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. 2024 Sep 25;14(10):248. doi: 10.1007/s13205-024-04093-5

Biological roles of THRAP3, STMN1 and GNA13 in human blood cancer cells

Suliman A Alsagaby 1,✉
PMCID: PMC11424602  PMID: 39345963

Abstract

Blood cancers, such as diffuse large B-cell lymphoma (DLBCL), Burkitt’s lymphoma (BL) and acute myeloid leukemia (AML), are aggressive neoplasms that are characterized by undesired clinical courses with dismal survival rates. The objective of the current work is to study the expression THRAP3, STMN1 and GNA13 in DLBCL, BL and AML, and to investigate if these proteins are implicated in the prognosis and progression of the blood cancers. Isolation of normal blood cells was performed using lymphoprep coupled with gradient centrifugation and magnetic beads. Flow-cytometric analysis showed high quality of the isolated cells. Western blotting identified THRAP3, STMN1 and GNA13 to be overexpressed in the blood cancer cells but hardly detected in normal blood cells from healthy donors. Consistently, investigations performed using genotype-tissue expression (GTEx) and gene expression profiling interactive analysis (GEPIA) showed that the three proteins had higher mRNA expression in various cancers compared with matched normal tissues (p ≤ 0.01). Furthermore, the up-regulated transcript expression of these proteins was a feature of short overall survival (OS; p ≤ 0.02) in patients with the blood cancers. Interestingly, functional profiling using gProfiler and protein–protein interaction network analysis using STRING with cytoscape reported THRAP3 to be associated with cancer-dependent proliferation and survival pathways (corrected p ≤ 0.05) and to interact with proteins (p = 1 × 10−16) implicated in tumourigenesis and chemotherapy resistance. Taken together, these findings indicated a possible implication of THRAP3, STMN1 and GNA13 in the progression and prognosis of the blood cancers. Additional work using clinical samples of the blood cancers is required to further investigate and validate the results reported here.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04093-5.

Keywords: THRAP3, STMN1, GNA13, Blood cancers

Introduction

Blood cancer encompasses heterogeneous groups of malignancies that initiates in lymphoid and/or myeloid linages (Guillerman et al. 2011). The Surveillance, Epidemiology, and End Results (SEER) estimated 80,550 new cases of non-Hodgkin lymphoma (NHL) and 59,610 new cases of leukemia to be reported in 2023 in the USA (SEER 2024). Furthermore, SEER estimated the death rates for NHL to reach 20,180 cases and for leukemia to be 23,710 cases form USA alone in 2023 (SEER 2024). DLBCL and BL are common types of B-lymphocytes cancers that are classified under NHL group of blood cancers (Shankland et al. 2012). Both DLBCL and BL are considered aggressive types of NHL and follow undesired clinical course with high prevalence rates of refractory and recurrence (Singh et al. 2020). The 5-year OS is 50% for DLBCL (Wight et al. 2018) and 61% for BL (Mburu et al. 2023). AML, which is a common kind of myeloid linage cancer, is an aggressive type of leukemia with poor clinical outcome; and dismal survival rate (5-year OS = 28%) (Newell et al. 2021). Despite the improvement made in the therapy of hematologic neoplasms, the clinical course and the 5-year OS remained unsatisfactory for DLBCL, BL and AMP patients (Alsagaby 2021; Alsagaby 2019; Alsagaby 2022; Alsagaby et al. 2022; Alsagaby et al. 2020; Ke et al. 2019; Newell et al. 2021; Rafei et al. 2019; Wang et al. 2020).

