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Clinical Proteomics logoLink to Clinical Proteomics
. 2019 Apr 5;16:12. doi: 10.1186/s12014-019-9232-6

Identification of prothymosin alpha (PTMA) as a biomarker for esophageal squamous cell carcinoma (ESCC) by label-free quantitative proteomics and Quantitative Dot Blot (QDB)

Yanping Zhu 1,#, Xiaoying Qi 1,#, Cuicui Yu 2,#, Shoujun Yu 3, Chao Zhang 1, Yuan Zhang 1, Xiuxiu Liu 1, Yuxue Xu 1, Chunhua Yang 1, Wenguo Jiang 1, Geng Tian 1, Xuri Li 4, Jonas Bergquist 1,5, Jiandi Zhang 1,6, Lei Wang 7,, Jia Mi 1,
PMCID: PMC6449931  PMID: 30988666

Abstract

Background

Esophageal cancer (EC) is one of the malignant tumors with a poor prognosis. The early stage of EC is asymptomatic, so identification of cancer biomarkers is important for early detection and clinical practice.

Methods

In this study, we compared the protein expression profiles in esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal esophageal tissues from five patients through high-resolution label-free mass spectrometry. Through bioinformatics analysis, we found the differentially expressed proteins of ESCC. To perform the rapid identification of biomarkers, we adopted a high-throughput protein identification technique of Quantitative Dot Blot (QDB). Meanwhile, the QDB results were verified by classical immunohistochemistry.

Results

In total 2297 proteins were identified, out of which 308 proteins were differentially expressed between ESCC tissues and normal tissues. By bioinformatics analysis, the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, we suggest that PTMA might be a potential candidate biomarker for ESCC.

Conclusion

In this study, label-free quantitative proteomics combined with QDB revealed that PTMA expression was up-regulated in ESCC tissues, and PTMA might be a potential candidate for ESCC. Since Western Blot cannot achieve rapid and high-throughput screening of mass spectrometry results, the emergence of QDB meets this demand and provides an effective method for the identification of biomarkers.

Keywords: Esophageal squamous cell carcinoma (ESCC), Label-free quantitative proteomics, Prothymosin alpha (PTMA), Quantitative Dot Blot (QDB)

Introduction

Esophageal cancer (EC) is one of the malignant tumors with a 5-year survival incidence of 20.9% [1, 2]. EC is ranked as the eighth most common malignant tumor with the sixth highest mortality rate worldwide. There are two histological subtypes of EC: esophageal squamous cell carcinoma (ESCC) and esophageal adeno carcinoma (EAC). ESCC often occurs in the top or middle of the esophagus, and starts in the flat thin cells that make up the lining of the esophagus. Meanwhile, EAC is most common in the lower portion of the esophagus, and starts in the glandular cells that are responsible for the production of fluids such as mucus. China is a high-risk area for EC, and more than 90% of cases are esophageal squamous cell carcinoma (ESCC) [35]. Moreover, most of the patients exhibit locally advanced or metastatic EC at the time of being diagnosed [6, 7]. Therefore, it is urgent to discover biomarkers for early clinical diagnosis to improve survival.

Esophageal cancer biomarkers have been found in saliva, blood, and urine. Sedighi et al. showed that the serum level of Matric metalloproteinase (MMP)-13 in ESCC patients were significantly higher than in the control group, and suggested that the MMP-13 was associated with increasing ESCC invasion, lymph node involvement and decreased survival rates [8]. In saliva, the miRNAs (miR-10b*, miR-144 and miR-451) were identified up-regulated expression in EC, which possessed discriminatory ability of detecting EC [9]. Although these biomarkers contribute to the early diagnosis and prognosis of EC, the EC biomarker is still in the stage of exploration and verification, with limitations of specificity and low sensitivity.

Proteomic technologies have been applied to understand tumor pathogenesis, and to discover novel targets for cancer therapy or prognosis. Combining MS-based proteomic data with integrative bioinformatics can predict protein signal network and identify more clinical relevant molecules [1012]. To date, quantitative proteomic methods have been applied in the study of various cancer, such as breast cancer, lung cancer, pancreatic cancer and gastric cancer [13]. Mass spectrometric identification of differentially expressed proteins has been a highly successful approach for finding novel cancer-specific biomarkers [14]. For more than a decade, attempts have been made to uncover valid biomarkers for the diagnosis of EC. Currently, various molecules have been identified as closely correlated with ESCC, such as transgelin (TAGLN) and proteasome activator 28-beta subunit (PA28β) [15], pituitary tumor transforming gene (PTTG) [6], transglutaminase 3 (TGM) by proteomics [2]. However, the number of proteins identified was limited in these studies and they did not provide validation of the suggested biomarkers. Therefore, it is still necessary to perform further in-depth proteomics to explore novel candidate biomarkers for EC, and to validate the findings with orthogonal techniques.

Differential proteins obtained from mass spectrometry are commonly identified by Western Blot. However, it couldn’t meet the requirements for high-throughput analysis, due to the complicated processing steps and the requirements for large amount of total protein. Recently, Quantitative Dot Blot (QDB) technology developed by our team achieves high-throughput quantitative detection with the same principle of traditional Western Blot. In addition, QDB technology has the advantages of less sample consumption, short time consumption and low cost [16]. The experiment has been successfully applied to the detection of biomarker of papillary thyroid carcinoma. With its accuracy and reliability, the QDB is a very effective method for protein detection.

The aim of this study was to investigate the protein expression profiles in ESCC tissues and adjacent normal esophageal tissues with a label-free quantitative proteomics approach through nano-liquid chromatography coupled with tandem mass spectrometry (Nano-LC–MS/MS). The differentially expressed proteins were selected and their expression trends were validated in ESCC by Western Blot, then high-throughput protein screening was achieved by QDB, and the results of QDB were verified by classical IHC experiment. This research provides a new methodological strategy for validation and identification ESCC biomarkers by combining quantitative proteomic with QDB.

Materials and methods

Tissue samples

The five patients for LC/MS analysis were all male, with the average age of 61. Samples of ESCC tissues and adjacent normal esophageal tissues were taken for mass spectrometry analysis. The 64 pairs of matched ESCC and adjacent normal tissue samples for QDB were based on a clear pathological diagnosis, which included 35 men and 29 women, with an age range of 46–73 years (mean 61 years). The above samples were obtained at the Affiliated Yantai Hospital of Binzhou Medical University. All data were obtained from patient medical records. All specimens were quickly rinsed and then frozen immediately in liquid nitrogen and then stored at − 80 °C until further processing. The tissue microarrays (TMA) (ES701 and ES1922) for immunohistochemistry analysis were purchased from the alenabio company, the total sample size reached 117 pairs after removing duplicates in two arrays (n = 14). This study was approved by the Human Research Ethics Committee of Binzhou Medical University.

Reagents

Rabbit anti-PPP1CA (CSB-PA030161) and rabbit anti-PAK2 (CSB-PA622641DSR1HU) were purchased from CUSABIO (Wuhan, China). Rabbit anti-PTMA (YN2871) and rabbit anti-HMGB-2 (YT2187) were purchased from ImmunoWay Biotechnology Company (USA). The antibody of Caveolin (AF0126), Integrin beta-1 (AF5379), Collagen alpha-2(VI) (DF3552), Leiomodin-1 (DF12160) and Vinculin (AF5122) were purchased from Affinity Biosciences (USA). Mouse anti-GAPDH monoclonal antibody (sc-32233) was purchased from Santa Cruz Biotechnology (Dallas, TX, USA). Goat anti-rabbit (127,760) and goat anti-mouse (124,227) secondary antibodies were purchased from ZSGB-BIO (Beijing, China).

