63 1 1.18E-16 87
SLC2A4 − 1.3065 5.1362 − 4.4148 1.45E-03 1.56E-02 − 1.1444 P14672 Solute carrier family 2, facilitated glucose transporter member 4 T*PSLLEQEVKPSTELEYLGPDEND T; S 486; 488 0.5; 0.5 1.85E-10 91
SLC46A1 − 1.1586 7.9882 − 4.9777 6.35E-04 9.78E-03 − 0.2891 A0A024QZ15 Proton-coupled folate transporter AD*LEFQQFPQS*P S 458 1 1.60E-05 107 19,669 26,410 1.34 4.09E-02
SLC7A14 − 1.3388 10.1699 − 4.9424 6.68E-04 1.01E-02 − 0.3414 Q8TBB6 Probable cationic amino acid transporter EACS*PVS*EGDEFSGPATNTCGAK S 465; 468 0.998; 0.939 1.03E-05 67
SLC8A2 2.0827 10.6502 5.7643 2.15E-04 5.42E-03 0.8287 Q9UPR5 Sodium/calcium exchanger 2 GIS*ALLLNQGDGDR S 622 1 7.55E-34 97 1039 1666 1.60 1.40E-01
SMARCA2 − 1.0693 9.4559 − 3.2347 9.47E-03 4.85E-02 − 3.0619 P51531 Probable global transcription activator SNF2L2 AKPVVS*DFDS*DEEQDER S 1568; 1572 1; 1 3.09E-10 108 8702 21,740 2.50 1.06E-05
SNAP23 1.0040 6.7468 4.8842 7.26E-04 1.05E-02 − 0.4280 O00161 Synaptosomal-associated protein; synaptosomal-associated protein 23 TTWGDGGENS*PCNVVSK S 110 1 3.54E-04 83 5323 11,357 2.13 6.07E-04
SNX17 1.1624 8.6272 4.3682 1.56E-03 1.63E-02 − 1.2171 Q15036 Sorting nexin-17 VTSSVPLPSGSTSS*PGR S 336 0.658 4.46E-05 89 5947 11,510 1.94 1.05E-03
VTSSVPLPS*GS*T*S*S*PGR S; S; T; S; S 331; 333; 334; 335; 336 0.2; 0.2; 0.2; 0.2; 0.2 1.79E-03 67
SORBS2 − 2.2906 6.9395 − 3.2557 9.15E-03 4.75E-02 − 3.0267 O94875 Sorbin and SH3 domain-containing protein 2 QNAEIWSS*T*EET*VS*PKIK S; T; T; S 370; 371; 374; 376 0.238; 0.238; 0.238; 0.238 1.88E-02 34
SORT1 1.0872 11.4517 5.0676 5.58E-04 9.09E-03 − 0.1567 Q99523 Sortilin SGYHDDS*DEDLLE S 825 0.999 1.99E-04 99 4842 5538 1.14 1.42E-01
SPAG9 1.1290 9.1942 3.9292 3.08E-03 2.42E-02 − 1.9163 O60271 C-Jun-amino-terminal kinase-interacting protein 4 SAS*QS*SLDKLDQELK S; S 730; 732 0.834; 0.554 2.29E-17 145 21,879 108,870 4.98 2.23E-07
SAS*QS*S*LDKLDQELK S 730; 732; 733 0.577; 0.507; 0.91 5.14E-44 145 11,070 44,110 3.98 4.65E-06
SPARCL1 − 3.1871 13.6134 − 4.7627 8.66E-04 1.17E-02 − 0.6103 Q14515 SPARC-like protein 1 EES*HEQS*AEQGK S 80 9.37E-38 176 44,342 10,604 0.24 1.86E-05
SSS*QELGLK S 92 0.999 1.61E-28 188 100,790 28,141 0.28 1.91E-05
SKEES*HEQS*AEQGK S; S 80; 84 0.999; 1 9.37E-38 176 57,751 20,002 0.35 5.38E-05
EHANS*KQEEDNTQSDDILEESDQPTQVSK S 231 1 5.61E-70 176 20,394 7396 0.36 5.25E-04
HIQETEWQS*QEGK S 295 1 2.83E-21 152 24,777 14,058 0.57 1.15E-03
DQGNQEQDPNIS*NGEEEEEKEPGEVGTHNDNQER S 198 0.997 1.10E-05 53 2334 2083 0.89 1.92E-01
EHANS*KQEEDNT*QS*DDILEESDQPTQVSK S; T; S 231; 238; 240 0.331; 0.331; 0.331 1.44E-02 38
S*KEES*HEQSAEQGK S; S 76; 80 0.999; 0.995 7.77E-05 77
SPTB 1.0905 6.8402 3.9457 3.00E-03 2.38E-02 − 1.8895 P11277 Spectrin beta chain, erythrocyte LS*S*S*WESLQPEPSHPY S 2123; 2124; 2125 0.653; 0.693; 0.653 1.43E-23 154 9038 16,844 1.86 4.11E-04
WDAPDDELDNDNSS*AR S 36 0.964 1.15E-18 148 6818 10,720 1.57 7.93E-04
LSS*S*WESLQPEPSHPY S 2124; 2125 0.582; 0.983 4.77E-17 137 18,958 25,412 1.34 5.27E-03
LSSS*WES*LQPEPSHPY S; S 2125; 2128 0.86; 1 8.08E-10 116 12,384 10,854 0.88 7.41E-02
VLS*PVDSGNK S 1226 1 4.33E-03 63
SRRM2 1.1850 7.1163 6.1599 1.29E-04 4.14E-03 1.3566 Q9UQ35 Serine/arginine repetitive matrix protein 2 TS*PPLLDR S 2398 0.899 1.67E-02 70 2052 6736 3.28 7.74E-07
EISS*S*PTSK S 455; 456 0.657; 0.864 4.72E-04 99 8692 22,507 2.59 1.78E-06
RPS*PQPS*PR S 2702; 2706 1; 1 1.13E-03 78 6822 21,190 3.11 4.31E-06
SGMS*PEQSR S 1132 0.767 5.10E-03 79 6423 23,060 3.59 4.83E-06
SST*PPRQS*PSR S; S 903; 908 0.756; 0.93 1.17E-02 89 7024 17,287 2.46 1.19E-05
S*S*T*PPRQSPSR S; S; T 901; 902; 903 0.4476; 0.447; 0.849 1.17E-02 89 7916 19,551 2.47 1.62E-05
SS*RSSPELTR S 1691 0.505 1.44E-02 60 3402 9542 2.81 1.81E-05
S*RS*PLAIR S; S 2044; 2046 1; 1 1.07E-02 63 11,261 26,216 2.33 2.12E-05
ENSFGS*PLEFR S 1329 1 3.46E-05 113 12,581 33,168 2.64 2.86E-05
EIS*SS*PTSK S 454 0.946 1.16E-03 94 9678 23,505 2.43 2.97E-05
S*S*RS*SPELTR S; S; S 1690; 1691; 1693 0.501; 0.501; 0.664 1.44E-02 60 1233 3849 3.12 3.92E-05
T*SPPLLDR T 2397 0.949 9.09E-03 125 1856 5793 3.12 4.14E-05
S*RS*PATAK S; S 484; 486 1; 1 8.82E-03 66 2800 13,671 4.88 5.19E-05
SCFES*S*PDPELK S; S 875; 876 1; 1 2.22E-05 118 15,580 35,770 2.30 5.73E-05
SLS*YS*PVER S; S 2692; 2694; 0.997; 0.999 2.02E-04 103 9902 30,084 3.04 7.07E-05
VPS*PTPAPK S 2581 0.964 1.55E-02 61 3557 11,007 3.09 7.34E-05
GEFSAS*PMLK S 1124 0.999 1.38E-03 104 14,424 35,744 2.48 9.98E-05
SGS*S*PGLR S; S 1443; 1444 0.933; 0.899 5.07E-03 82 4248 12,065 2.84 1.49E-04
QGSITS*PQANEQSVT*PQRR S; T 857; 866 0.761; 0.779 5.23E-03 63 1771 5578 3.15 1.74E-04
AQT*PPGPSLSGSKS*PCPQEK S 1014 0.984 5.61E-04 65 2459 11,814 4.81 2.24E-04
SATRPS*PS*PER S; S 351; 353 0.982; 0.994 5.54E-03 85 9186 27,782 3.02 2.36E-04
S*RT*PPVTR S; T 1925; 1927 1; 1 1.78E-02 72 3013 9448 3.14 3.54E-04
GDS*RS*PSHK S; S 2727; 2729 1; 0.999 1.24E-02 57 2764 11,511 4.16 4.64E-04
S*PS*PASGR S; S 295; 297 1; 0.999 3.32E-04 77 8935 20,961 2.35 7.87E-04
S*SSPVTELASRS*PIR S 1112 0.834 4.10E-05 83 3678 7472 2.03 1.16E-03
S*VS*PCSNVESR S; S 952; 954 1; 0.978 1.24E-02 84 6685 12,365 1.85 1.24E-03
S*LS*GSSPCPK S; S 778; 780 1; 0.999 1.17E-04 112 31,177 58,376 1.87 1.42E-03
AQT*PPGPSLSGS*KSPCPQEK T; S 1003; 1012 1; 0.693 1.98E-15 123 1958 4374 2.23 3.06E-03
SS*SPVTELASR S 1102 0.567 4.10E-05 87 10,436 15,924 1.53 5.33E-03
S*RS*PSSPELNNK S; S 1497; 1499 0.93; 0.93 5.98E-03 66 11,049 15,474 1.40 1.06E-02
RGEGDAPFSEPGTTSTQRPS*S*PETATK S 322; 323 0.646; 0.847 2.10E-17 101 1813 2549 1.41 1.28E-02
SSS*PVTELASR S 1103 0.993 3.29E-15 154 8557 9123 1.07 3.32E-01
GRS*ECDS*S*PEPK S; S; S 1477; 1482; 1483 1; 1; 1 1.00E-07 128 10,775 11,809 1.10 3.56E-01
AQT*PPGPSLS*GSKSPCPQEK S 1010 0.996 3.50E-03 55
EISSSPT*SK T 458 0.551 1.25E-02 75
ERS*GS*ESSVDQK S; S 1517; 1519 0.993; 0.995 5.19E-03 65
GQSQT*SPDHR T; S 1063; 1064 0.496; 0.496 2.73E-02 79
HGGS*PQPLATTPLSQEPVNPPSEAS*PT*R S 377; 398; 400 0.991; 0.415; 0.415 3.40E-04 37
HGGS*PQPLATTPLSQEPVNPPS*EASPTR S 395 0.953 3.40E-04 37
MALPPQEDAT*ASPPR T 1177 0.855 1.04E-02 55
QSHSES*PSLQSK S 1083 0.994 3.88E-03 96
RGEGDAPFSEPGT*T*S*T*QRPS*S*PETATK T; T; S; T; S; S 315; 316; 317; 318; 322; 323 0.325; 0.325; 0.325; 0.325; 0.325; 0.325 3.79E-03 50
RGEGDAPFSEPGT*T*S*T*QRPS*S*PETATK T; S; T 316; 317; 318 0.399; 0.399; 0.399 1.72E-04 57
S*DTSSPEVR S 1069 0.307 6.58E-03 66
S*GS*S*QELDVKPS*AS*PQER S; S; S; S; S 1539; 1541; 1542; 1550; 1552 0.334; 0.334; 0.334; 0.499; 0.499 2.63E-03 54
S*SS*PVTELASR S; S 1101; 1103 0.96; 0.955 4.10E-05 83
SSBP3 1.2570 7.2589 4.6141 1.08E-03 1.32E-02 − 0.8364 Q9BWW4 Single-stranded DNA-binding protein 3 NS*PNNISGISNPPGTPR S 347 1 4.55E-14 121 5331 8383 1.57 4.80E-03
STIM1 1.0481 9.8564 4.9079 7.01E-04 1.03E-02 − 0.3927 Q13586 Stromal interaction molecule 1 AEQS*LHDLQER S 257 1 1.74E-02 78
STK39 1.4339 11.9102 6.9767 4.79E-05 2.42E-03 2.3769 Q9UEW8 STE20/SPS1-related proline-alanine-rich protein kinase TEDGDWEWS*DDEMDEK S 385 1 1.13E-219 188 27,341 40,397 1.48 7.42E-04
STX1A 1.8823 8.2932 5.5539 2.85E-04 6.25E-03 0.5386 Q16623 Syntaxin-1A TAKDS*DDDDDVAVTVDR S 14 1 4.88E-57 182 72,880 183,350 2.52 9.68E-06
STXBP5L 1.5925 9.9115 4.8616 7.50E-04 1.07E-02 − 0.4617 Q9Y2K9 Syntaxin-binding protein 5-like SS*SISS*IDKDSK S; S; S 819; 820; 823 0.346; 0.346; 0.831 1.68E-02 44
SYN1 − 1.0498 11.9626 − 5.4376 3.34E-04 6.79E-03 0.3755 P17600 Synapsin-1 PVAGGPGAPPAARPPAS*PS*PQR S; S 551; 553 1; 1 1.08E-22 130 8100 33,111 4.09 3.59E-04
TCEA1 − 1.0289 8.8480 − 4.2602 1.84E-03 1.79E-02 − 1.3868 P23193 Transcription elongation factor A protein 1 EPAITSQNS*PEAR S 100 1 1.20E-47 197 66,104 140,043 2.12 8.61E-05
TCF12 − 1.1897 9.1467 − 3.9273 3.09E-03 2.42E-02 − 1.9193 A0A024R5Z0 Transcription factor 12 T*S*ST*NEDEDLNPEQK T; S; T 581; 582; 584 0.256; 0.256; 0.256 3.44E-02 44
TLN2 1.5289 7.8000 4.3424 1.62E-03 1.67E-02 − 1.2575 Q9Y4G6 Talin-2 LDEGT*PPEPK T 1843 1 1.10E-02 93 7613 8825 1.16 1.17E-02
TNKS1BP1 1.0225 7.5659 4.1437 2.20E-03 1.99E-02 − 1.5717 Q9C0C2 182 kDa tankyrase-1-binding protein AS*PEPPGPESSSR S 672 1 1.03E-03 70 6115 19,567 3.20 2.19E-05
VPS*S*DEEVVEEPQSR S; S 1620; 1621 1; 1 1.38E-16 138 7581 16,197 2.14 4.57E-05
GWS*QEGPVK S 221 1 1.47E-02 62 6177 15,957 2.58 6.76E-05
LDS*PPPSPITEASEAAEAAEAGNLAVSSR S 494 0.984 1.15E-12 88
LDS*PPPSPITEASEAAEAAEAGNLAVSSR S 494; 498 0.492; 0.492 1.22E-05 38
TPI1 1.7746 10.1649 6.8527 5.53E-05 2.64E-03 2.2279 P60174 Triosephosphate isomerase KQS*LGELIGTLNAAK S 58 1 5.49E-61 200 57,287 111,203 1.94 2.76E-05
IIYGGS*VTGATCK S 249 0.992 7.14E-37 110
TPMT 1.0815 6.1567 4.4016 1.48E-03 1.58E-02 − 1.1650 P51580 Thiopurine S-methyltransferase TSLDIEEYS*DTEVQK S 14 0.806 4.23E-05 86
TRIM3 1.4660 5.4120 4.1545 2.16E-03 1.97E-02 − 1.5544 O75382 Tripartite motif-containing protein 3 REDS*PGPEVQPMDK S 7 1 8.45E-07 73
TSC2 2.1795 10.7866 6.3857 9.73E-05 3.49E-03 1.6479 P49815 Tuberin S*SSSPELQTLQDILGDPGDK S; S; S 1385; 1386; 1387 0.313; 0.313; 0.313 1.56E-11 74
USP24 1.3950 11.1223 9.0626 5.31E-06 7.86E-04 4.6057 Q9UPU5 Ubiquitin carboxyl-terminal hydrolase 24 VSDQNS*PVLPK S 2047 1 7.77E-03 92 24,339 33,164 1.36 4.29E-03
WBP4 1.0929 7.4877 4.0214 2.66E-03 2.23E-02 − 1.7675 O75554 WW domain-binding protein 4 NSDGGS*DPETQK S 262 0.996 5.26E-03 90 4151 7080 1.71 4.96E-04
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DEG, differentially expressed gene; LogFC, log2(fold change); LogFC > = 1, upregulated DEG; LogFC <= − 1, downregulated DEGs; T, NFPA; N, control pituitaries; Ratio (T/N), ratio of phosphorylation level of NFPAs to controls