Cancer-associated proteins are of major interest for the search for therapeutic targets and diagnostic/prognostic markers in cancer (Calderwood et al. 2006; Frenzel et al. 2009; Li et al. 2020). Identification of cancer-associated proteins is also useful to characterize the molecular mechanisms through which cancer initiates and progresses (Bresnick et al. 2015; Iakoucheva et al. 2002; Otto et al. 2017). In our previous work on the proteomics of chronic lymphocytic leukemia (CLL; which is a kind of blood cancer different from DLBCL, BL and AML), we reported a number of CLL-related proteins, such as THRAP3, STMN1 and GNA13 (Alsagaby et al. 2021, 2014). THRAP3 is implicated in the production, splicing and stabilizing of RNA (Beli et al. 2012; Lande-Diner et al. 2013; Lee et al. 2010; Vohhodina et al. 2017). For example, THRAP3 plays roles in the synthesis, maturation and stability of mRNA of cell cycle genes such as cyclin D1 and RUNX2 (Bracken et al. 2008; Zhou et al. 2022). Our previous published studies showed THRAP3 to be associated with the progressive type of CLL, with urgent need of treatment and short OS (Alsagaby et al. 2014, 2021), suggesting roles of THRAP3 in cancer progression and prognosis (Ino et al. 2016; Wang et al. 2024b). STMN1 (also known as oncoprotein 18) is a microtubule depolymerizing protein that is implicated in mitotic spindle organization (Zeng et al. 2024) and cytokinesis (Menon et al. 2014); hence, it promotes the progression of cell cycle (Liu et al. 2019). In line with this, STMN1 has roles in tumourigenesis; and promotes metastasis and drug resistance in cancer cells (Li et al. 2024; Liu et al. 2021; Wang et al. 2024a). GNA13 (also known as G alpha 13) is transducer protein for different transmembrane signaling systems that lead to cell proliferation, cell adhesion, and cell migration (Krakstad et al. 2004; Yuan et al. 2016). In cancer cells, GNA13 promotes proliferation and metastasis and confers drug resistance (Ke et al. 2023; Rasheed et al. 2018; Shi et al. 2024; Wu et al. 2024; Zhang et al. 2018).

As discussed above, THRAP3, STMN1 and GNA13 were linked to cancer where they play roles in the proliferation, metastasis and drug resistance. In our previous proteomics studies of CLL, we identified THRAP3, STMN1, and GNA13 as CLL-related proteins. As proof of principle, THRAP3 was selected for further investigations and was found to be overexpressed in CLL cells compared with normal B cells and to predict rapid progression of CLL and short overall survival of CLL patients. Therefore, it is rationale to hypothesize that these proteins might be implicated in the progression and prognosis of other blood cancers. As a follow-up of our previous studies, the objectives of the current work were to (i) investigate whether THRAP3, STMN1, and GNA13 are overexpressed in other types of blood cancers (other than CLL) such as BL, DLBCL and AML compared with normal blood cells (PBMCs and B cells), (ii) investigate the prognostic potential of these proteins in the BL, DLBCL and AML, (iii) use in silico approaches to investigate the potential roles of these proteins in the progression of BL, DLBCL and AML.

Materials and methods

Cell culture

Blood cancer cell lines, OCI-LY3 (cell line of DLBCL), Daudi (cell line of BL) and THP1 (cell line of AML), were used for the study. The cell lines Daudi and THP1 were obtained from the American Type Culture Collection (ATCC), and the cell line OCI-LY3 was obtained from German Collection of Microorganisms and Cell Cultures GmbH (DSMZ). A cell culture (RPMI 1640 medium to which added 10% fetal calf serum (FCS), 40 μg/mL gentamycin, 100 U/mL penicillin, 100 μg/mL streptomycin sulfate, 2 mg/mL sodium bicarbonate and 4.5 mg/mL glucose) was used to cultivate the blood cancer cells at 37 °C in the presence of humidity and 5% CO2.

Isolation of PBMCs and B cells

Extraction of PBMCs from blood samples of healthy subjects was done using lymphoprep and gradient centrifugation. Briefly, blood samples were transferred into new tubes and diluted with an equal volume of phosphate-buffered saline (PBS; 0.01 M phosphate buffer, 2.7 mM KCl, 137 mM NaCl). Next, lymphoprep was slowly added to the bottom of the tubes, then the samples were centrifuged at 280 × g for 20 min. Post-centrifugation, the PBMCs layers were transferred into new tubes using plastic pipette. The isolation of B cells from the extracted PBMCs was performed using the materials and methods provided with the CD19-magnetic beads kit (Invitrogen: 111.34 D). Briefly, the extracted PBMCs were re-suspended with 5 ml PBS and were mixed with an appropriate amount of the beads (100 μl beads/1 × 107 target cells). The samples were then incubated on a roller for 20 min at 4 °C and then put in a magnet for 2 min at room temperature. The cell suspension containing the bead-non-bound PBMCs was transferred to new tubes (this cell population is mostly PBMCs minus the B cells). Next, the reagents and the instructions provided in DETACHaBEADS CD19 kit (Invitrogen: 125.06 D) were used to detach the beads from B cells. Briefly, the bead-bound cells (B cells) were mixed with the detaching reagent (40 μl detaching reagent per 1 ml sample) and were incubated on a roller for 45 min at 4 °C. Next, the samples were placed in a magnet for 2 min at room temperature. The cell suspension containing the beads-non-bound B cells was then transferred to new tubes.