Sample preparation

The 5 pairs of clinical samples were homogenized and broken with lysis buffer containing 9 M Urea, 20 mM HEPES, and protease inhibitor cocktail. The samples were centrifuged at 12,000×g for 10 min at 4 °C and supernatants retained. Then 20 μg of total protein were digested using the way of in-solution digestion. Firstly, the samples were reduced with 50 mM dithiothreitol (DTT) at 50 °C for 15 min, then alkylated with 50 mM iodoacetamide (IAA) for 15 min in darkness, and then diluted 4 times with digestion buffer (50 mM NH4HCO3, pH 8.0). The proteins were digested by Trypsin with a final concentration of 5% (w/w), then incubated at 37 °C overnight. The reaction was stopped by diluting the sample 1:1 with trifluoroacetic acid (TFA) in acetonitrile (ACN) and Milli-Q water (1/5/94 v/v). Finally, peptides were desalted using Pierce C18 Spin Columns and dried completely in a vacuum centrifuge.

LC–MS/MS

The peptides were dissolved in 20 μL 0.5% TFA in 5% ACN and analyzed using QExactive Plus Orbitrap™ mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) coupled with the liquid chromatography system (EASY-nLC 1000, Thermo Fisher Scientific, Bremen, Germany). A 85-min LC gradient was applied, with a binary mobile phase system of buffer A (0.1% formic acid) and buffer B (80% acetonitrile with 0.1% formic acid) at a flow rate of 250 nL/min. In MS analysis, peptides were loaded onto the 2 cm EASY-column precolumn (1D 100 μm, 5 μm, C18, Thermo Fisher Scientific), and eluted at a 10 cm EASY-column analytical column (1D 75 μm, 3 μm, C18, Thermo Fisher Scientific). For information data dependent analysis (DDA), full scan MS spectra were executed in the m/z range 150–2000 at a resolution of 70,000. The peptides elution was performed with a linear gradient from 4 to 100% ACN at the speed 250 nL/min in 90 min. Then the top 10 precursors were dissociated into fragmentation spectra by high collision dissociation (HCD) in positive ion mode.

Proteomic data processing

The acquired data were analyzed by using Maxquant (version 1.5.0.1) against the UniProt Homo sapiens database. The searching parameters were set as maximum 10 and 5 ppm error tolerance for the survey scan and MS/MS analysis, respectively. The enzyme was trypsin, and two missed cuts were allowed. The max number of modifications per peptide is 5. Using the Label-free quantification (LFQ), the LFQ minimum ratio count was set to 2. The FDR (false discovery rate) was set to 1% for the peptide spectrum matches (PSMs) and protein quantitation. Gene ontology and protein class analysis were performed with the PANTHER system (http://pantherdb.org/). Meanwhile, the heat map of significantly different proteins was screened by using Morpheus (https://software.broadinstitute.org/morpheus). The protein–protein interaction analysis of the differently expressed proteins was performed by STRING (https://string-db.org/).

Western blot (WB)

Tissues lysates were prepared by using highly efficient RIPA lysis buffer including PMSF (Phenylmethanesulfonyl fluoride). The total proteins were quantified by BCA protein assay kit and then separated by sodium dodesyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE). Equal amounts of protein were separated by 6%, 15% and 12% SDS-PAGE, respectively. Subsequently, proteins were transferred to a PVDF membrane and then blocked with TBS (pH 7.4) containing 0.05% Tween 20 and 5% nonfat milk. Next, the membranes were incubated with rabbit anti-PTMA (1:1000), rabbit anti-HMGB-2 (1:500), rabbit anti- PPP1CA (1:1000), rabbit anti-PAK2 (1:1000), and mouse anti-GAPDH (1:1000) antibodies at 4 °C overnight, respectively. The other five antibodies (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were diluted in a ratio of 1:200. After washing, membranes were incubated with goat anti-rabbit (1:2000) and goat anti-mouse (1:2000) secondary antibodies at room temperature for 1 h. The ECL system was used to detect protein expression.

QDB

The total proteins were quantified by BCA protein assay kit and then validated by Quantitative Dot Blot (QDB). Firstly, we determined the linear range of PTMA of the QDB analysis, through the testing of series of concentrations including 0, 0.25, 0.5, 1, 2 and 4 μg/μL. After that, equal amounts of protein were loaded. The sample was incubated at 37 °C for 15 min or until the membrane was completely dried. To block the plate, the QDB plate was dipped in 20% methanol. The plate was then washed with TBST, followed by 5% fat-free milk under constant shaking at room temperature for 1 h. After washing with TBST, the QDB plate was placed in a 96 well plate and 100 μL of primary antibodies was separately added to each individual well and shaken overnight at 4 °C. After washing the QDB plate, 100 μL of the secondary antibody was added to each well and incubated for 1 h at room temperature with shaking. Samples were washed with TBST and detected with the ECL substrate using a Tecan Infiniti 200 pro microplate reader. For each sample, a triplicate measurement was performed, and the average value was obtained. The relative quantitation of each PTMA protein in the lysates was then calculated.

Immunohistochemistry (IHC)

The PTMA expression was detected by IHC in tissue microarrays (TMA) (ES701, ES1922). Firstly, the tissue microarrays were heated at 60 °C for 30 min, then deparaffinized and hydrated with xylol and gradient alcohol, respectively. Next, the antigen retrieval was accomplished by boiling the TMAs for 10 min in citrate buffer (0.01 M, pH 6.0). After cooling at room temperature, the microarrays were treated with 3% hydrogen peroxide for 30 min at 37 °C. The samples were blocked with bovine serum albumin for 30 min at 37 °C, then the PTMA antibody (YN2871, ImmunoWay; dilution 1:50) were incubated overnight at 4 °C in a moist chamber. After using the Histostain-SP (Streptavidin–Peroxidase) kit (SP-0023) as the secondary antibody following the recommendation from the manufacture, operation manual, the samples were washed with PBS (0.01 M, pH 7.2–7.4). Finally, the immunoreactivity was detected by DAB Horseradish Peroxidase Color Development Kit.

Statistics analysis

The WB data was analyzed by means and standard deviation for four independent experiments. The other data was compared between esophageal cancer tissues and adjacent normal esophageal tissues using the two-tailed paired Student’s t test. All statistical analyses were performed by using the statistical software SPSS v20.0 (Chicago, Illinois, USA). P < 0.05 was considered statistically significant.

Results

Identification of differently expressed proteins

The clinical information of the five patients was summarized in Table 1. The five pairs of cancer tissues and adjacent normal tissues were analyzed by label-free mass spectrometry. Total 2297 proteins were identified and 308 proteins with significant differences were selected. Among these proteins, 102 proteins were expressed only in ESCC tissues (Table 2), 155 proteins were significantly up-regulated (Table 3) and 40 proteins were down-regulated in ESCC tissues (Table 4) (P < 0.05). Using the PANTHER classification system, we analyzed the biological significance of these proteins including the cellular component, molecular function and biological process (Fig. 1). The majority of proteins belonged to cell part proteins (37.3%) and organelle proteins (30.1%), possessed the ability of binding (41.8%) and catalytic activity (25.8%), and involved in the cellular process (29.6%), metabolic process (20.2%), cellular component organization or biogenesis (16.3%).

Table 1.

The clinical features of ESCC patients for mass spectrometry

No. Gender Age Organ/anatomic site Grade TNM
1 Male 69 Mid-thoracic esophagus II T2N0MO
2 Male 61 esophagus I T1N0M0
3 Male 59 Middle-lower esophagus II T1N0M0
4 Male 52 Mid-thoracic esophagus III T3N0M0
5 Male 64 Middle segment of esophagus II T2N1M1

Table 2.