Gene ontology enrichment analysis

The GO enrichment analysis of those identified 130 overlapped molecules (phosphoproteins; invasive DEGs) was annotated with CCs (Supplemental Table 6), MFs (Supplemental Table 7), and BPs (Supplemental Table 8). CC analysis revealed those 130 overlapped molecules (phosphoproteins; invasive DEGs) were mainly distributed in the cytosol, focal adhesion, secretory granule, actin cytoskeleton, cell–cell adherens junction, cytoskeleton, plasma membrane, cell–cell junction, cell cortex, membrane, stress, protein complex, COP9 signalosome, cytoplasm, Golgi apparatus, nuclear speck, SNARE complex, and kinesin complex (Supplemental Table 6). MF analysis revealed that those overlapped molecules (phosphoproteins; invasive DEGs) were significantly involved in cadherin binding involved in cell–cell adhesion, actin filament binding, protein binding, actin binding, structural constituent of cytoskeleton, guanyl-nucleotide exchange factor activity, Rho guanyl-nucleotide exchange factor activity, calmodulin binding, GTPase activator activity, ankyrin binding, structural molecule activity, calcium ion transmembrane transporter activity, spectrin binding, poly(A) RNA binding, microtubule binding, neuropeptide hormone activity, inositol 1,4,5-trisphosphate-sensitive calcium-release channel activity, phosphatidylinositol binding, hormone activity, phosphatidylinositol-3,4,5-trisphosphate binding, and protein kinase activity (Supplemental Table 7). BP analysis revealed multiple statistically significant biological processes, including cell–cell adhesion, regulation of insulin secretion, striated muscle cell differentiation, positive regulation of GTPase activity, neurotransmitter secretion, glucose homeostasis, cellular response to prostaglandin E stimulus, regulation of small GTPase-mediated signal transduction, regulation of Rho protein signal transduction, positive regulation of apoptotic process, renal water homeostasis, glucose transport, positive regulation of endodeoxyribonuclease activity, peptidyl-threonine phosphorylation, transmembrane transport, positive regulation of cAMP biosynthetic process, cellular response to hydroperoxide, signal transduction, platelet degranulation and activation, cellular iron ion homeostasis, and peripheral nervous system myelin maintenance (Supplemental Table 8).