Flow-cytometric analysis

As a quality control step for the isolation of normal blood cells (previous section), PBMCs and B cells were studied using flow-cytometry. Briefly, the cells (3 × 105 cells/mL) were subjected to a washing step using PBS and stained with anti-human CD19 antibody to which allophycocyanin was conjugated (Invitrogen). Next, cells were incubated for 10 min at room temperature in dark and then were washed with PBS. Finally, FACS CantoII flow cytometer (Becton Dickinson) was used to study the cells, and FACSdiva software (Becton Dickinson) was used for the analysis.

Pooling of normal blood cells

PBMCs from each four healthy donors were pooled (making two pooled PBMCs from blood samples of eight healthy donors; Fig. 1A). B cells from each three healthy subjects were pooled (making three pooled B cells samples from nine healthy subjects; Fig. 1B). These pooled samples were used as control samples for the subsequent analysis.

Fig. 1.

Fig. 1

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Diagram of pooling normal cells. This figure shows how the pooling of normal PBMCs samples (A) and normal B cells’ samples was performed (B)

Western blotting

Western blotting analysis was conducted using the protocol and materials provided with the XCell II Blot module (Invitrogen). Briefly, proteins extract from the blood cancer cells and from normal blood cells were run under reducing conditions on gel electrophoresis (precast NuPAGE 4–12% Bis–Tris Zoom gels from Invitrogen). Next, proteins were transferred onto a membrane (polyvinylidene fluoride (PVDF) membrane from GE Healthcare) using NuPAGE transfer buffer (1X) (Invitrogen) with 10% (v/v) methanol by electroblotting for 90 min at 30 V and 170 A. Next, the PVDF was incubated with a blocking buffer (0.2% (w/v) I-Block, 0.1% (v/v) Tween-20 and 0.4% (w/v) sodium azide) for 2 h on a shaker at room temperature. Then, the PVDF membrane was incubated with diluted primary antibodies for the specific detection of proteins of interest. Protein detection was conducted using near-infrared western blot detection protocol (LI-COR). Specific detection was done for four proteins using specific primary antibodies: anti-actin antibody (A-2066; used at 1/1000) from Sigma, anti-THRAP3 antibody (ab71985; used at 1/1000) from Abcam, anti-GNA13 antibody (sc-293424; used at 1/1000) from Sant Cruz Biotechnology, and anti-STMN1 antibody (sc-48362; used at 1/1000) from Sant Cruz Biotechnology.

Transcriptomics data sets

Two transcriptomics data sets from Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) (Clough et al. 2016) and from The Cancer Genomics Atlas (TCGA; https://www.cancer.gov/ccg/research/genome-sequencing/tcga) (Weinstein et al. 2013) were used for the OS analysis (Kaplan–Meier curves) and for correlation coefficient analysis (Pearson score; PS). The data set GSE181063 was obtained from GEO; this data set was generated from total RNA extracts that were isolated form formalin-fixed, paraffin-embedded (FFPE) biopsies of lymph node from 1315 patients; 1227 were diagnosed with DLBCL and 88 were diagnosed with BL. The mRNA analysis of the transcriptomics data set (GSE181063) was performed using DNA microarray-based transcriptomics approach, where all mRNAs were relatively quantified. The DNA microarray platform used to generate the data set GSE181063 was Illumina Whole-Genome DASL HT (version 4) array. The second data set was obtained from TCGA; the data set’s name is “Acute Myeloid Leukemia (TCGA, NEJM 2013)”. This data set was generated from total RNA extracts that were isolated from AML blasts of 173 AML patients. The mRNA analysis of this data set was performed using the RNA sequencing-based transcriptomics approach where the number of sequenced copies of mRNA reflects its quantity. The RNA sequencing platform used to generate this data set was Illumina RNA sequencing (version 2) RSEM. For the analyses that aimed to study the expression of genes of interest in cancer tissues and matched normal tissues, the transcriptomics data sets that were generated from malignant samples and matched normal samples by TCGA and Genotype-Tissue Expression (GTEx; https://gtexportal.org/home/aboutGTEx) (Lonsdale et al. 2013) were computed through Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/) (Tang et al. 2017) and used.