List of 102 proteins that were uniquely identified in ESCC tissues

Protein IDs Protein names
P30050 60S ribosomal protein L12
P25788 Proteasome subunit alpha type-3
Q15254 Prothymosin alpha
P12956 X-ray repair cross-complementing protein 6
O15371 Eukaryotic translation initiation factor 3 subunit D
Q59FF0 Staphylococcal nuclease domain-containing protein 1
Q06323 Proteasome activator complex subunit 1
Q15366 Poly(rC)-binding protein 2;Poly(rC)-binding protein 3
Q99729 Heterogeneous nuclear ribonucleoprotein A/B
P62273 40S ribosomal protein S29
O15144 Actin-related protein 2/3 complex subunit 2
Q07955 Serine/arginine-rich splicing factor 1
Q13838 Spliceosome RNA helicase DDX39B
Q14666 Keratin, type I cytoskeletal 17
P00491 Purine nucleoside phosphorylase
P13667 Protein disulfide-isomerase A4
P49755 Transmembrane emp24 domain-containing protein 10
P34932 Heat shock 70 kDa protein 4
P62750 60S ribosomal protein L23a
Q9BRL6 Serine/arginine-rich splicing factor 2
P26583 High mobility group protein B2
O60716 Catenin delta-1
Q13151 Heterogeneous nuclear ribonucleoprotein A0
P62244 40S ribosomal protein S15a
Q8TBK5 60S ribosomal protein L6
P39656 Dolichyl-diphosphooligosaccharide–protein glycosyltransferase 48 kDa subunit
Q53GA7 Tubulin alpha-1C chain
Q92598 Heat shock protein 105 kDa
Q92928 Ras-related protein Rab-1B
Q59F66 Probable ATP-dependent RNA helicase DDX17
P46782 40S ribosomal protein S5
P78417 Glutathione S-transferase omega-1
P23526 Adenosylhomocysteinase
P62081 40S ribosomal protein S7
P11413 Glucose-6-phosphate 1-dehydrogenase
P67809 Nuclease-sensitive element-binding protein 1
Q08211 ATP-dependent RNA helicase A
P17980 26S protease regulatory subunit 6A
Q59EG8 26S proteasome non-ATPase regulatory subunit 2
P27695 DNA-(apurinic or apyrimidinic site) lyase, mitochondrial
P61019 Ras-related protein Rab-2A
P28066 Proteasome subunit alpha type
P49588 Alanine–tRNA ligase, cytoplasmic
O14818 Proteasome subunit alpha type
Q8NB80 Serine/arginine-rich splicing factor 7
Q86UE4 Protein LYRIC
P83731 60S ribosomal protein L24
B4DDM6 Mitotic checkpoint protein BUB3
P20618 Proteasome subunit beta type
P31942 Heterogeneous nuclear ribonucleoprotein H3
Q13177 Serine/threonine-protein kinase PAK 2
P53621 Coatomer subunit alpha;Xenin;Proxenin
Q04760 Lactoylglutathione lyase
Q99439 Calponin;Calponin-2
P62266 40S ribosomal protein S23
P62857 40S ribosomal protein S28
O43852 Calumenin
Q567R6 Single-stranded DNA-binding protein
P22234 Multifunctional protein ADE2
P62195 26S protease regulatory subunit 8
P98179 RNA-binding protein 3
P46781 40S ribosomal protein S9
Q96FW1 Ubiquitin thioesterase OTUB1
O14979 Heterogeneous nuclear ribonucleoprotein D-like
P51571 Translocon-associated protein subunit delta
P05455 Lupus La protein
Q96AE4 Far upstream element-binding protein 1
P17844 Probable ATP-dependent RNA helicase DDX5
P52597 Heterogeneous nuclear ribonucleoprotein F
P60866 40S ribosomal protein S20
Q13148 TAR DNA-binding protein 43
P62136 Serine/threonine-protein phosphatase PP1-alpha catalytic subunit
P07602 Prosaposin
P62633 Cellular nucleic acid-binding protein
Q6FI03 Ras GTPase-activating protein-binding protein 1
P51572 B-cell receptor-associated protein 31
P27635 60S ribosomal protein L10
Q09028 Histone-binding protein RBBP4
Q9UMS4 Pre-mRNA-processing factor 19
P62318 Small nuclear ribonucleoprotein Sm D3
Q15056 Eukaryotic translation initiation factor 4H
P38159 RNA-binding motif protein, X chromosome
Q1KMD3 Heterogeneous nuclear ribonucleoprotein U-like protein 2
P17987 T-complex protein 1 subunit alpha
Q13263 Transcription intermediary factor 1-beta
P29590 Protein PML
Q92499 ATP-dependent RNA helicase DDX1
P51858 Hepatoma-derived growth factor
P60468 Protein transport protein Sec61 subunit beta
Q13185 Chromobox protein homolog 3
P55209 Nucleosome assembly protein 1-like 1
P50454 Serpin H1
P42704 Leucine-rich PPR motif-containing protein, mitochondrial
P61204 ADP-ribosylation factor 1;ADP-ribosylation factor 3
Q9HB71 Calcyclin-binding protein
P11166 Solute carrier family 2, facilitated glucose transporter member 1
Q9Y265 RuvB-like 1
P62807 Histone H2B
Q9UK76 Hematological and neurological expressed 1 protein
P12004 Proliferating cell nuclear antigen
P43243 Matrin-3
P62333 26S protease regulatory subunit 10B

Table 3.