Clustering analysis

The clustering analysis of 130 overlapped molecules (phosphoproteins; invasive DEGs) identified 6 significantly functional categories (Table 5). Cluster 1 was mainly associated with cell–cell adherens and regulation, which was involved in the important overlapped molecules (phosphoproteins; invasive DEGs), including ALDOA, TNKS1BP1, LIMA1, CALD1, ASAP1, EPS15L1, H1FX, NDRG1, PLEC, CHMP2B, and PGM5. Cluster 2 was involved in platelet activation, which was involved in the important overlapped molecules (phosphoproteins; invasive DEGs), including AKT1, TLN2, STIM1, GNAS, SNAP23, PRKG1, ARHGEF12, ITPR1, ITPR2, and ITGA2B. Cluster 3 was mainly associated with GTPase signaling and regulation, which was involved in the important overlapped molecules (phosphoproteins; invasive DEGs), including AKT1, ARHGEF11, ARHGEF11, ARHGEF12, ARHGEF6, ASAP1, ASAP2, BCLAF1, CDC42EP4, DOCK11, DOCK7, GAL, GNAS, NGEF, SPTB, STXBP5L, and TSC2. Cluster 4 was associated with the microtubule and its regulation, which was involved in the overlapped molecules (phosphoproteins; invasive DEGs), including CLASP2, KIF13B, KIF16B, KIF1A, MAP4, NDRG1, and STIM1. Cluster 5 was mainly associated with protein kinase activity and peptidyl-threonine phosphorylation, which was involved in the important overlapped molecules (phosphoproteins; invasive DEGs), including AKT1, AVP, CASK, STK39, PRKCD, CDK14, and DCLK1. Cluster 6 was mainly associated with calcium signaling pathway and regulation, which was involved in the overlapped molecules (phosphoproteins; invasive DEGs), including SLC8A2, STIM1, ITPR1, ITPR2, and GNAS. Thereby, those phosphoproteins participated in the corresponding biological functions in NFPAs. It clearly demonstrated the important roles of phosphorylation in invasive NFPA pathogenesis.

Table 5.

Functional characteristics of 130 overlapped molecules between phosphoproteins and invasive DEGs in NFPAs, clustered with GO and KEGG pathway enrichments. Count means the number of genes enriched in each item

Category Functional characteristics Count % Fold enrichment P value Gene name of overlapped molecule
Annotation cluster 1
  GOTERM_CC_DIRECT GO:0005913~cell–cell adherens junction 11 8.73 5.05 6.35E-05 ALDOA, TNKS1BP1, LIMA1, PGM5, CALD1, ASAP1, EPS15L1, H1FX, NDRG1, PLEC, CHMP2B
  GOTERM_BP_DIRECT GO:0098609~cell–cell adhesion 10 7.94 5.16 1.34E-04 ALDOA, TNKS1BP1, LIMA1, CALD1, ASAP1, EPS15L1, H1FX, NDRG1, PLEC, CHMP2B
  GOTERM_MF_DIRECT GO:0098641~cadherin binding involved in cell–cell adhesion 10 7.94 4.73 2.58E-04 ALDOA, TNKS1BP1, LIMA1, CALD1, ASAP1, EPS15L1, H1FX, NDRG1, PLEC, CHMP2B
Annotation cluster 2
  KEGG_PATHWAY hsa04611:Platelet activation 10 7.94 10.58 2.53E-07 AKT1, TLN2, STIM1, GNAS, SNAP23, PRKG1, ARHGEF12, ITPR1, ITPR2, ITGA2B
Annotation cluster 3
  GOTERM_MF_DIRECT GO:0005085~guanyl-nucleotide exchange factor activity 6 4.76 6.98 1.66E-03 NGEF, ARHGEF6, DOCK7, ARHGEF12, DOCK11, ARHGEF11
  GOTERM_BP_DIRECT GO:0043547~positive regulation of GTPase activity 12 9.52 2.97 2.25E-03 NGEF, ARHGEF6, TSC2, ASAP2, ASAP1, GNAS, ARHGEF12, CDC42EP4, DOCK11, ARHGEF11, STXBP5L, SPTB
  GOTERM_MF_DIRECT GO:0005089~Rho guanyl-nucleotide exchange factor activity 5 3.97 8.91 2.34E-03 NGEF, ARHGEF6, ARHGEF12, DOCK11, ARHGEF11
  GOTERM_MF_DIRECT GO:0005096~GTPase activator activity 8 6.35 3.94 4.17E-03 ARHGEF6, TSC2, ASAP2, ASAP1, ARHGEF12, CDC42EP4, ARHGEF11, STXBP5L
  GOTERM_BP_DIRECT GO:0051056~regulation of small GTPase mediated signal transduction 5 3.97 5.22 1.52E-02 NGEF, ARHGEF6, TSC2, ARHGEF12, ARHGEF11
  GOTERM_BP_DIRECT GO:0035023~regulation of Rho protein signal transduction 4 3.17 6.91 1.98E-02 NGEF, ARHGEF6, ARHGEF12, ARHGEF11
  GOTERM_BP_DIRECT GO:0043065~positive regulation of apoptotic process 7 5.56 3.27 2.00E-02 AKT1, NGEF, BCLAF1, ARHGEF6, ARHGEF12, GAL, ARHGEF11
Annotation cluster 4
  GOTERM_MF_DIRECT GO:0008017~microtubule binding 6 4.76 3.96 1.76E-02 KIF1A, MAP4, NDRG1, CLASP2, KIF16B, KIF13B
  GOTERM_CC_DIRECT GO:0005874~microtubule 7 5.56 3.33 1.83E-02 KIF1A, STIM1, MAP4, NDRG1, CLASP2, KIF16B, KIF13B
  GOTERM_CC_DIRECT GO:0005871~kinesin complex 3 2.38 8.39 4.91E-02 KIF1A, KIF16B, KIF13B
Annotation cluster 5
  GOTERM_BP_DIRECT GO:0018107~peptidyl-threonine phosphorylation 3 2.38 11.05 2.97E-02 AKT1, STK39, PRKCD
  GOTERM_MF_DIRECT GO:0004672~protein kinase activity 7 5.56 2.68 4.61E-02 AKT1, AVP, CASK, STK39, PRKCD, CDK14, DCLK1
Annotation cluster 6
  GOTERM_BP_DIRECT GO:1903779~regulation of cardiac conduction 4 3.17 10.00 7.32E-03 SLC8A2, STIM1, ITPR1, ITPR2
  KEGG_PATHWAY hsa04020:Calcium signaling pathway 5 3.97 3.84 3.79E-02 SLC8A2, STIM1, GNAS, ITPR1, ITPR2
Annotation cluster 7
  KEGG_PATHWAY hsa04611:Platelet activation 10 7.94 10.58 2.53E-07 AKT1, TLN2, STIM1, GNAS, SNAP23, PRKG1, ARHGEF12, ITPR1, ITPR2, ITGA2B
  KEGG_PATHWAY hsa04270:Vascular smooth muscle contraction 8 6.35 9.41 1.64E-05 CALD1, GNAS, PRKG1, ARHGEF12, PRKCD, ITPR1, ARHGEF11, ITPR2
  KEGG_PATHWAY hsa04915:Estrogen signaling pathway 6 4.76 8.34 6.44E-04 AKT1, FKBP5, GNAS, PRKCD, ITPR1, ITPR2
  GOTERM_BP_DIRECT GO:0050796~regulation of insulin secretion 5 3.97 10.44 1.30E-03 STX1A, MARCKS, GNAS, ITPR1, ITPR2
  KEGG_PATHWAY hsa04922:Glucagon signaling pathway 5 3.97 6.95 5.21E-03 AKT1, ACACA, GNAS, ITPR1, ITPR2
  KEGG_PATHWAY hsa04730:Long-term depression 4 3.17 9.17 8.74E-03 GNAS, PRKG1, ITPR1, ITPR2
  KEGG_PATHWAY hsa05205:Proteoglycans in cancer 6 4.76 4.13 1.34E-02 AKT1, ANK1, ARHGEF12, ITPR1, PXN, ITPR2
  KEGG_PATHWAY hsa04970:Salivary secretion 4 3.17 6.40 2.30E-02 GNAS, PRKG1, ITPR1, ITPR2
  KEGG_PATHWAY hsa04540:Gap junction 4 3.17 6.25 2.44E-02 GNAS, PRKG1, ITPR1, ITPR2
  KEGG_PATHWAY hsa04022:cGMP–PKG signaling pathway 5 3.97 4.35 2.55E-02 AKT1, SLC8A2, PRKG1, ITPR1, ITPR2
  KEGG_PATHWAY hsa04912:GnRH signaling pathway 4 3.17 6.05 2.66E-02 GNAS, PRKCD, ITPR1, ITPR2
  KEGG_PATHWAY hsa04750:Inflammatory mediator regulation of TRP channels 4 3.17 5.62 3.22E-02 GNAS, PRKCD, ITPR1, ITPR2
  KEGG_PATHWAY hsa04020:Calcium signaling pathway 5 3.97 3.84 3.79E-02 SLC8A2, STIM1, GNAS, ITPR1, ITPR2
  GOTERM_BP_DIRECT GO:0030168~platelet activation 4 3.17 4.87 4.84E-02 AKT1, PRKCD, ITPR1, ITPR2
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Signaling pathways involved in overlapped molecules (phosphoproteins; invasive DEGs)