Functional enrichment

The gProfiler (https://biit.cs.ut.ee/gprofiler/gost) (Reimand et al. 2016) was used to conduct functional enrichment of genes of interest. Four popular databases were utilized for this analysis: Gene Ontology (GO) database (http://geneontology.org/) (Gene Ontology 2004), KEGG pathway database (https://www.genome.jp/kegg/) (Kanehisa et al. 2023, 2000), Reactome pathways database (https://reactome.org/) (Croft et al. 2014), and WikiPathways database (https://www.wikipathways.org/index.php/WikiPathways) (Kelder et al. 2012). The following settings were selected for the analysis: organism was “homo sapiens”; the statistical domain scope was “only annotated genes”; and the correct p value was ≤ 0.05. Benjamini–Hochberg method was applied for the calculation of corrected p value (false-discovery rate, FDR).

Protein–protein interaction network analysis

The “Search Tool for the Retrieval of Interacting Genes” (STRING; https://string-db.org/) (Szklarczyk et al. 2019) was employed for protein–protein interaction (PPI) analysis and for the network construction. The analysis was conducted under a number of settings: organism was “homo sapiens”; the network type was “full STRING network”; the meaning of network edges was based on confidence; all active sources for interaction were included; and the PPIs enrichment score was set at < 0.001. Cytoscape (version 3.4.0; https://cytoscape.org/) (Shannon et al. 2003) was then used to visualize the PPIs network file that was generated from STRING.

Statistical analysis

Excel software (version 16) was used for the calculation of Pearson coefficient score (PS) and its p value. Prism Graphpad software (version 7) was used for performing Kaplan–Meier curve analysis and for the calculation of hazard ration (HR) and p value on the basis of the Log-rank test. Benjamini–Hochberg method was used for the calculation of the corrected p value (FDR) of the functional enrichment analysis using gProfiler. One-way ANOVA test was applied for the calculation of p value of the deferential expression in cancer tissues versus matched normal tissues using GEPIA.

Results

Normal blood cells

Normal PBMCs and B cells were used as control cells. Blood samples from 9 healthy donors were used from which PBMCs and B cells were extracted. First, PBMCs were isolated using lymphoprep and gradient centrifugation (Fig. 2A and B). Next, positive isolation of CD19-positive cells (B cells; Fig. 2C and D) was applied using magnetic beads method to the PBMCs (isolated earlier). Flow-cytometric analysis demonstrates the enrichment of B cells that increased from 10.5% in PBMC population (Fig. 2B) to 91.6% in B-cell population (Fig. 2D).

Fig. 2.

Fig. 2

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Flow-cytometric analysis of the isolation of PBMCs and B cells. Lymphoprep and gradient centrifugation were applied on blood samples from healthy donors to extract PBMCs (A and B). Next, B cells were isolated from PBMCs using CD19 magnetic beads (C and D). LP: lymphocytes population; GMP: granulocytes and monocytes population

Association of THRAP3, STMN1 and GNA13 with blood cancer cells

To investigate if preferential expression of THRAP3, STMN1 and GNA13 exist in blood cancer cells, the expression of these proteins was investigated in normal blood samples (control samples) and in three types of blood cancer: DLBCL cells (OCI-LY3), BL cells (Daudi) and AML cells (THP1), using western blotting (Fig. 3). The control samples were two pooled PBMC samples (each one was from four different healthy donors) and three B-cell samples (each one was from three different healthy donors). THRAP (109 KDa), GNA13 (44 KDa) and STMN1 (18 KDa) were evidently expressed in the blood cancer cells but were almost not detected in the normal blood cells, although the protein amount loaded from the normal blood cells was fourfold higher (20 μg) than that from the blood cancer cells (5 μg) as reflected by β-actin (45 KDa).

Fig. 3.