List of 155 proteins that were overexpressed in ESCC tissues

IDs Log ratio P value Protein names
P60842 7.814 0.000 Eukaryotic initiation factor 4A-I
P23396 6.277 0.000 40S ribosomal protein S3
P52272 7.623 0.000 Heterogeneous nuclear ribonucleoprotein M
P43686 10.195 0.000 26S protease regulatory subunit 6B
P14866 8.871 0.000 Heterogeneous nuclear ribonucleoprotein L
P53675 5.484 0.001 Clathrin heavy chain;Clathrin heavy chain 1
P84090 11.171 0.001 Enhancer of rudimentary homolog
P22392 12.881 0.001 Nucleoside diphosphate kinase
Q01105 7.330 0.001 Protein SET;Protein SETSIP
P84103 7.084 0.001 Serine/arginine-rich splicing factor 3
P07900 9.462 0.001 Heat shock protein HSP 90-alpha
Q01518 2.076 0.001 Adenylyl cyclase-associated protein
Q15233 22.489 0.001 Non-POU domain-containing octamer-binding protein
P51149 7.249 0.001 Ras-related protein Rab-7a
Q05CK9 9.797 0.001 Heterogeneous nuclear ribonucleoprotein Q
P10809 9.235 0.001 60 kDa heat shock protein, mitochondrial
P68371 1.935 0.001 Tubulin beta-4B chain
P37802 3.333 0.001 Transgelin-2
P62826 6.962 0.002 GTP-binding nuclear protein Ran
P25398 4.816 0.002 40S ribosomal protein S12
P57723 4.611 0.002 Poly(rC)-binding protein 1
Q12906 28.577 0.002 Interleukin enhancer-binding factor 3
P08865 5.309 0.002 40S ribosomal protein SA
P63244 6.237 0.002 Guanine nucleotide-binding protein subunit beta-2-like 1
P14314 14.510 0.002 Glucosidase 2 subunit beta
P60900 9.105 0.002 Proteasome subunit alpha type
P06748 12.711 0.002 Nucleophosmin
P05388 8.012 0.002 60S acidic ribosomal protein P0
P46940 3.595 0.003 Ras GTPase-activating-like protein IQGAP1
P61978 10.444 0.003 Heterogeneous nuclear ribonucleoprotein K
P05141 2.807 0.003 ADP/ATP translocase 2
Q6LDX7 13.007 0.003 Tyrosine-protein kinase receptor
Q99623 14.381 0.003 Prohibitin-2
P06733 2.361 0.003 Alpha-enolase
P13639 5.459 0.003 Elongation factor 2
Q15084 43.388 0.003 Protein disulfide-isomerase A6
Q96DV6 3.944 0.003 40S ribosomal protein S6
Q66K53 9.606 0.003 HNRPA3 protein
P15880 4.502 0.003 40S ribosomal protein S2
P39019 5.898 0.004 40S ribosomal protein S19
P63104 2.043 0.004 14-3-3 protein zeta/delta
P22626 6.638 0.004 Heterogeneous nuclear ribonucleoproteins A2/B1
P30101 6.086 0.005 Protein disulfide-isomerase
P25786 8.420 0.005 Proteasome subunit alpha type-1
P11940 12.404 0.006 Polyadenylate-binding protein
P16401 4.877 0.006 Histone H1.5
P07237 5.704 0.006 Protein disulfide-isomerase
Q16777 10.160 0.006 Histone H2A type 2-C;Histone H2A type 2-A
P05386 5.889 0.006 60S acidic ribosomal protein P1
P31948 11.491 0.006 Stress-induced-phosphoprotein 1
P31946 2.156 0.007 14-3-3 protein beta/alpha
P68104 2.558 0.007 Elongation factor 1-alpha
P00338 1.590 0.007 L-lactate dehydrogenase
Q14103 6.189 0.007 Heterogeneous nuclear ribonucleoprotein D0
P38646 10.649 0.007 Stress-70 protein, mitochondrial
P26641 19.766 0.007 Elongation factor 1-gamma
O75347 4.168 0.008 Tubulin-specific chaperone A
P09429 5.878 0.008 High mobility group protein B1
P62942 7.427 0.008 Peptidyl-prolyl cis–trans isomerase FKBP1A
Q9NUV1 7.289 0.008 Cytosolic non-specific dipeptidase
P11021 7.467 0.008 78 kDa glucose-regulated protein
P11142 2.320 0.008 Heat shock cognate 71 kDa protein
P02533 5.320 0.008 Keratin, type I cytoskeletal 14
P30040 6.657 0.008 Endoplasmic reticulum resident protein 29
P50990 11.713 0.008 T-complex protein 1 subunit theta
P46783 9.508 0.008 40S ribosomal protein S10
P31943 14.091 0.008 Heterogeneous nuclear ribonucleoprotein H
P19338 13.679 0.009 Nucleolin
P14625 13.173 0.009 Endoplasmin
Q92597 4.464 0.009 Protein NDRG1
P26599 19.501 0.009 Polypyrimidine tract-binding protein 1
P68363 2.317 0.009 Tubulin alpha-1B chain
P61604 9.723 0.009 10 kDa heat shock protein, mitochondrial
P08238 8.920 0.009 Heat shock protein HSP 90-beta
Q00839 15.338 0.009 Heterogeneous nuclear ribonucleoprotein U
P04843 64.275 0.009 Dolichyl-diphosphooligosaccharide–protein glycosyltransferase subunit 1
P09651 10.489 0.010 Heterogeneous nuclear ribonucleoprotein A1
P22314 3.758 0.010 Ubiquitin-like modifier-activating enzyme 1
P30085 3.180 0.010 UMP-CMP kinase
P23246 39.026 0.011 Splicing factor, proline- and glutamine-rich
P29692 13.726 0.011 Elongation factor 1-delta
P27797 7.508 0.011 Calreticulin
Q06830 1.788 0.011 Peroxiredoxin-1
P84243 2.541 0.012 Histone H3
P05023 15.342 0.012 Sodium/potassium-transporting ATPase subunit alpha-1
Q14974 3.995 0.014 Importin subunit beta-1
P30154 2.882 0.014 Serine/threonine-protein phosphatase 2A
P49448 5.013 0.015 Glutamate dehydrogenase
P20700 14.379 0.015 Lamin-B1
P55072 6.054 0.016 Transitional endoplasmic reticulum ATPase
P35579 8.278 0.016 Myosin-9
P40227 8.241 0.016 T-complex protein 1 subunit zeta
P13010 223.628 0.017 X-ray repair cross-complementing protein 5
Q03252 12.919 0.017 Lamin-B2
P27824 9.105 0.017 Calnexin
P02545 1.376 0.017 Prelamin-A/C;Lamin-A/C
P67936 10.102 0.017 Tropomyosin alpha-4 chain
P04908 2.018 0.018 Histone H2A
P13797 5.684 0.019 Plastin-3
P52907 3.377 0.019 F-actin-capping protein subunit alpha-1
P63241 4.197 0.019 Eukaryotic translation initiation factor 5A
P62491 3.628 0.019 Ras-related protein Rab-11A;Ras-related protein Rab-11B
P45880 2.304 0.020 Voltage-dependent anion-selective channel protein 2
P05387 4.257 0.020 60S acidic ribosomal protein P2
Q5SRT3 3.484 0.021 Chloride intracellular channel protein
P07437 3.687 0.021 Tubulin beta chain
P23284 8.401 0.022 Peptidyl-prolyl cis–trans isomerase
P18124 5.442 0.022 60S ribosomal protein L7
P07355 1.909 0.022 Annexin;Annexin A2
P46777 12.124 0.023 60S ribosomal protein L5
Q99714 1.923 0.023 3-hydroxyacyl-CoA dehydrogenase type-2
O75531 9.745 0.024 Barrier-to-autointegration factor
Q14697 21.165 0.025 Neutral alpha-glucosidase AB
P62263 6.347 0.025 40S ribosomal protein S14
P0DMV9 2.049 0.026 Heat shock 70 kDa protein 1B
P29034 6.458 0.026 Protein S100-A2
P62888 2.893 0.026 60S ribosomal protein L30
Q6IBT3 23.335 0.027 T-complex protein 1 subunit eta
P47756 2.818 0.027 F-actin-capping protein subunit beta
P35222 7.555 0.028 Catenin beta-1
P07339 5.983 0.029 Cathepsin D
Q86SZ7 4.151 0.029 Proteasome activator complex subunit 2
P15311 3.903 0.029 Ezrin;Tyrosine-protein kinase receptor
P59665 4.537 0.029 Neutrophil defensin 1
P09960 5.492 0.030 Leukotriene A-4 hydrolase
P63220 4.048 0.030 40S ribosomal protein S21
Q16658 114.974 0.031 Fascin
P07954 5.399 0.032 Fumarate hydratase, mitochondrial
P54819 4.652 0.034 Adenylate kinase 2, mitochondrial
P07737 1.223 0.034 Profilin-1
P63313 5.261 0.034 Thymosin beta-10
P21796 3.716 0.034 Voltage-dependent anion-selective channel protein 1
P61247 12.449 0.035 40S ribosomal protein S3a
P14618 1.508 0.035 Pyruvate kinase
P61626 4.029 0.036 Lysozyme;Lysozyme C
Q15181 8.459 0.037 Inorganic pyrophosphatase
P27348 3.220 0.037 14-3-3 protein theta
P49411 14.069 0.037 Elongation factor Tu, mitochondrial
P05164 10.019 0.037 Myeloperoxidase
P61160 5.976 0.038 Actin-related protein 2
Q04917 4.768 0.039 14-3-3 protein eta
P62805 1.761 0.039 Histone H4
P26373 3.700 0.040 60S ribosomal protein L13
Q14204 2.799 0.041 Cytoplasmic dynein 1 heavy chain 1
P56537 7.504 0.041 Eukaryotic translation initiation factor 6
P08708 10.144 0.042 40S ribosomal protein S17
P15153 2.613 0.042 Ras-related C3 botulinum toxin substrate 2
P31949 2.100 0.045 Protein S100
P36952 6.679 0.046 Serpin B5
Q15149 4.694 0.047 Plectin
P46779 6.182 0.048 60S ribosomal protein L28
Q59FH0 5.442 0.048 Histone H2A
P62937 1.778 0.049 Peptidyl-prolyl cis–trans isomerase
P07741 5.077 0.049 Adenine phosphoribosyltransferase
P62269 3.688 0.050 40S ribosomal protein S18

Table 4.