The KEGG pathway enrichment analysis of 130 overlapped molecules (phosphoproteins; invasive DEGs) identified 14 statistically significant signaling pathways (Table 6; Supplemental Fig. 1) that were related to tumor invasiveness and involved in protein phosphorylations (Table 4), including (i) platelet activation involved in phosphorylation at residues Ser122 and Ser124 in AKT1; Thr1843 (ratio of T/N = 1.16, P = 1.17E-02) in TLN2; Ser257 in STIM1; Ser995 (ratio of T/N = 2.77, P = 2.32E-05) in GNAS; Ser110 (ratio of T/N = 2.13, P = 6.07E-04) in SNAP23; Thr515 in PRKG1; Ser309 (ratio of T/N = 2.94, P = 9.07E-04) in ARHGEF12; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2; and Ser880 in ITGA2B (Table 6; Supplemental Fig. 1.1); (ii) vascular smooth muscle contraction involved in phosphorylation at residues Ser207 (ratio of T/N = 3.30, P = 2.31E-06) in CALD1; Ser995 (ratio of T/N = 2.77, P = 2.32E-05) in GNAS; Thr515 in PRKG1; Ser309 (ratio of T/N = 2.94, P = 9.07E-04) in ARHGEF12; Tyr514 in PRKCD; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1;, Ser663 (ratio of T/N = 2.32, P = 4.24E-04); Thr668 (ratio of T/N = 2.32, P = 4.24E-04); Ser1295 (ratio of T/N = 1.45, P = 1.11E-03); Ser115 (ratio of T/N = 1.81, P = 1.02E-02); Ser1478 and Ser1480 in ARHGEF11; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.2); (iii) estrogen signaling pathway involved in phosphorylation at residues Ser122 and Ser124 in AKT1; Ser13 in FKBP5; Ser995 (ratio of T/N = 2.77, P = 2.32E-05) in GNAS; Tyr514 in PRKCD; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.3); (iv) Fc gamma R-mediated phagocytosis involved in phosphorylation at residues Ser122 and Ser124 in AKT1; Ser701 (ratio of T/N = 1.57, P = 2.02E-03) in ASAP2; Ser717 (ratio of T/N = 2.04, P = 1.35E-04) in ASAP1; Ser27 (ratio of T/N = 8.66, P = 3.86E-06); Ser26 (ratio of T/N = 5.66, P = 8.79E-06), Thr150 (ratio of T/N = 3.88, P = 1.52E-05), Ser145 (ratio of T/N = 1.62, P = 8.15E-03), Ser147, Ser118, Thr120, Ser128, Thr130, Ser138, and Ser101 in MARCKS; and Tyr514 in PRKCD (Table 6; Supplemental Fig. 1.4); (v) glucagon signaling pathway involved in phosphorylation at residues Ser122 and Ser124 in AKT1; Ser29 (ratio of T/N = 2.98, P = 6.08E-05) in ACACA; Ser995 (Ratio of T/N = 2.77, P = 2.32E-05) in GNAS; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.5); (vi) proteoglycans in cancer involved in phosphorylation at residues Ser122 and Ser124 in AKT1; Ser781 (ratio of T/N = 3.58, P = 4.13E-06), Ser1428 (ratio of T/N = 1.72, P = 1.74E-05), Thr961 (ratio of T/N = 1.61, P = 4.31E-04), Ser960 (ratio of T/N = 1.70, P = 5.29E-04), Thr961 (ratio of T/N = 1.70, P = 5.29E-04), Ser1607 (ratio of T/N = 0.76, P = 3.19E-03), Thr1684, Ser1686, Ser1686, and Thr1688 in ANK1; Ser309 (ratio of T/N = 2.94, P = 9.07E-04) in ARHGEF12; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; Ser302 (ratio of T/N = 1.43, P = 2.16E-02), Ser303 (ratio of T/N = 1.43, P = 2.16E-02), Ser322 (ratio of T/N = 1.88, P = 4.60E-02), Thr318, and Ser320 in PXN; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.7); (vii) insulin signaling pathway involved in phosphorylation in residues Ser122 and Ser124 in AKT1; Ser114 (ratio of T/N = 0.7, P = 6.75E-04) in PRKAR2B; Thr486 and Ser488 in SLC2A4; Ser1385, Ser1386, and Ser1387 in TSC2; and Ser29 (ratio of T/N = 2.98, P = 6.08E-05) in ACACA (Table 6; Supplemental Fig. 1.8); (viii) gap junction involved in phosphorylation at residues Ser995 (ratio of T/N = 2.77, P = 2.32E-05) in GNAS; Thr515 in PRKG1; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.10); (ix) cGMP–PKG signaling pathway involved in phosphorylation at residues Ser122 and Ser124 in AKT1; Ser622 (ratio of T/N = 1.60, P = 1.40E-01) in SLC8A2; Thr515 in PRKG1; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.11); (x) GnRH signaling pathway involved in phosphorylation at residues Ser995 (ratio of T/N = 2.77, P = 2.32E-05) in GNAS; Tyr514 in PRKCD; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.12); (xi) inflammatory mediator regulation of TRP channels involved in phosphorylation at residues Ser995 (ratio of T/N = 2.77, P = 2.32E-05) in GNAS; Tyr514 in PRKCD; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.13); and (xii) calcium signaling pathway involved in phosphorylation at residues Ser622 (ratio of T/N = 1.60, P = 1.40E-01) in SLC8A2; Ser257 in STIM1; Ser995 (ratio of T/N = 2.77, P = 2.32E-05) in GNAS; Ser1177, Thr1178, Ser1180, and Tyr1181 in ITPR1; and Ser1687 (ratio of T/N = 0.67, P = 2.20E-03) in ITPR2 (Table 6; Supplemental Fig. 1.14). These findings clearly demonstrate that phosphorylation participates in invasiveness-related signaling pathways in invasive NFPAs.

Table 6.

Statistically significant KEGG signaling pathways identified from 130 overlapped molecules between phosphoproteins and invasive DEGs in NFPAs. Count means the number of genes enriched in each pathway

Category Pathway name Count % Fold enrichment P value Benjamini Gene name of overlapped molecule
KEGG_PATHWAY hsa04611:Platelet activation 10 7.94 10.58 2.53E-07 3.61E-05 AKT1, TLN2, STIM1, GNAS, SNAP23, PRKG1, ARHGEF12, ITPR1, ITPR2, ITGA2B
KEGG_PATHWAY hsa04270:Vascular smooth muscle contraction 8 6.35 9.41 1.64E-05 1.17E-03 CALD1, GNAS, PRKG1, ARHGEF12, PRKCD, ITPR1, ARHGEF11, ITPR2
KEGG_PATHWAY hsa04915:Estrogen signaling pathway 6 4.76 8.34 6.44E-04 3.02E-02 AKT1, FKBP5, GNAS, PRKCD, ITPR1, ITPR2
KEGG_PATHWAY hsa04666:Fc gamma R-mediated phagocytosis 5 3.97 8.19 2.89E-03 9.82E-02 AKT1, ASAP2, ASAP1, MARCKS, PRKCD
KEGG_PATHWAY hsa04922:Glucagon signaling pathway 5 3.97 6.95 5.21E-03 1.39E-01 AKT1, ACACA, GNAS, ITPR1, ITPR2
KEGG_PATHWAY hsa04730:Long-term depression 4 3.17 9.17 8.74E-03 1.89E-01 GNAS, PRKG1, ITPR1, ITPR2
KEGG_PATHWAY hsa05205:Proteoglycans in cancer 6 4.76 4.13 1.34E-02 2.40E-01 AKT1, ANK1, ARHGEF12, ITPR1, PXN, ITPR2
KEGG_PATHWAY hsa04910:Insulin signaling pathway 5 3.97 4.98 1.64E-02 2.55E-01 AKT1, PRKAR2B, SLC2A4, TSC2, ACACA
KEGG_PATHWAY hsa04970:Salivary secretion 4 3.17 6.40 2.30E-02 3.09E-01 GNAS, PRKG1, ITPR1, ITPR2
KEGG_PATHWAY hsa04540:Gap junction 4 3.17 6.25 2.44E-02 2.98E-01 GNAS, PRKG1, ITPR1, ITPR2
KEGG_PATHWAY hsa04022:cGMP-PKG signaling pathway 5 3.97 4.35 2.55E-02 2.85E-01 AKT1, SLC8A2, PRKG1, ITPR1, ITPR2
KEGG_PATHWAY hsa04912:GnRH signaling pathway 4 3.17 6.05 2.66E-02 2.75E-01 GNAS, PRKCD, ITPR1, ITPR2
KEGG_PATHWAY hsa04750:Inflammatory mediator regulation of TRP channels 4 3.17 5.62 3.22E-02 3.02E-01 GNAS, PRKCD, ITPR1, ITPR2
KEGG_PATHWAY hsa04020:Calcium signaling pathway 5 3.97 3.84 3.79E-02 3.26E-01 SLC8A2, STIM1, GNAS, ITPR1, ITPR2
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Hub molecules identified with protein–protein interaction analysis of 130 overlapped molecules (phosphoproteins; invasive DEGs)