Fig. 3

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Protein expression of THRAP3, STMN1 and GNA13 in blood cancer cells and normal blood cells. Western blotting was employed to study the expression of THRAP3, STMN1 and GNA13 in DLBCL cells (OCI-LY3), BL cells (Daudi), AML cells (THP-1) and normal blood cells. Normal blood cells: #1 and #2 are pooled PBMC samples; #3, #4 and #5 are pooled B-cell samples

Next, the three proteins (THRAP3, GNA13 and STMN1) were further studied at the level of transcription (mRNA expression) in malignant lymphoma tissue versus matched normal tissue using TCGA and GTEx transcriptomics data sets through GEPIA. Interestingly, the findings showed increased mRNA expression of the three proteins in malignant lymphoma tissue compared with matched normal tissue (Fig. 4). Statistical significance of increased of transcript expression was found for THARP3 (Fig. 4A; n = 384; p = 0.01) and STMN1 (Fig. 4B; n = 384; p = 0.01).

Fig. 4.

Fig. 4

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Transcript expression of THRAP3, STMN1 and GNA13 in malignant lymphoid tissues (MLT) and matched normal tissues. The analysis was conducted using TCGA and GTEx data operated through GEPIA. THRAP3 and STMN1 returned p value ≤ 0.01, whereas GNA13 showed p value > 0.05

Next, effort was made to investigate whether other types of cancer exhibit high-expression of the transcripts of the three proteins. For this analysis, TCGA and GTEx data computed through GEPIA were also used. Excitingly, the findings demonstrated a significant association of the mRNA expression of THRAP3, GNA13 and STMN1 with various kinds of cancer as opposed to matched normal tissues (Figs. 5, 6 and 7). All findings of the Figs. 5, 6 and 7 were reported with a cutoff p value ≤ 0.01. Supplementary information 1–3 also show the transcript expression of these proteins in other types of cancer versus matched normal tissues regardless of significant association. Collectively, the results reported here identified an association of THRAP3, GNA13 and STMN1 with the blood cancer cells and with some other cancers, suggesting THRAP3, GNA13 and STMN1 to be cancer-associated proteins.

Fig. 5.

Fig. 5

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Transcript expression of THRAP3 in solid malignancies and matched normal tissues. TCGA and GTEx data computed through GEPIA were used for the analysis. All results shown in this figure are reported with p ≤ 0.01. CHOL cholangiocarcinoma, PAAD pancreatic adenocarcinoma, THYM thymoma

Fig. 6.

Fig. 6

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Transcript expression of STMN1 in solid neoplasms and matched normal tissues. TCGA and GTEx data computed through GEPIA were used for the analysis. All findings demonstrated in this figure are reported with p ≤ 0.01. BLCA bladder urothelial carcinoma, BRCA breast invasive carcinoma, COAD colon adenocarcinoma; LIHC liver hepatocellular carcinoma, PAAD pancreatic adenocarcinoma; SKCM skin cutaneous melanoma, STAD stomach adenocarcinoma, THYM thymoma

Fig. 7.

Fig. 7

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Transcript expression of GNA13 in solid cancers and matched normal tissues. This analysis was done using TCGA and GTEx data through GEPIA. All comparisons presented in this figure are reported with p ≤ 0.01. CHOL cholangiocarcinoma, ESCA esophageal carcinoma, GBM glioblastoma multiforme, LAML acute myeloid leukemia, LGG brain lower grade glioma, PAAD pancreatic adenocarcinoma, SKCM skin cutaneous melanoma, STAD stomach adenocarcinoma

Implication of THRAP3, STMN1 and GNA13 in the progression and prognosis of blood cancers

The results reported in the previous section were encouraging to investigate whether THRAP3, STMN1 and GNA13 are involved in the progression of DLBCL, BL and AML. Overall survival (OS) of cancer patients is a useful tool for indicating whether a cancer is progressive (aggressive; poor prognosis) or indolent (stable; good prognosis). Short OS is associated with progressive cancers, whereas long OS is characteristics of indolent cancers. Previously published transcriptomics data sets with available OS information from GEO and TCGA were utilized for this analysis (from GEO: GSE181063; from TCGA: Acute Myeloid Leukemia (TCGA, NEJM 2013)). Increased expression of STMN1 was indicative of shorter OS in DLBCL patients; median OS of the high-expression group was 5.8 years compared with 10.2 years for the low-expression group (p < 0.0001, HR = 1.5, n = 1227; Fig. 8A). Similar results were also found in BL patients where increased expression of STMN1 was predictive of short OS; median OS of the high-expression group was 0.62 years compared with undefined in the low-expression group (p = 0.007, HR = 2.4, n = 88; Fig. 8B). High-expression of THRAP3 also predicted shorter OS in DLBCL patients; median OS of the high-expression group was 6.2 years compared with 8.7 years for the low-expression group (p = 0.001, HR = 1.2, n = 1227; Fig. 8C). Likewise, increased expression of THRAP3 was also informative of shorter OS in BL patients; median OS of the high-expression group was 0.8 years compared with undefined in the low-expression group (p = 0.03, HR = 2.1, n = 88; Fig. 8D). Similarly, high-expression of THRAP3 indicated shorter OS in AML patients; median OS of the high-expression group was 1.3 years compared with 2.3 years for the low-expression group (p = 0.01, HR = 1.6, n = 173; Fig. 8E). Increased expression of GNA13 predicted shorter OS only in AML patients where median OS of the high-expression group was 1.3 compared with 2.2 years for the low-expression group (p = 0.02 HR = 1.6, n = 173; Fig. 8F). The association of THRAP3, STMN1 and GNA13 with the blood cancer cells as opposed to normal blood cells and the association of increased transcript expression of the proteins with shorter OS in DLBCL, BL and AML underscore a potential involvement of these proteins in the progression and prognosis of these blood cancers.