List of 40 proteins that were low-expressed in ESCC tissues

IDs Log ratio P value Protein names
P55268 0.078 0.001 Laminin subunit beta-2
Q13361 0.000 0.001 Microfibrillar-associated protein 5
O95682 0.000 0.001 Tenascin-X
P12277 0.024 0.001 Creatine kinase B-type
P20774 0.018 0.002 Mimecan
P06396 0.501 0.002 Gelsolin
O75106 0.000 0.002 Membrane primary amine oxidase
P60660 0.260 0.002 Myosin light polypeptide 6
P51884 0.118 0.003 Lumican
P35555 0.183 0.003 Fibrillin-1
Q5U0D2 0.081 0.004 Transgelin
P35749 0.029 0.004 Myosin-11
P51888 0.032 0.004 Prolargin
P24844 0.033 0.005 Myosin regulatory light polypeptide 9
P17661 0.063 0.005 Desmin
P98160 0.213 0.006 Basement membrane-specific heparan sulfate proteoglycan core protein
P12109 0.299 0.006 Collagen alpha-1(VI) chain
Q07507 0.084 0.006 Dermatopontin
P11047 0.209 0.006 Laminin subunit gamma-1
Q6ZN40 0.114 0.006 CDNA FLJ16459 fis
P18206 0.259 0.008 Vinculin
Q14112 0.065 0.010 Nidogen-2
P21291 0.086 0.011 Cysteine and glycine-rich protein 1
P68032 0.312 0.011 Actin, alpha cardiac muscle 1
Q9NZN4 0.000 0.012 EH domain-containing protein 2
P07585 0.087 0.012 Decorin
Q15746 0.021 0.014 Myosin light chain kinase, smooth muscle
Q9Y490 0.318 0.015 Talin-1
P12110 0.223 0.016 Collagen alpha-2(VI) chain
P21810 0.235 0.020 Biglycan
Q93052 0.048 0.021 Lipoma-preferred partner
P30086 0.507 0.021 Phosphatidylethanolamine-binding protein 1
P62736 0.043 0.022 Actin, aortic smooth muscle
Q96AC1 0.029 0.023 Fermitin family homolog 2
Q6NZI2 0.213 0.025 Polymerase I and transcript release factor
Q59F18 0.000 0.027 Smoothelin isoform b variant
O14558 0.000 0.027 Heat shock protein beta-6
Q13642 0.004 0.028 Four and a half LIM domains protein 1
P12111 0.321 0.031 Collagen alpha-3(VI) chain
P29536 0.000 0.032 Leiomodin-1
P05556 0.416 0.033 Integrin beta-1
Q15124 0.000 0.033 Phosphoglucomutase-like protein 5
P21333 0.213 0.033 Filamin-A
Q53GG5 0.013 0.036 PDZ and LIM domain protein 3
P01009 0.429 0.037 Alpha-1-antitrypsin;Short peptide from AAT
P43121 0.000 0.038 Cell surface glycoprotein MUC18
P52943 0.210 0.041 Cysteine-rich protein 2
P08294 0.000 0.043 Extracellular superoxide dismutase [Cu–Zn]
P56539 0.155 0.043 Caveolin
O15061 0.000 0.045 Synemin
Q9NR12 0.044 0.047 PDZ and LIM domain protein 7

Fig. 1.

Fig. 1

Classification of identified proteins by gene ontology based on their a molecular function, b biological process and c cellular component. The analysis of proteins were performed via the PANTHER (http://pantherdb.org/)

Bioinformatics analysis of differentially expressed proteins

A volcano plot was generated based on the differential expression ratio and P value (Fig. 2a). Moreover, the heat map of significantly different proteins was shown in Fig. 2b by using Morpheus (https://software.broadinstitute.org/morpheus). Further protein–protein interaction analysis of the differently expressed proteins was performed by STRING, the result was shown in Fig. 3. Out of the four proteins selected for next analysis, the PPI network analysis revealed that PTMA was a valid target of c-myc transcriptional activation, while PPP1CA was involved in down-regulation of TGF-beta receptor signaling. PAK2 plays a role in apoptosis and activation of Rac, while HMGB2 is participating in chromatin regulation and retinoblastoma in cancer. Above mentioned, all these four proteins were associated with the occurrence and development of cancer. Bioinformatics analysis of the four genes from TCGA database revealed that the four genes up-regulated in gene level in EC tissue (Fig. 4). Whether these four genes can be used as biomarkers of esophageal cancer remains to be further studied.

Fig. 2.

Fig. 2

Analysis of protein differential expression. a Volcano plot graph illustrating the differential abundant proteins in the quantitative analysis. The − log10 (P value) was plotted against the log2 (ratio cancer/normal). The red dots represented proteins up-regulated in cancer samples, green dots corresponded to proteins down-regulated in cancer samples. b The heat map of significantly different proteins was shown between cancer tissues and adjacent normal tissues. The analysis was achieved by using Morpheus (https://software.broadinstitute.org/morpheus)

Fig. 3.

Fig. 3

Protein-protein interaction network of the differently expressed proteins was identified by STRING. Four proteins were selected for further study with filled red circles (https://string-db.org/)

Fig. 4.

Fig. 4

The expression of PTMA, PAK2, PPP1CA and HMGB2 in ESCC based on major cancer stages. In the TCGA databases, the four genes were up-regulated in EC patients (P < 0.001). (http://ualcan.path.uab.edu/analysis.html)

Validation of differentially expressed proteins by Western Blot

To further validate the LC–MS/MS results, we evaluated the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins [Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin] with Western Blot on the same samples. Compared with adjacent normal tissues, the protein expression of PTMA, PAK2, PPP1CA, HMGB2 were up-regulated (Fig. 5a, b), and the protein expression of Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1, Vinculin were down-regulated in ESCC tissues from four pairs of samples (Fig. 5c, d). The results showed that the trends expression of these proteins were consistent with the LC–MS results.

Fig. 5.

Fig. 5

The differentially expressed proteins were validated by Western Blot. Compared with adjacent normal tissues, the protein expression of PTMA, PAK2, PPP1CA, HMGB2 were up-regulated (a, b), and the protein expression of Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1, Vinculin were down-regulated in ESCC tissues from four pairs of samples (c, d). Representative immunoblot images (a, c) and histograms (mean ± SD; b, d).The experiments were repeated at least three times, N represented normal tissues and T represented tumor tissues

Validation of PTMA involved in ESCC by QDB and IHC

In order to validate the proteins identified by mass spectrometric, the QDB technique was applied in a larger set of samples. We collected the samples of 64 patients, and the relevant clinical information was summarized in Table 5. In the analysis of 64 patient samples, we found that 53 out of 64 esophageal cancer tissues showed higher PTMA expression than in the normal tissues (P < 0.001) (Fig. 6). This trend was in accordance with the previous data. To further validate the QDB results, we performed the tissue microarray analysis by IHC. The results showed that among 117 pairs of tissues, the high expression rate of PTMA in tumor tissues was 98% (115/117). A significant overexpression of PTMA was found in tumor tissues in contrast to adjacent normal tissues (P < 0.01) (Fig. 7). The sample information in the chip is summarized in Tables 6 and 7. We further evaluated the expression pattern of PTMA with the progression, and analyzed the PTMA expression trend in the different tumor Grades. The results revealed that the PTMA expression was up-regulated gradually along the progression of ESCC (Fig. 8). The PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression (P < 0.05). So we can suspect that PTMA might be participating in the development of esophageal cancer.

Table 5.