The PPI network was constructed using molecules with combined scores greater than 0.4, based on 130 overlapped molecules (phosphoproteins; invasive DEGs) (Fig. 3a; Supplemental Table 9). Some overlapped molecules (phosphoproteins; invasive DEGs) in PPI networks trend to be co-expressed or co-localized with high combined score, the difference might be related to functional differences between the overlapped molecules, and various co-expression and co-localization might be reflected in the molecular characteristics or structures of two molecules [31], which help to understand the function and biological mechanisms of NFPA invasiveness. Among 130 overlapped molecules (phosphoproteins; invasive DEGs), some between molecules had very high combined score; for example, SNAP23 and STX1A, AKT1 and TSC2, CHD3 and GATAD2A, SPTB and ANK1, ALDOA and TPI1, POMC and AVP, STIM1 and ITPR1, SCG3 and CHGA, DMTN and ADD2, CHGB and SCG3, ALDOA and PKM, STIM1 and ITPR2, PKM and TPI1, POMC and GAL, SYN1 and STX1A, DMTN and ADD3, and HNRNPA1 and SRRM2 had the combined score over 0.95 (Supplemental Table 9).

Fig. 3.

Fig. 3

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The hub molecules were identified with protein–protein interaction (PPI)-based molecular complex detection (MCODE) using 130 overlapped molecules (phosphoproteins; invasive DEPs) in NFPAs. a The protein–protein interactions (PPIs) with combined scores greater than 0.4 were selected to construct PPI network. b The entire PPI network was analyzed using molecular complex detection (MCODE), and one module was obtained with module score = 5.778. The different colored nodes represent different query proteins and first shell of interactors. The white nodes represent second shell of interactors. The empty nodes represent the proteins with unknown 3D structure. The filled nodes represent the proteins with known or predicted 3D structure

The hub molecules based on the PPI network play important roles in a molecular network system, which also reflects its crucial roles in a biological system such as NFPA invasive behavior system. The entire PPI network was analyzed using MCODE, and one module was obtained with module score = 5.778 (Fig. 3b). Thus, a total of 10 hub molecules were identified in invasive NFPAs (Table 7), including SCG3 (secretogranin-3), TSC2 (tuberin), ALB (serum albumin), AKT1 (RAC-alpha serine/threonine-protein kinase), APOL1 (apolipoprotein L1), ACACA (acetyl-CoA carboxylase 1), SPARCL1 (SPARC-like protein 1), SLC2A4 (solute carrier family 2 facilitated glucose transporter member 4), CHGB (secretogranin-1), and IGFBP5 (insulin-like growth factor-binding protein 5). These hub molecules not only had the differential expression at the mRNA level but also had the changed level of phosphorylation. These hub molecules assisted in improving the understanding of the key molecular mechanisms underlying NFPA invasiveness, and the results may help the further study of the biological mechanism of NFPA invasive behavior. It also clearly demonstrated that phosphorylation of these hub molecules played important roles in NFPA invasive behavior; for example, phosphorylation occurred at residues Ser37 (ratio T/N = 0.46, P = 1.5E-04) in SCG3; Ser1385, Ser1386, and Ser1387 in TSC2; Ser82 (ratio T/N = 0.74, P = 3.24E-04) in ALB; Ser122 and Ser124 in AKT1; Ser352 (ratio T/N = 0.71, P = 5.33E-03) and Ser355 (ratio T/N = 0.71, P = 5.33E-03) in APOL1; Ser29 (ratio of T/N = 2.98, P = 6.08E-05) in ACACA; Ser76, Ser80 (ratio T/N = 0.24, P = 1.86E-05), Ser84 (ratio T/N = 0.35, P = 5.38E-05), Ser92 (ratio T/N = 0.28, P = 1.91E-05), Ser198 (ratio T/N = 0.89, P = 1.92E-01), Ser231, Thr238, Ser240, and Ser295 (ratio T/N = 0.57, P = 1.15E-053) in SPARCL1; Thr486 and Ser488 in SLC2A4; and Thr123 in IGFBP5; and CHGB was phosphorylated at 50 residues (41 Ser, 5 Thr, and 4 Tyr residues) (Table 7). The hub molecules (phosphoproteins; invasive DEGs) SLC2A4 and TSC2 were involved in insulin signaling pathway, ACACA was involved in insulin signaling pathway and glucagon signaling pathway, and AKT1 was involved in insulin signaling pathway, glucagon signaling pathway, cGMP–PKG signaling pathway, proteoglycans in cancer, Fc gamma R-mediated phagocytosis, estrogen signaling pathway, and platelet activation pathway (Table 6). It clearly demonstrated that one signaling pathway included multiple hub molecules, and one hub molecule was involved in multiple signaling pathways, to display a real signaling pathway network system in NFPA invasive behavior. Those hub molecules played the crucial roles in the signaling pathway network system in the invasive behavior of NFPAs, which might be the effective panel biomarker and multitherapeutic targets for NFPA invasiveness.

Table 7.

A total of 10 hub molecules in invasive NFPAs identified with PPI analysis of 130 overlapped molecules (invasive DEG; phosphoproteins)