Fig. 8.

Fig. 8

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Association of the transcript expression of THRAP3, STMN1 and GNA13 with short OS in the blood cancers. Two transcriptomics data sets from GEO (data set accession number: GSE181063 for DLBCL and BL) and from TCGA (name of data set: Acute Myeloid Leukemia (TCGA, NEJM 2013) for AML) were used for this analysis. DLBCL diffuse large B-cell lymphoma, BL Burkitt’s lymphoma, AML acute myeloid leukemia, OS overall survival, HR hazard ration

Functional profiling of THRAP3 in blood cancer cells

Of the three proteins, the increased expression of THRAP3 was a feature of shorter OS in all the studied kinds of blood cancer. Therefore, it was selected for further bioinformatics-based functional analysis to shed some light onto its possible pathological roles in the blood cancers. Correlation coefficient analysis using Pearson score (PS) was performed on the transcriptomics data sets (from GEO: GSE181063; from TCGA: Acute Myeloid Leukemia (TCGA, NEJM 2013)) to detect gene candidates whose expression correlate with the expression of THRAP3 in DLBCL, BL and AML. As a result, correlation with THRAP3 (Fig. 9A) was found for 682 genes in DLBCL (PS ≥ 0.50, p < 0.00001, count of patients = 1227), 1884 genes in BL (PS ≥ 0.50, p < 0.00001, count of patients = 88) and 661 genes in AML (PS ≥ 0.50, p < 0.00001, count of patients = 173). Next, functional profiling analysis of these genes was conducted using gProfiler against four different databases (WP, KEGG, Reactome, GO) and are shown in supplementary information 4–6. Interestingly, in all the studied blood cancers, mRNA processing, mRNA splicing and cell cycle were the top pathways enriched by the genes that correlated with THRAP3. Furthermore, ATP production pathways, such as aerobic respiration, oxidative phosphorylation, citrate cycle and glucose metabolism, and proliferation/survival pathways such as telomere maintenance, signaling by Rho GTPases, TOR signaling, activation of NF-kappaB, NOTCH signaling, B-cell receptor signaling, anti-apoptosis, Wnt signaling, and FGFR2 signaling were also enriched in one or more of the blood cancers. All enriched pathways were reported with corrected p value (FDR) ≤ 0.05. Figure 9B shows the enriched pathways by the genes that correlated with THRAP3 in BL.

Fig. 9.

Fig. 9

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Functional profiling of THRAP3 in blood cancer cells. Pearson score (correlation coefficient) and the transcriptomics data sets (GSE181063 and Acute Myeloid Leukemia (TCGA, NEJM 2013)) from GEO and TCGA were used to search for genes whose expression correlate with the transcript expression of THAP3 in DLBCL, BL and AML (A). Next, functional enrichment of the genes that correlated with THRAP3 expression in BL was performed using gProfiler (B). DLBCL diffuse large B-cell lymphoma; BL Burkitt’s lymphoma, AML acute myeloid leukemia