The clinical features of ESCC patients for QDB analysis

No. Gender Age Organ/anatomic site Grade TNM
1 Male 69 esophagus II T1N0M0
2 Male 61 esophagus I T0N0M0
3 Male 59 esophagus II T3N0M0
4 Female 65 esophagus I T0N0M0
5 Male 52 esophagus II–III T3N0M0
6 Female 73 esophagus I–II T1N0M0
7 Male 46 esophagus I T0N0M0
8 Male 64 Lower segment of esophagus II T3N2M0
9 Male 57 Mid-thoracic esophagus II T3N0M0
10 Male 54 Mid-thoracic esophagus II–III T3N0M0
11 Male 72 Mid-thoracic esophagus II T3N3M0
12 Male 66 Mid-thoracic esophagus II T3N3M0
13 Male 62 Middle-lower esophagus II T1N0M0
14 Male 60 esophagus II T3N0M0
15 Female 60 esophagus II T3N0M0
16 Male 64 esophagus II T3N0M0
17 Female 58 Lower thoracic esophagus III T3N0M0
18 Male 53 esophagus II T3N0M0
19 Male 65 Lower thoracic esophagus II–III T3N0M0
20 Female 60 Mid-thoracic esophagus I–III T3N0M0
21 Male 69 Middle-lower esophagus II T3N3M0
22 Female 66 esophagus II–III T3N2M0
23 Female 67 Lower segment of esophagus II–III T3N3M1
24 Male 67 Mid-thoracic esophagus III T3N1M0
25 Female 55 Mid-thoracic esophagus II T2N1M0
26 Female 61 Mid-thoracic esophagus I–II T1N2M0
27 Male 68 esophagus II–III T3N2M0
28 Female 48 Mid-thoracic esophagus I–II T3N0M0
29 Female 63 Mid-thoracic esophagus II T1N1M0
30 Male 70 Lower segment of esophagus II T2N1M0
31 Female 59 Mid-thoracic esophagus III T3N1M0
32 Female 48 Mid-thoracic esophagus II T3N0M0
33 Female 53 Mid-thoracic esophagus II T3N2M1
34 Female 58 Lower thoracic esophagus I-II T3N0M0
35 Male 62 Mid-thoracic esophagus II T2N0M0
36 Female 59 esophagus II T3N1M1
37 Female 57 esophagus II T3N0M0
38 Female 57 Lower thoracic esophagus II T3N1M1
39 Female 62 Mid-thoracic esophagus I–II T3N0M0
40 Female 69 Mid-thoracic esophagus II–III T3N1M1
41 Female 61 Mid-thoracic esophagus II T3N2M1
42 Female 67 Mid-thoracic esophagus II T2N0M0
43 Female 47 Mid-thoracic esophagus II T2N0M0
44 Female 69 Lower thoracic esophagus III T2N2M1
45 Male 66 esophagus II T3N0M0
46 Male 72 Mid-thoracic esophagus II T3N0M0
47 Female 69 Mid-thoracic esophagus II–III T3N0M0
48 Female 73 Mid-thoracic esophagus I T1N0M0
49 Male 62 esophagus II T3N0M0
50 Male 58 esophagus II T2N0M0
51 Male 56 Lower segment of esophagus II T1N0M0
52 Male 56 Middle-lower esophagus II T3N0M0
53 Male 56 Middle-lower esophagus II T3N0M0
54 Male 55 esophagus I–II T3N0M0
55 Female 61 esophagus I–II T3N0M0
56 Female 71 Middle-lower esophagus I–II T1N0M0
57 Male 61 esophagus II–III T3N3M1
58 Male 62 Upper thoracic esophagus III T3N0M0
59 Male 67 Mid-thoracic esophagus I T1N0M0
60 Male 65 esophagus I T3N0M0
61 Male 58 esophagus II–III T2N1M1
62 Male 49 Lower segment of esophagus I T1N0M0
63 Female 66 esophagus III T3N1M1
64 Male 70 esophagus I T1N0M0

Fig. 6.

Fig. 6

The relative PTMA expression was tested by QDB in ESCC and adjacent normal tissues from 64 esophageal cancer patients. a The differential expression of PTMA was shown in each pair of tissues. b The PTMA expression was up-regulated in esophageal cancer tissues from the average of 64 pairs of tissues

Fig. 7.

Fig. 7

The relative PTMA expression was tested by IHC in ESCC and adjacent normal tissues among 117 pairs of tissues (× 200). a The expression of PTMA in adjacent normal tissues were presented. b The expression of PTMA in esophageal cancer were up-regulated. c The gray-scale analysis of immunohistochemical results (P < 0.001)

Table 6.

The 35 pairs samples in tissue microarrays (TMA) (ES701) for immunohistochemistry analysis

No. Gender Age Organ/anatomic site Grade TNM
1 Male 60 Esophagus II T3N1M0
2 Male 60 Esophagus
3 Male 44 Esophagus I T3N1M0
4 Male 44 Esophagus
5 Male 50 Esophagus I T3N2M0
6 Male 50 Esophagus
7 Male 53 Esophagus I T3N0M0
8 Male 53 Esophagus
9 Male 64 Esophagus I T3N1M0
10 Male 64 Esophagus
11 Male 69 Esophagus I T3N0M0
12 Male 69 Esophagus
13 Male 59 Esophagus I T3N0M0
14 Male 59 Esophagus
15 Male 60 Esophagus I T3N1M0
16 Male 60 Esophagus
17 Male 72 Esophagus I T3N1M0
18 Male 72 Esophagus
19 Female 60 Esophagus I T3N1M0
20 Female 60 Esophagus
21 Female 75 Esophagus III T3N0M0
22 Female 75 Esophagus
23 Male 57 Esophagus II T3N1M0
24 Male 57 Esophagus
25 Female 54 Esophagus II T3N1M0
26 Female 54 Esophagus
27 Male 45 Esophagus III T3N0M0
28 Male 45 Esophagus
29 Male 52 Esophagus II T3N0M0
30 Male 52 Esophagus
31 Male 68 Esophagus T3N0M0
32 Male 68 Esophagus
33 Male 67 Esophagus I T3N0M0
34 Male 67 Esophagus
35 Male 55 Esophagus I T3N0M0
36 Male 55 Esophagus
37 Male 71 Esophagus I T3N1M0
38 Male 71 Esophagus
39 Male 63 Esophagus III T3N1M0
40 Male 63 Esophagus
41 Male 67 Esophagus III T3N1M0
42 Male 67 Esophagus
43 Male 57 Esophagus III T3N0M0
44 Male 57 Esophagus
45 Male 63 Esophagus III T3N0M0
46 Male 63 Esophagus
47 Male 57 Esophagus III T3N1M0
48 Male 57 Esophagus
49 Male 58 Esophagus III T3N1M0
50 Male 58 Esophagus
51 Male 53 Esophagus II T3N1M0
52 Male 53 Esophagus
53 Male 49 Esophagus I T3N1M0
54 Male 49 Esophagus
55 Male 68 Esophagus III T3N1M0
56 Male 68 Esophagus
57 Male 48 Esophagus III T3N0M0
58 Male 48 Esophagus
59 Female 58 Esophagus II T3N1M0
60 Female 58 Esophagus
61 Male 44 Esophagus III T3N1M0
62 Male 44 Esophagus
63 Male 63 Esophagus II T3N1M0
64 Male 63 Esophagus
65 Male 68 Esophagus III T3N1M0
66 Male 68 Esophagus
67 Female 68 Esophagus III T3N1M0
68 Female 68 Esophagus
69 Male 62 Esophagus III T2M1N1B
70 Male 62 Esophagus

Table 7.

The 96 pairs samples in tissue microarrays (TMA) (ES1922) for immunohistochemistry analysis