Protein accession ID DEG name DEG level Protein name Phosphorylation level
logFC AveExpr t P value Phosphopeptide Phosphorylated site Phosphorylated position Phosphorylated probabilities PEP Score Ratio (T/N) P value (t test)
Q8WXD2 SCG3 1.2843 10.4326 4.3052 1.72E-03 Secretogranin-3 ELS*AERPLNEQIAEAEEDKIK S 37 1 2.80E-38 149 0.46 1.50E-04
LFPAPS*EKSHEETDSTK S 359 0.468 1.85E-02 36
P49815 TSC2 2.1795 10.7866 6.3857 9.73E-05 Tuberin S*SSSPELQTLQDILGDPGDK S; S; S 1385; 1386; 1387 0.313; 0.313; 0.313 1.56E-11 74
P02768-1 ALB 1.9536 6.1289 4.5055 1.27E-03 Serum albumin TCVADES*AENCDK S 82 1 1.24E-47 197 0.74 3.24E-04
P31749 AKT1 1.1801 6.8509 5.4900 3.11E-04 RAC-alpha serine/threonine-protein kinase S*GSPSDNSGAEEMEVSLAKPK S; S 122; 124 0.457; 0.457 1.16E-07 92
A5PL32 APOL1 − 1.1936 9.8189 − 3.2480 9.26E-03 Apolipoprotein L1 VTEPIS*AES*GEQVER S; S 352; 355 1; 1 2.53E-03 59 0.71 5.33E-03
Q13085 ACACA 1.1822 6.8754 3.3664 7.62E-03 Acetyl-CoA carboxylase 1 FIIGSVSEDNS*EDEISNLVK S 29 1 3.35E-32 154 2.98 6.08E-05
Q14515 SPARCL1 − 3.1871 13.6134 − 4.7627 8.66E-04 SPARC-like protein 1 EES*HEQS*AEQGK S 80 9.37E-38 176 0.24 1.86E-05
SSS*QELGLK S 92 0.999 1.61E-28 188 0.28 1.91E-05
SKEES*HEQS*AEQGK S; S 80; 84 0.999; 1 9.37E-38 176 0.35 5.38E-05
EHANS*KQEEDNTQSDDILEESDQPTQVSK S 231 1 5.61E-70 176 0.36 5.25E-04
HIQETEWQS*QEGK S 295 1 2.83E-21 152 0.57 1.15E-03
DQGNQEQDPNIS*NGEEEEEKEPGEVGTHNDNQER S 198 0.997 1.10E-05 53 0.89 1.92E-01
EHANS*KQEEDNT*QS*DDILEESDQPTQVSK S; T; S 231; 238; 240 0.331; 0.331; 0.331 1.44E-02 38
S*KEES*HEQSAEQGK S; S 76; 80 0.999; 0.995 7.77E-05 77
P14672 SLC2A4 − 1.3065 5.1362 − 4.4148 1.45E-03 Solute carrier family 2, facilitated glucose transporter member 4 T*PSLLEQEVKPSTELEYLGPDEND T; S 486; 488 0.5; 0.5 1.85E-10 91
P05060 CHGB − 1.3288 14.5253 − 3.2997 8.51E-03 Secretogranin-1 SETHAAGHS*QEK S 225 1 4.87E-30 175 0.14 6.87E-07
GRGS*EEYR S 367 1 1.70E-07 146 0.12 7.46E-07
AS*EEEPEYGEEIK S 335 1 5.81E-71 223 0.13 2.05E-06
EKSS*QES*GEETGS*QENHPQESK S; S; S 236; 239; 245 0.531; 1; 0.965 4.20E-73 205 0.06 3.67E-06
ADEPQWSLYPSDS*QVS*EEVK S; S 146; 149 0.999; 0.999 1.30E-150 236 0.31 5.28E-06
SSQESGEET*GSQENHPQESK T 243 0.978 1.47E-45 161 0.02 5.36E-06
GERGEDS*S*EEK S; S 182; 183 1; 1 1.80E-03 100 0.16 7.03E-06
AS*EEEPEY*GEEIK S; Y 335; 341 1; 1 2.35E-06 98 0.18 8.29E-06
SSQES*GEETGS*QENHPQESK S; S 239; 245 0.992; 1 4.20E-73 205 0.06 1.01E-05
S*QREDEEEEEGENYQK S 160 1 1.77E-17 140 0.25 1.17E-05
SAEFPDFYDS*EEPVSTHQEAENEKDR S 626 1 1.85E-99 214 0.26 1.18E-05
S*QEES*EEGEEDATS*EVDK S 259; 263; 272 1; 1; 0.857 1.51E-53 157 0.14 1.23E-05
SSQGGS*LPSEEK S 298 0.981 3.28E-14 142 0.28 1.31E-05
DPADASEAHES*SSR S 98 0.966 1.32E-30 169 0.10 1.41E-05
NYPS*LELDK S 391 1 1.30E-04 112 0.14 1.44E-05
ADEPQWSLYPS*DS*QVSEEVK S; S 144; 146 0.941; 0.751 3.58E-98 136 0.52 1.53E-05
EKSS*QES*GEET*GSQENHPQESK S; S; T 237; 239; 243 0.819; 0.935; 0.598 1.47E-45 161 0.05 1.53E-05
SSQGGSLPS*EEK S 301 0.999 5.51E-20 128 0.25 1.88E-05
S*AEFPDFYDSEEPVSTHQEAENEKDR S 617 1 8.30E-152 246 0.22 2.13E-05
S*AEFPDFY*DSEEPVSTHQEAENEKDR Y 624 0.995 8.86E-43 152 0.23 2.23E-05
SQEESEEGEEDAT*SEVDK T 271 0.973 1.38E-88 219 0.21 2.23E-05
M#AHGY*GEES*EEER Y; S 401; 405 1; 1 2.41E-02 51 0.23 2.34E-05
ERADEPQWSLYPSDSQVS*EEVK S 149 1 2.83E-147 264 0.29 2.80E-05
PQS*EES*WDEEDKR S; S 377; 380 1; 1 4.73E-42 181 0.15 3.27E-05
SAEFPDFYDS*EEPVS*THQEAENEK S; S 626; 631 0.996; 0.863 3.45E-35 140 0.30 3.33E-05
SS*QGGSLPSEEK S 294 0.807 3.74E-09 133 0.24 3.77E-05
S*SQGGSLPSEEK S 293 0.992 1.97E-30 180 0.25 4.48E-05
GHPQEES*EESNVSMASLGEK S 311 1 6.58E-209 232 0.22 5.91E-05
SAEFPDFYDSEEPVS*T*HQEAENEK S; T 631; 632 0.591; 0.586 3.45E-35 124 0.31 5.94E-05
EKS*S*QES*GEET*GS*QENHPQESK S; S; S; T; S 235; 236; 239; 243; 245 0.643; 0.589; 0.589; 0.589; 0.589 1.47E-45 161 0.05 7.50E-05
WQQQGDLQDT*KENR T 492 1 6.83E-05 85 0.37 9.99E-05
GHPQEES*EESNVS*M#ASLGEK S 317 0.988 1.45E-59 184 0.29 1.70E-04
ADEPQWSLY*PSDSQVS*EEVK Y; S 142; 149 0.981; 0.96 6.42E-108 134 0.52 1.87E-04
GHPQEES*EESNVS*MAS*LGEK S; S; S 314; 320; 323 0.739; 1; 1 1.83E-50 161 0.37 4.40E-04
WAEGGGHS*R S 130 1 4.86E-03 80 0.21 5.96E-04
GHPQEES*EES*NVS*MASLGEK S; S; S 314; 317; 320 0.959; 0.959; 0.959 8.63E-27 145 0.31 6.63E-04
CIIEVLSNALS*K S 46 1 3.06E-04 94 0.40 1.63E-02
DHHS*THYRASEEEPEY*GEEIK S; T; Y 329; 330; 341 0.355; 0.355; 0.98 1.56E-02 43
DPADASEAHESS*S*RGEAGAPGEEDIQGPTK S; S 99; 100 0.651; 0.651 8.57E-07 62
LLRDPADAS*EAHESSSR S 93 0.989 5.88E-05 88
S*ETHAAGHS*QEK S 217 0.757 1.02E-02 78
P24593 IGFBP5 − 2.6828 10.6639 − 4.1430 2.20E-03 Insulin-like growth factor-binding protein 5 EHEEPT*TSEMAEETYSPK T 123 0.346 2.19E-04 56
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DEG, differentially expressed gene; LogFC, log2(fold change). LogFC > = 1, upregulated DEG; LogFC <= − 1, downregulated DEGs; Ratio (T/N), the ratio of phosphorylation level of NFPAs to controls

Discussion

Phosphorylation is one of the important protein PTMs (ranging from 400 to 600 PTMs in humans [16]) that cause the diversity of proteins, namely proteoforms or protein species [17]. Proteoform or protein species is defined as a primary amino acid sequence + PTMs + spatial conformation + binding partners + cofactors + localization + a defined function [17]. The number of proteoforms (at least 1,000,000 proteoforms in humans) is much more than the number of transcripts (at least 100,000 transcripts in humans) and genes (about 20,300 genes in humans) [32, 33]. Proteoform is the basic unit in a proteome and is the final performer of gene functions. In-depth investigation of proteoforms in human proteome will directly benefit the discovery of reliable biomarkers for accurate molecular mechanisms, therapeutic targets, personalized medicine, or precision medicine in human pituitary adenomas [32]. Therefore, proteoforms have important scientific merit in the entire medical science and life science [34, 35]. The studies on PTMs directly benefit the characterization of proteoforms. This study focused on phosphoproteomic profiling in human NFPA tissues, which belongs to our long-term research program in proteomics, PTMs, and proteoforms in human pituitary adenomas. TMT labeling–TiO2 enrichment–LC-MS/MS was an effective quantitative proteomics approach [36] and identified a total of 2982 phosphorylation sites within 1035 phosphoproteins, with quantitative information for 1660 (1660/2982 = 55.67%) phosphorylation sites and qualitative information for 1322 (1322/2982 = 44.33%) phosphorylation sites, in NFPA tissues relative to controls. It is the first large-scale phosphoproteomic profiling of NFPAs with quantitative information, and those identified phosphoproteins participated in multiple biological process and signaling pathways, including cell–cell adherens junction, cadherin binding involved in cell–cell adhesion, cell–cell adhesion, SNARE interactions in vesicular transport, SNAP receptor activity, SNARE complex, histone H2B ubiquitination, histone monoubiquitination, endodermal cell fate commitment, Cdc73/Paf1 complex, myosin II complex and filament, oxygen transporter activity, oxygen transport, haptoglobin binding, haptoglobin–hemoglobin complex, hemoglobin complex, cAMP-dependent protein kinase complex, protein kinase A catalytic subunit binding, cAMP-dependent protein kinase regulator activity, ESCRT III complex disassembly, positive regulation of viral release from host cell, and eukaryotic 43S preinitiation complex. Also, 31 statistically significant signaling pathways were mined with KEGG pathway analysis from 1035 phosphoproteins in NFPAs, including platelet activation, mRNA surveillance pathway, RNA transport, spliceosome, endocytosis, proteoglycans in cancer, mTOR signaling pathway, insulin signaling pathway, MAPK signaling pathway, SNARE interactions in vesicular transport, gap junction, focal adhesion, glucagon signaling pathway, estrogen signaling endoplasmic reticulum, and GnRH signaling pathway. Those data provided the largest phosphorylation profiling and phosphorylation-mediated signaling pathway systems, which are the precious resource to deeply investigate biological functions of protein phosphorylations in NFPAs, and identify the reliable and effective new biomarkers for patient stratification, prognostic/predictive assessment, and personalized treatment of NFPAs.

Tumor invasiveness is a very challenging clinical problem in NFPAs, which causes the different therapeutic strategies after neurosurgery [37–39]. Investigation of tumor invasiveness in NFPAs is always very interesting and meaningful. Some researchers have focused on several invasiveness-related molecules including pituitary tumor-transforming gene (PTTG), vascular endothelial growth factor (VEGF), hypoxia inducible factor-1a (HIF-1a), fibroblast growth factor-2 (FGF-2), and matrix metalloproteinases (MMPs) such as MMP-2 and MMP-9, and their interacted complex molecular network in human pituitary adenoma [4, 38, 40]. However, the world of molecules of invasive NFPAs is very complex; these molecules [4, 38, 40] are only the partial molecules, which cannot represent the entire molecular world of NFPA invasive behavior. Multiomics-based molecular pathway networks [4, 15, 30] are an effective strategy and approach to study NFPA invasiveness, and annotate the interactome in invasive NFPA, and also are the real way to resolve its clinical invasiveness challenge, for in-depth clarification of its molecular mechanism of NFPA invasiveness and discovery of reliable invasiveness-related biomarkers for diagnosis, prognostic/predictive assessment, and therapeutic targets for personalized therapy [6, 7, 16, 41, 42]. This study used transcriptomics data between invasive NFPAs and control pituitaries, from the public GEO database, which was integrated with the identified phosphoprotein data. An overlapped analysis between 1035 phosphoproteins and 2751 DEGs in invasive NFPAs relative to controls revealed 130 overlapped molecules (phosphoproteins; invasive DEGs). Those 130 overlapped molecules were involved in multiple biological processes, including cell–cell adherens and regulation, platelet activation, GTPase signaling and regulation, microtubule and its regulation, protein kinase activity and peptidyl-threonine phosphorylation, and calcium signaling pathway and regulation. Thereby, those phosphoproteins participated in the corresponding biological functions in NFPA invasive behaviors. We have clearly demonstrated the important roles of phosphorylation in invasive NFPA pathogenesis.