The pathway “spliceosome”, which is needed to remove introns from mRNA precursor and join exon to produce a mature mRNA molecule, was among the top pathways that were enriched in BL and DLBCL. Figure 10 shows a KEGG-based map of the pathway (KEGG; entry number: hsa03040; corrected p value (FDR) of the pathway enrichment = 2 × 10−7) and color-highlights the spliceosome genes that were found to correlate with THRAP3 in BL. For example, small nuclear ribonucleoprotein-associated proteins B (SNRPBI, which is an oncoprotein) correlated with THRAP3 (PS = 0.8), and is a key component of the five small nuclear ribonucleoproteins (snRNPs; U1, U2, U4 and U5) that majorly constitute the spliceosome pathway. Another interesting example is cell division cycle 5-like protein (CDCL5, which is a tumorigenic protein) had a correlation score with THRAP3 (PS = 0.7) and is part of the Prep19 complex of the spliceosome. Another interesting pathway that was enriched in BL is B-cell receptor (BCR) signaling pathway, which is the key pathway for the activation, proliferation and survival of B cells. Figure 11 shows a KEGG-based map of the BCR signaling pathway (KEGG; entry number: hsa04662; corrected p value (FDR) of the pathway enrichment = 0.01) and color-highlights the pathway genes that were found to correlate with THRAP3 in BL. For instance, CD79 (correlation with THRAP3 (PS) = 0.8) and CD79 (correlation with THRAP3 (PS) = 0.7) are major components of the B-cell receptor through which the activation of the pathway happens. In addition, phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit (PIK3CA; correlation with THRAP3 (PS) = 0.6) which is a downstream effector of BCR signaling that is needed for the activation of AKT serine/threonine kinase 1 (AKT1, correlation with THRAP3 (PS) = 0.8) leading to the survival of B cells.

Fig. 10.

Fig. 10

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Illustration of the genes that correlated with THRAP3 in BL and were assigned to the spliceosome pathway (KEGG; entry number: hsa03040). The genes that were color-highlighted are those that correlated with THRAP3 in BL. The color range is indicative of the correlation coefficient (Pearson score)

Fig. 11.

Fig. 11

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Demonstration of the genes that correlated with THRAP3 in BL and were assigned to the B-cell receptor signaling pathway (KEGG; entry number: hsa04662). The genes that were color-highlighted are those that correlated with THRAP3 in BL. The color range is indicative of the correlation coefficient (Pearson score)

Protein–protein interaction network analysis of THRAP3

Investigation was conducted to search for interaction partners of THRAP3 among the protein product of the genes that correlated with THRAP3 transcript. In BL, large number of genes (n = 1884) showed significant correlation with THRAP3 transcript compared with the other two blood cancers. Therefore, BL was selected for the present analysis. PPI network analysis was performed using “STRING” and was visualized using Cytoscape. In the PPI network (Fig. 12A), node are proteins, edges are interaction between proteins, and number of binding partners of a protein is indicated by node degree. The investigation reported that THRAP3 interacts (edges) with 43 proteins (node degree) and that 260 interactions (edges) were found between the 43 proteins, making the total number of edges to be 303 compared with only 27 expected. The average node degree = 13.8; all the data were reported with PPI enrichment p value = 1 × 10−16. The functional enrichments analysis by “STRING” presented in chord plot (Fig. 12B) reported the 43 proteins (with which THRAP3 interacts) to play roles in RNA splicing (corrected p value = 3.44 × 10−25), RNA metabolic process (corrected p value = 3.92 × 10−22) and genes expression (corrected p value = 1.49 × 10−21).

Fig. 12.

Fig. 12

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Protein–protein interaction network analysis of THRAP3. STRING was used to search for THRAP3 interaction partners among the protein product of the genes that were found to correlate with THRAP3 in BL (data set GSE181063). Next, the PPIs network was visualized using Cytoscape (A). Functional profiling of the THRAP3 interactors (n = 43 proteins) was performed using STRING and showed in cord plot (B)