No. Gender Age Organ/anatomic site Grade TNM
1 Male 58 Esophagus I T3N0M0
2 Male 58 Esophagus
3 Male 68 Esophagus I T3N1M0
4 Male 68 Esophagus
5 Male 52 Esophagus I T1N0M0
6 Male 52 Esophagus
7 Female 66 Esophagus I T3N0M0
8 Female 66 Esophagus
9 Male 72 Esophagus I T3N1M0
10 Male 72 Esophagus
11 Male 67 Esophagus I T3N0M0
12 Male 67 Esophagus
13 Male 66 Esophagus I T3N1M0
14 Male 66 Esophagus
15 Male 55 Esophagus I T3N1M0
16 Male 55 Esophagus
17 Male 67 Esophagus I T3N1M0
18 Male 67 Esophagus
19 Female 71 Esophagus I T3N0M0
20 Female 71 Esophagus
21 Male 69 Esophagus I T3N0M0
22 Male 69 Esophagus
23 Male 68 Esophagus I T3N0M0
24 Male 68 Esophagus
25 Male 44 Esophagus I T3N1M0
26 Male 44 Esophagus
27 Female 63 Esophagus I T2N0M0
28 Female 63 Esophagus
29 Female 54 Esophagus I T3N1M0
30 Female 54 Esophagus
31 Male 60 Esophagus I T2N0M0
32 Male 60 Esophagus
33 Female 68 Esophagus II T3N0M0
34 Female 68 Esophagus
35 Male 49 Esophagus I T3N1M0
36 Male 49 Esophagus
37 Male 61 Esophagus I T3N0M0
38 Male 61 Esophagus
39 Female 69 Esophagus I T3N1M0
40 Female 69 Esophagus
41 Male 49 Esophagus I T3N1M0
42 Male 49 Esophagus
43 Male 68 Esophagus I T3N0M0
44 Male 68 Esophagus
45 Male 66 Esophagus II T3N0M0
46 Male 66 Esophagus
47 Male 53 Esophagus II T3N1M0
48 Male 53 Esophagus
49 Female 58 Esophagus I T3N0M0
50 Female 58 Esophagus
51 Male 63 Esophagus I T3N0M0
52 Male 63 Esophagus
53 Female 68 Esophagus I T2N0M0
54 Female 68 Esophagus
55 Female 68 Esophagus I T3N0M0
56 Female 68 Esophagus
57 Male 58 Esophagus I T3N0M0
58 Male 58 Esophagus
59 Female 60 Esophagus I T3N0M0
60 Female 60 Esophagus
61 Male 70 Esophagus II T2N1M0
62 Male 70 Esophagus
63 Female 61 Esophagus I T3N0M0
64 Female 61 Esophagus
65 Male 54 Esophagus II T3N0M0
66 Male 54 Esophagus
67 Male 45 Esophagus II T3N0M0
68 Male 45 Esophagus
69 Male 75 Esophagus III T3N0M0
70 Male 75 Esophagus
71 Male 63 Esophagus I T3N0M0
72 Male 63 Esophagus
73 Male 68 Esophagus I T3N0M0
74 Male 68 Esophagus
75 Female 50 Esophagus II T3N0M0
76 Female 50 Esophagus
77 Male 72 Esophagus III T3N0M0
78 Male 72 Esophagus
79 Female 53 Esophagus III T3N0M0
80 Female 53 Esophagus
81 Male 69 Esophagus II T3N1M0
82 Male 69 Esophagus
83 Male 57 Esophagus I T3N0M0
84 Male 57 Esophagus
85 Male 68 Esophagus III T3N1M0
86 Male 68 Esophagus
87 Male 51 Esophagus III T3N0M0
88 Male 51 Esophagus
89 Male 70 Esophagus I T3N1M0
90 Male 70 Esophagus
91 Male 68 Esophagus II T3N1M0
92 Male 68 Esophagus
93 Male 57 Esophagus III T3N0M0
94 Male 57 Esophagus
95 Male 48 Esophagus II T3N0M0
96 Male 48 Esophagus
97 Male 63 Esophagus III T3N1M0
98 Male 63 Esophagus
99 Male 65 Esophagus II T3N0M0
100 Male 65 Esophagus
101 Male 71 Esophagus III T3N1M0
102 Male 71 Esophagus
103 Male 78 Esophagus III T3N0M0
104 Male 78 Esophagus
105 Male 53 Esophagus II T3N1M0
106 Male 53 Esophagus
107 Male 57 Esophagus II T3N0M0
108 Male 57 Esophagus
109 Male 63 Esophagus II T3N1M0
110 Male 63 Esophagus
111 Male 63 Esophagus III T3N1M0
112 Male 63 Esophagus
113 Female 58 Esophagus I T3N1M0
114 Female 58 Esophagus
115 Male 50 Esophagus II T2N0M0
116 Male 50 Esophagus
117 Male 44 Esophagus I T3N1M0
118 Male 44 Esophagus
119 Male 61 Esophagus I T3N1M0
120 Male 61 Esophagus
121 Male 61 Esophagus I T3N1M0
122 Male 61 Esophagus
123 Male 57 Esophagus II T3N1M0
124 Male 57 Esophagus
125 Male 60 Esophagus I T3N0M0
126 Male 60 Esophagus
127 Male 58 Esophagus II T3N0M0
128 Male 58 Esophagus
129 Male 61 Esophagus II T3N0M0
130 Male 61 Esophagus
131 Male 52 Esophagus I T3N1M0
132 Male 52 Esophagus
133 Female 60 Esophagus II T3N1M0
134 Female 60 Esophagus
135 Male 68 Esophagus II T3N0M0
136 Male 68 Esophagus
137 Female 43 Esophagus III T3N1M0
138 Female 43 Esophagus
139 Male 59 Esophagus III T3N1M0
140 Male 59 Esophagus
141 Male 55 Esophagus III T3N1M0
142 Male 55 Esophagus
143 Male 68 Esophagus III T3N0M0
144 Male 68 Esophagus
145 Female 70 Esophagus III T3N0M0
146 Female 70 Esophagus
147 Male 74 Esophagus III T2N0M0
148 Male 74 Esophagus
149 Male 54 Esophagus I T2N0M0
150 Male 54 Esophagus
151 Male 64 Esophagus III T3N1M0
152 Male 64 Esophagus
153 Male 57 Esophagus I T3N1M0
154 Male 57 Esophagus
155 Male 48 Esophagus III T3N0M0
156 Male 48 Esophagus
157 Female 61 Esophagus III T3N0M0
158 Female 61 Esophagus
159 Male 61 Esophagus III T3N1M0
160 Male 61 Esophagus
161 Male 65 Esophagus III T3N0M0
162 Male 65 Esophagus
163 Male 55 Esophagus III T2N0M0
164 Male 55 Esophagus
165 Female 56 Esophagus III T3N0M0
166 Female 56 Esophagus
167 Female 73 Esophagus II T3N0M0
168 Female 73 Esophagus
169 Male 70 Esophagus III T3N0M0
170 Male 70 Esophagus
171 Male 53 Esophagus III T3N1M0
172 Male 53 Esophagus
173 Male 67 Esophagus III T2N0M0
174 Male 67 Esophagus
175 Male 69 Esophagus III T3N0M0
176 Male 69 Esophagus
177 Male 68 Esophagus III T3N0M0
178 Male 68 Esophagus
179 Male 64 Esophagus III T3N0M0
180 Male 64 Esophagus
181 Male 61 Esophagus III T3N1M0
182 Male 61 Esophagus
183 Male 59 Esophagus III T3N0M0
184 Male 59 Esophagus
185 Male 57 Esophagus III T2N0M0
186 Male 57 Esophagus
187 Male 64 Esophagus III T3N0M0
188 Male 64 Esophagus
189 Female 67 Esophagus I T2N0M0
190 Female 67 Esophagus
191 Male 47 Esophagus III T2N0M0
192 Male 47 Esophagus

Fig. 8.

Fig. 8

The PTMA expression was up-regulated gradually along the progression of ESCC. a The PTMA expression trend at the different Grades in QDB samples. b The PTMA expression trend at the different Grades in IHC samples. I, II, III represented ESCC Grade I, Grade II and Grade III respectively. (*P < 0.05)

Discussions

At present, most patients with esophageal cancer are diagnosed at the late and advanced stages [17]. It is thus urgent to reveal biomarkers related to the progression of esophageal cancer for early diagnosis. Recently, several biomarkers were identified in EC detection, diagnosis, treatment and prognosis. For example, the epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF) and estrogen receptor (ER) were important detection factors for immunohistochemistry in EC [1820]. In blood, the serum p53 antibody had a potential diagnostic value for EC, however, the detection was limited by its low sensitivity [21]. Therefore, we need to discover and verify more biomarker candidates for the prediction, diagnosis, treatment and prognosis of esophageal cancer.