Furthermore, based on 130 overlapped molecules (phosphoproteins; invasive DEGs), 12 statistically significant signaling pathways (Table 6; Supplemental Fig. 1) were identified to associate with tumor invasiveness, and the identified phosphoproteins were involved in these signaling pathways. For example, the platelet activation pathway included phosphoproteins (invasive DEGs) AKT1, TLN2, STIM1, GNAS, SNAP23, PRKG1, ARHGEF12, ITPR1, ITPR2, and ITGA2B; the vascular smooth muscle contraction pathway included phosphoproteins (invasive DEGs) CALD1, GNAS, PRKG1, ARHGEF12, PRKCD, ITPR1, ARHGEF11, and ITPR2; the estrogen signaling pathway included phosphoproteins (invasive DEGs) AKT1, FKBP5, GNAS, PRKCD, ITPR1, and ITPR2; Fc gamma R-mediated phagocytosis included phosphoproteins (invasive DEGs) AKT1, ASAP2, ASAP1, MARCKS, and PRKCD; the glucagon signaling pathway included phosphoproteins (invasive DEGs) AKT1, ACACA, GNAS, ITPR1, and ITPR2; proteoglycans in cancer included phosphoproteins (invasive DEGs) AKT1, ANK1, ARHGEF12, ITPR1, PXN, and ITPR2; the insulin signaling pathway included phosphoproteins (invasive DEGs) AKT1, PRKAR2B, SLC2A4, TSC2, and ACACA; the gap junction pathway included phosphoproteins (invasive DEGs) GNAS, PRKG1, ITPR1, and ITPR2; the cGMP–PKG signaling pathway included phosphoproteins (invasive DEGs) AKT1, SLC8A2, PRKG1, ITPR1, and ITPR2; the GnRH signaling pathway included phosphoproteins (invasive DEGs) GNAS, PRKCD, ITPR1, and ITPR2; inflammatory mediator regulation of TRP channels included phosphoproteins (invasive DEGs) GNAS, PRKCD, ITPR1, and ITPR2; and the calcium signaling pathway included phosphoproteins (invasive DEGs) SLC8A2, STIM1, GNAS, ITPR1, and ITPR2. These KEGG signaling pathways formed the signaling pathway profiling of NFPA invasive behavior and mutually interacted through hub molecules. Interestingly, PPI network-based MCODE analysis identified 10 hub molecules (phosphoproteins; DEGs) in invasive NFPAs (Table 7; Fig. 3b), namely SLC2A4, TSC2, AKT1, SCG3, ALB, APOL1, ACACA, SPARCL1, CHGB, and IGFBP5. Comparative analysis of these hub molecules with the nodes of signaling pathways (Table 6; Supplemental Fig. 1) found that one signaling pathway might include multiple hub molecules, and one hub molecule might be involved in multiple signaling pathways [43]; for example, the insulin signaling pathway included hub molecules SLC2A4, TSC2, and AKT1; and AKT1 was involved in insulin signaling pathway, cGMP–PKG signaling pathway, glucagon signaling pathway, estrogen signaling pathway, platelet activation pathway, Fc gamma R-mediated phagocytosis, and proteoglycans in cancer. These signaling pathways and hub molecules formed the real signaling pathway network system for NFPA invasive behavior, which is offering great promise to clarify the molecular mechanisms of NFPA invasive behavior; determine the effective panel of biomarkers [11] for patient stratification, prognostic/predictive assessment, and personalized treatment of invasive NFPAs; and discover multitherapeutic targets for personalized therapy for patients suffering of invasive NFPAs [44–46].

Currently, the diagnosis of NFPA invasive behaviors mainly relies on tumor morphological changes observed by neurosurgery and nuclear magnetic resonance (NMR) image changes at its middle or late stages, and it is very difficult to determine its invasiveness at its early stage [9, 10]. Even at its middle or late stages, NMR image and morphological change-based diagnosis of invasive behavior of NFPAs are also not fully accurate. In this study, the identified invasiveness-related, phosphorylation-mediated signaling pathways and pathway network-based hub molecules (phosphoproteins; invasive DEGs) have the potential for establishing panel of biomarkers allowing early prediction of invasive behavior and focusing on therapeutic targets to prevent its progression and accurate prognostic/predictive assessment of invasive behavior after neurosurgery, in order to personalize the treatment of invasive NFPA patients. Here, 10 pathway–network-based hub molecules (phosphoproteins; invasive DEGs) were taken for further discussion in the context of PPPM in invasive NFPAs. AKT1 is a crucial component in the PI3K–Akt signaling pathway that is required for VEGF-A mRNA expression in GH3 cells induced by 17alpha-estradiol [47], and the anticancer drug precursor methylseleninic acid (MSeA) can decrease AKT phosphorylation to inhibit the growth and survival of human umbilical vein endothelial cells [48], and AKT1-mutant estrogen receptor (ER)-positive metastatic breast cancer has a longer survival duration on mTOR inhibitor therapy [49]. Thus, AKT is an important therapeutic target potentially capable of improving the treatment results of solid tumors [50]. ACACA is acetyl-CoA carboxylase 1 that catalyzes the ATP-dependent carboxylation of acetyl-CoA, a rate-limiting step in fatty acid biosynthesis [51]. A study found that ACACA is a potent therapeutic target for anticancer therapy [52]. Also, phospho-ACACA protein is an independent prognostic/predictive factor for human gastric cancer without lymph node metastasis [53]. TSC2, tuberin, is a well-known suppressor of the mTOR pathway [54]. SPARCL1 can suppress cancer metastasis and recruit macrophases by activation of canonical WNT/β-catenin signaling [55], or it can suppress cancer cell proliferation and migration via the MEK/ERK signaling [56]. SLC2A4, namely solute carrier family 2 facilitated glucose transporter member 4, encodes glucose transporter 4 protein (GLUT4), which has been identified as a promising therapeutic target for cancer, because the putative antimicrobial peptides (AMPs) can serve as a therapeutic drug in treating cancer by inhibiting SLC2A4 that is responsible for the production of energy for cancer cells during angiogenesis [57]. IGFBP5 is the insulin-like growth factor binding protein 5, which acts as a tumor suppressor, and is often dysregulated in human cancers to associate with tumorigenicity and metastasis; thus, IGFBP5 might be a novel therapeutic target for human melanoma [58, 59]. Furthermore, clinical findings demonstrate the potential of IGFBP5 as an effective biomarker predicting the response to therapy and clinical outcome of cancer patients [60]. SCG3 is secretogranin-3 [61, 62], CHGB is secretogranin-1 [63, 64], ALB is serum albumin [65, 66], and APOL1 is apolipoprotein L1 [67–69]; these molecules were found to be phosphorylated in this study, and they can be secreted into body fluid thus having the potential for the early prediction and prognostic assessment of invasive behavior of NFPAs from the molecular view of point.

Thereby, PTMs are the main factor to result in proteoforms—the final functional forms of a gene/protein. In the context of proteoforms, we focused on an important PTM—phosphorylation that is involved in many biological processes and cell signaling transductions, in NFPA pathogenesis. Further, we integrated the phosphoproteomics and transcriptomics data in invasive NFPAs to obtain phosphorylation-mediated molecular events in invasive NFPAs, which is the precious resource to (i) benefit the understanding of molecular mechanisms of NFPA invasive behavior, (ii) discover effective therapeutic targets to prevent the occurrence and progression of invasive NFPAs, and (iii) develop effective phosphorylation-related biomarkers for prognostic/predictive assessment to stratify NFPA patients into invasive and noninvasive groups who will undergo personalized therapeutic approaches after neurosurgery. This study may serve as an example of effective contribution to the paradigm shift from experimental medicine to PPPM in pituitary adenomas.

Strength and limitation

This study provided the first large-scale phosphoproteomic profiling with 2982 phosphorylation sites within 1035 phosphoproteins in NFPAs and the corresponding phosphorylation-mediated functional characteristics and signaling pathway networks. Moreover, 1035 phosphoproteins were integrated with transcriptomics data (2751 DEGs) in invasive NFPAs relative to controls, which obtained 130 overlapped molecules (phosphoproteins; invasive DEGs), followed by pathway network analysis to obtain 12 statistically significant signaling pathways and 10 hub molecules associated with NFPA invasive behaviors. These findings offer an increasing promise for insights into the molecular mechanisms, determination of effective therapeutic targets, and discovery of effective pathway network-based panel of biomarkers for patient stratification, prognostic/predictive assessment, and personalized treatment of NFPA patients. However, one must realize that those phosphoproteomic data (4 NFPAs vs. 4 controls) and transcriptomics (4 invasive NFPAs vs. 3 controls from GEO database) were derived from a limited sample size. In order to transit those findings into routine practice, a significantly expanded sample size will be needed to further validate and study in detail the molecular mechanisms, functional roles, and potentially therapeutic targets of these identified phosphorylation-involved signaling pathways and hub molecules in NFPA patients. One should also try their best to address the signaling pathway alterations at the proteoform level for real evidence-based PPPM in invasive NFPAs.