Mass spectrometry is an effective method for finding distinct molecular regulators, between normal tissues and cancer tissues [22]. In current study, we proposed a significant proteomics profiling difference including 308 proteins. However, compare to previous tissue-based ESCC proteomics study, a poor overlap of proteome profiling was noticed. There are several potential reasons. First, like many other cancers, ESCC is a heterogeneous cancer with different gene expression profiles from different populations [23]. Recently, the whole-genome sequencing revealed the diverse models of structural variations in ESCC, which indicted the biological differences among patients [24]. Therefore, the proteome variation may be a consequence of distinct molecular signatures that exist in ESCC. Another reasons could be related to the different experiment design, some of studies pooled several individual samples into a sample pooling, which would also lead to potential difference compare to our individual analysis [25]. The difference of data analysis method would be another reason too, most of the labeled-based MS approach selected the expression fold change as the major criteria. In our study, with a label-free approach, we proposed paired Student’s t-test significance as the main criteria. Such difference could lead to a different proteome profiling. The poor overlap indicated the importance of large-scale validation of biomarker. Thus we suggest in future studies, the proposed novel biomarker should be validated in a larger population no less than 100 samples. Besides TMA, our group recently developed QDB as a novel fast and accurate validation approach, which can easily validate biomarkers up to thousand samples [16].

Human prothymosin-α (PTMA) is a 109 amino acid protein belonged to the α-thymosin family, which is ubiquitously distributed in mammalian blood, tissues and especially abundant in lymphoid cells. However, its role still remains elusive. The growing evidences suggested that PTMA being an important immune mediator as well as a biomarker might eventually become a new therapeutic target or diagnostic method in several diseases such as cancer and inflammation [26]. So we focused on the possibility of PTMA as a biomarker of ESCC.

The proteomic studies show that PTMA exerts multifunction in nuclear and cytoplasmic. In proliferating cells, PTMA mainly locates in nuclear depending on the C-terminus signal sequence, but this protein can be transferred from the nucleus into the cytoplasmic during the cell extraction process [27, 28]. PTMA may mediate the chromatin activity by participated the nuclear-protein complex. In cytoplasmic, the function of PTMA is related to the state of phosphorylation, for example, the Thr7 is the only residue phosphorylated in carcinogenic lymphocytes while the Thr12 or Thr13 phosphorylated in normal lymphocytes [29, 30]. The co-immunoprecipitation experiments shows that PTMA interact with SET, ANP32A and ANP32B to form the complex, which is related to the cell proliferation, membrane trafficking, proteolytic processing and so on [3133].

PTMA is known to play an important role in cell growth, proliferation, apoptosis and so on [34, 35]. Recent studies have confirmed that overexpression of PTMA is involved in the development of various malignancies, including colorectal, bladder, lung, and liver cancer [3638]. In vivo tumorigenesis, the PTMA expression promotes the transplant tumor growth in mice and speeds up their death. Meanwhile, the PTMA interacts with TRIM21 directly to regulate the Nrf2 expression through p62/Keap1 signaling in human bladder cancer [39]. In the patients with squamous cell carcinoma (SCC), adenosquamous cell carcinoma (ASC) and adenocarcinoma (AC) of the gallbladder, the positive expression of PTMA may be associated with the tumorigenesis, tumor progression and prognosis in gallbladder tumor. In addition, the high expression of PTMA may be as an indicator in the prevention and early diagnosis of gallbladder tumor [40]. In addition to inducing cancer, Wang et al. discovered that PTMA as a new autoantigen regulated oral submucous fibroblast proliferation and extracellular matrix using human proteome microarray analysis. In addition, PTMA knockdown reversed TGFβ1-induced fibrosis process through reducing the protein levels of collagen I, α-SMA and MMP [34]. However, there have been no evidences that PTMA participates in the pathogenesis of esophageal cancer.

Our mass spectrometry results showed that PTMA expression was up-regulated in ESCC tissues, and if the result was universal, it would provide a good biomarker for the diagnosis of ESCC. The traditional Western Blot is tedious, laborious and time-consuming for hundreds and thousands of large samples tests. In order to verify the results of mass spectrometry, we adopted the QDB technology invented recently, which was capable of high-throughput identification of target proteins from the perspective of biological experiments compared with Western Blot. QDB performed an affordable method for high-throughput immunoblot analysis and achieved relative or absolute quantification. In addition, the QDB needs less sample consumption, and the data can be conveniently read by a microplate reader. In HEK293 cells, the QDB successfully compared the levels of relative p65 levels between Luciferase and p65 clones in 71 pairs of samples. We have confirmed the accuracy and reliability of QDB from both cells and tissues [16]. As above mentioned, QDB is a convenient, reliable and affordable method. In our study, we confirmed that 53 out of 64 tested ESCC tissues had higher PTMA expression by the QDB, and the results were identified by classical IHC methods in 117 pairs of samples.

In this study, we included both explore experiment and validation experiment, using early and late stage samples. The results from explore experiment indicated that PTMA was overexpressed in all stages. We further evaluated the expression pattern of PTMA with the progression, and analyzed the PTMA expression trend in the different Grades. The results revealed that the PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. As it is almost impossible to obtain the extreme early stage (such as the stage without any symptom, or the stage prior to Grade I), but from the trend between Grade I and III, we can suspect the expression ratio of PTMA would be a potential indicator for the progression, even in the early diagnosis.

Conclusions

In our research, we used label-free quantitative proteomics to detect differentially expressed protein profiles in ESCC tissues compared to control tissues. In total 2297 proteins were identified and 308 proteins with significant differences were selected for study. Based on in-depth bioinformatic analysis, the four up-regulated proteins [PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin] were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, the PTMA is suggested as a candidate biomarker for ESCC. Our research also presents a new methodological strategy for the identification and validation of novel cancer biomarkers by combining quantitative proteomic with QDB.

Authors’ contributions

JM and LW conceived the experiments; YPZ, XYQ, CCY, YZ and XXL performed the experiments; CCY, SJY, YXX and CHY collected the clinical materials; JM and CZ analyzed the protein data; WGJ, GT and JDZ conducted the statistical analysis; XRL and JB modified the paper. All authors read and approved the final manuscript.

Competing interests

All authors declare that they have no competing interests. Jiandi Zhang declares competing interests, and he has filed patent applications. Jiandi Zhang is the founders of Yantai Zestern Biotechnique Co. LTD, a startup company with interest to commercialize the QDB technique and QDB plate.

Availability of data and materials

The data will be made available upon publication.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was approved by the Human Research Ethics Committee of Binzhou Medical University (2016-37).

Funding

This work is supported by the National Natural Science Foundation of China (81670855, 31671139) for sample collection and publication charges, Key Research and Development Plan of Shandong Province (2016GSF201100, 2017GSF218113, 2018GSF118131, 2018GSF118183) for MS experiments and IHC TMA analysis, Yantai science and technology plan (2017WS102) and Doctoral fund of Shandong Natural Science Foundation (ZR2017BC063) for antibody consumption, BZMC Scientific Research Foundation (BY2017KYQD08) for QDB analysis, Scientific Research Foundation for Returned Overseas Chinese Scholars of the Education Office of Heilongjiang Province (LC2009C21) for interpretation of data, Development Plan of Traditional Chinese Medicine Science in Shandong Province (2017-237) for general lab facility.

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Contributor Information

Yanping Zhu, Email: yanpingzhu1983@163.com.

Xiaoying Qi, Email: 1418599128@qq.com.

Cuicui Yu, Email: yhdyyhhf@126.com.

Shoujun Yu, Email: 84117961@qq.com.

Chao Zhang, Email: 651244893@qq.com.

Yuan Zhang, Email: 522684072@qq.com.

Xiuxiu Liu, Email: 1450625528@qq.com.

Yuxue Xu, Email: xuyuxue2320@163.com.

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Wenguo Jiang, Email: jiangwg@foxmail.com.

Geng Tian, Email: tiangeng@live.se.

Xuri Li, Email: slsherrylee2@gmail.com.

Jonas Bergquist, Email: jonas.bergquist@kemi.uu.se.

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Jia Mi, Phone: +86 535 6913395, Email: jia.mi@kemi.uu.se.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data will be made available upon publication.


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