Moreover, one should note the potential bias resource of phosphoproteomics analysis due to the different ethnic origin of the samples. Because the post-mortem control pituitary tissue samples were very difficult to be obtained, this phosphoproteomics study used control pituitary tissues from three subjects from the USA of Caucasian ethnic origin and one subject of African-American ethnic origin, and NFPA tissues from 4 Chinese patients (Table 1). It is also important to emphasize yet another limitation as a source of potential differences in phosphorylation—it is the fact that all control samples were taken post-mortem, while NFPA tissues were taken as biopsies from the living patients. For future thorough investigation of phosphorylation level of each phosphoprotein in NFPAs and control pituitary tissues, we would recommend to investigate the variations of phosphorylation levels among different ethnic origin of the samples (American Caucasian, African-American, and Chinese tissue samples). However, for transcriptomics data (DEG data) between 4 invasive NFPAs and 3 control pituitary tissues in the GEO database, these NFPAs and control pituitary tissue samples were from the same ethnic origin. After the overlapped analysis of phosphoproteins and DEG data, the bias of phosphoproteomics derived from ethnic origin of samples would be adjusted. Also, our previous study investigated the heterogeneity of human control pituitary proteome and identified differentially expressed proteins (DEPs) in the group of different genders (male vs. female), in the group of different ages (30-, 40-, and 50-year-old groups), and in the group of different ethnic origin (white vs. black), of control pituitary tissues [70]. The heterogeneity of human control pituitary proteomes did not significantly affect the differences (DEPs) between NFPAs and control pituitary tissues [71, 72]. It clearly indicated that the pituitary adenoma disease-induced differences might be much larger than, or different from, the gender-, age-, and ethnic origin-induced differences, or the potential differences arising from the type of the tissue origin (post-mortem vs. biopsies), in pituitary tissue proteome, which might help to assure our phosphoproteomics results.

Conclusions and expert recommendation

Protein phosphorylation is an important molecular event in the pathological process of NFPA. This study is the first report to provide the large-scale phosphorylation site profiling with quantitative information and the potential biological functions of protein phosphorylation in human NFPA tissues relative to controls, with TMT-labeled TiO2 enrichment–LC-MS/MS method, bioinformatics, and pathway network analysis. A total of 2982 phosphorylation sites within 1035 phosphoproteins and their involved functional characteristics and signaling pathways were identified in NFPAs. Further, the identified phosphoproteins in NFPAs were integrated with DEG data in invasive NFPAs relative to controls, which revealed a set of overlapped molecules (phosphoproteins; invasive DEGs), followed by analysis of functional characteristics and signaling pathway alterations of those overlapped molecules to reveal the important protein phosphorylation in the pathological process of invasive NFPAs. A total of 130 overlapped molecules (phosphoproteins; invasive DEGs) and their functional characteristics were revealed, and 12 statistically significant signaling pathways were identified to associate the invasive characteristics of invasive NFPAs. Ten hub molecules (phosphoproteins; DEGs) were identified with PPI network-based MCODE analysis in invasive NFPAs, including SLC2A4, TSC2, AKT1, SCG3, ALB, APOL1, ACACA, SPARCL1, CHGB, and IGFBP5. Those findings are the precious resource for new phosphoprotein biomarkers to take deep insight into the molecular mechanisms of NFPAs, prognostic/predictive assessment, patient stratification, and personalized treatment of invasive NFPAs, which might contribute to the development of PPPM in pituitary adenomas [45, 46, 73, 74].

We recommend to strengthen the studies of the large-scale phosphoproteins and their involved signaling pathways in NFPA pathogenesis, and to integrate phosphoproteomics data and transcriptomics data [15, 74] for invasive NFPAs to discover reliable and effective phosphoprotein biomarkers for molecular mechanism clarification of NFPA invasive behavior. These invasiveness-related phosphoprotein biomarkers might be used to discriminate invasive NFPAs from noninvasive NFPAs for patient stratification. The stratified patients (invasive vs. noninvasive NFPAs) will accept the corresponding prognostic assessment and personalized treatment, which will directly contribute to the predictive medicine and personalized medicine in NFPAs. Further, the invasiveness-related biomarkers might benefit the early-stage diagnostics of invasive NFPAs to let invasive NFPA patients undergo an early-stage medical treatment and prevent its progression, or the invasiveness-specific biomarkers might be developed into therapeutic targets for personalized drug treatment to prevent the occurrence and progression of NFPA invasive behavior. These will directly contribute to the predictive, preventive, and personalized medicine applied in NFPA patients.

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Acknowledgments

The authors also acknowledge Professor Xuejun Li from Xiangya Hospital and Professor Dominic M. Desiderio from University of Tennessee Health Science Center to assist in obtaining human tissues.

Abbreviations

ACACA

acetyl-CoA carboxylase 1

ACN

acetonitrile

AGC

automatic gain control

AKT

protein kinase B

AKT1

RAC-alpha serine/threonine-protein kinase

ALB

serum albumin

AMPK

AMP-activated protein kinase

APOL1

apolipoprotein L1

ATF2

activating transcription factor 2

BP

biological process

CC

cellular component

cGMP

cyclic nucleotide cGMP

CHGB

secretogranin-1

DEG

differentially expressed gene

DEP

differentially expressed protein

DTT

dithiothreitol

r-ERG channel

rat ether-à-go-go-related (ERG) channel

ERK

extracellular signal–regulated kinase

ESCRT

endosomal sorting complex required for transport

ESI-TRAP

electrospray ionization-ion trap

FC

fold change

FDR

false discovery rate

FGF-2

fibroblast growth factor-2

FPA

functional pituitary adenoma

GEO

Gene Expression Omnibus

GH

growth hormone

GH3

pituitary growth hormone 3

GnRH

gonadotropin-releasing hormone

GO

Gene Ontology

HCD

high-energy collision dissociation

HIF-1a

hypoxia-inducible factor-1a

HPLC

high-performance liquid chromatography

IGF-1

insulin growth factor-1

IGFBP5

insulin-like growth factor-binding protein 5

KEGG

Kyoto Encyclopedia of Genes and Genomes

LC

liquid chromatography

MAPK

mitogen-activated protein kinase

MCODE

molecular complex detection

MF

molecular function

MMPs

matrix metalloproteinases

MS/MS

tandem mass spectrometry

mTOR

mammalian target of rapamycin

NCBI

National Center for Biotechnology Information

NFκB

nuclear factor kappa-B

NFPA

nonfunctional pituitary adenoma

NMR

nuclear magnetic resonance

PACAP

pituitary adenylyl cyclase activating polypeptide

PI3K

phosphatidylinositol 3 kinase

PKG

protein kinase G

PPI

protein–protein interaction

PTM

post-translational modification

PTTG

pituitary tumor-transforming gene

RSK

ribosomal S6 kinase

SCG3

secretogranin-3

Ser or S

serine

SLC2A4

solute carrier family 2 facilitated glucose transporter member 4

S/N

signal-to-noise

SNAP

soluble N-ethylmaleimide-sensitive fusion attachment protein

SNARE

soluble N-ethylmaleimide-sensitive factor attachment protein receptor

SPARCL1

SPARC-like protein 1

TFA

trifluoroacetic acid

Thr or T

threonine

TMT

tandem mass tag

TSC2

Tuberin

TRH

thyrotropin-releasing hormone

Tyr or Y

tyrosine

VEGF

vascular endothelial growth factor

Author contributions

D.L. analyzed data, prepared figures and tables, and wrote the manuscript draft. J.J., N.L., M.L., and S.W. participated in partial data analysis and bioinformatics. X.Z. conceived the concept, designed and instructed the experiments, analyzed the data, obtained the phosphoproteomic data, supervised the results, coordinated, wrote and critically revised the manuscript, and was responsible for its financial supports and the corresponding works. All authors approved the final manuscript.

Funding information

This work was supported by the Shandong First Medical University Talent Introduction Funds (to X.Z.), the Hunan Provincial Hundred Talent Plan (to X.Z.), the SCIBP supported project (No. SCIBP2019090006), and China “863” Plan Project (Grant No. 2014AA020610-1 to XZ).

Compliance with ethical standards

Competing interests

The authors declare that they have no conflict of interest.

Ethical approval

Four pituitary adenoma tissue samples, obtained from the Department of Neurosurgery of Xiangya Hospital, Central South University, were approved by the Xiangya Hospital Medical Ethics Committee of Central South University. Post-mortem control pituitary tissue samples, obtained from the Memphis Regional Medical Center (n = 5), were approved by the University of Tennessee Health Science Center Internal Review Board.

Footnotes

Abbreviations for all particular genes and proteins can be found in the Supplemental Table 1 and the UniProtKB database at the following link: https://www.expasy.org/.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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ESM 7 (28.5KB, xls)

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ESM 8 (26.5KB, xls)

(XLS 26 kb)

ESM 9 (27KB, xls)

(XLS 27 kb)

ESM 10 (66KB, xls)

(XLS 66 kb)

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