Discussion

The present work reported THRAP3, STMN1 and GNA13 to be highly expressed in blood cancer cells (DLBCL cells, BL cells and AML cells) but hardly detected in normal blood cells from healthy individuals. In our previous proteomics work on CLL cells, we found a potential link of the three proteins (THRAP3, STMN1 and GNA13) with the disease (Alsagaby et al. 2014). We found THRAP3 in particular to be highly expressed in CLL cells as compared to normal B cells that were isolated from healthy donors (Alsagaby et al. 2021). The results shown here support our previously reported findings (Alsagaby et al. 2021). Furthermore, the present work found that the increased expression of THRAP3, STMN1 and GNA13 to be not limited to blood cancers but to be a common feature of some other types of cancer. Similar results by other scientists were reported, where overexpression of STMN1 was shown in a number of cancers; such as AML (Vicari et al. 2022), multiple myeloma (Wang et al. 2023), gastric cancer (Bai et al. 2017), lung cancer (Bao et al. 2017) and pancreatic cancer (Bao et al. 2017). Likewise, GNA13 was found to be highly expressed in different cancers like B-cell lymphomas (Bao et al. 2017; Shimono et al. 2018), liver cancer (Xu et al. 2016), and gastric cancer (Zhang et al. 2016). Collectively, the association of THARP3, STMN1 and GNA13 with the blood cancers and some other solid cancers shown in this study supports previously published reports and argues that these three proteins might be cancer-associated proteins.

Increased transcript expression of THARP3, STMN1 and GNA13 was found here to be associated with short OS (a feature of rapid progression and poor prognosis) of the blood cancers. Agreeing with this finding, our previous study found increased expression of THRAP3 at the levels of protein and mRNA to be associated with early need of therapy and short OS of CLL, which are features of a progressive/aggressive type of CLL (Alsagaby et al. 2014, 2021). Overexpression of these three proteins was reported to combine with rapid progression and poor prognosis of many kinds of cancer. For instance, high expression of STMN1 identifies rapid progression and poor prognosis of gastric cancer (Bai et al. 2017), pancreatic cancer (Li et al. 2015), lung cancer (Bao et al. 2017) and AML (Vicari et al. 2022). Similarly, increased expression of GNA13 was also reported to identify fast progression and poor prognosis of follicular lymphoma (Shimono et al. 2018), liver cancer (Shimono et al. 2018) and esophageal cancer (Pan et al. 2022). Taken together, the findings reported here and by others support each other and indicate a possible implication these three proteins in the progression and undesired prognosis of cancer.

The association of THRAP3, STMN1 and GNA13 with the blood cancer cells and with short OS in patients with the blood cancers, reported here, highlights a possible oncogenic roles of these proteins. In line with this view, STMN1 was reported to drive proliferation, survival and drug resistance of lung cancer, endometrial carcinoma, thyroid carcinoma and ovarian cancer (He et al. 2016; Li et al. 2017b; Nie et al. 2022; Pan et al. 2021; Zhang et al. 2020a). Furthermore, targeting STMN1 was shown to inhibit proliferation and metastasis of pancreatic cancer (Li et al. 2014; Zhu et al. 2018). Similarly, GNA13 was reported to promote drug resistance and tumor growth in squamous cell cancers, breast cancer and colorectal cancer (Nie et al. 2022; Rasheed et al. 2015; Zhang et al. 2018). In multiple myeloma, THRAP3 was found to confer drug resistance and tumor growth (Chen et al. 2023). Taken these findings together, THRAP3, STMN1 and GNA13 are perhaps involved tumourigenesis of different cancers (including blood cancers) and may represent good therapeutic targets.

The functional profiling and PPI analysis of THRAP3 supports and to some extent explain its association with blood cancer cells and with short OS in patients diagnosed with DLBCL, BL and AML. THRAP3 was found to significantly associate with cancer-dependent proliferation and survival pathways, such as spliceosome pathway, hippo pathway, alternative splicing, NF-kappaB pathway, BCR signaling pathway, FOXO pathway and mTOR pathway. The aforementioned pathways are known to drive the growth and survival of cancer cells and have been recognized as good targets for cancer therapy (Burger et al. 2018; Calses et al. 2019; El Marabti et al. 2018; Farhan et al. 2017; Guertin et al. 2007; Naugler et al. 2008). Specifically, SNRPB1 and CDCL5, which were found here to correlate with THRAP3, are major key players in the spliceosome pathway and have been implicated in tumorigenesis and cancer progression; and considered promising therapeutic targets (Chen et al. 2016; Li et al. 2017a; Wu et al. 2022; Zhang et al. 2020c; Zhu et al. 2020). Furthermore, CD79A, CD79B, PIK3CA and AKT1, which were found here to also correlate with THRAP3, are key components of the BCR signaling pathway, and are known to drive the growth and progression of various types of B-cell lymphoma, and are viewed as attractive therapeutic targets (Cui et al. 2017; Jahn et al. 2015; Tkachenko et al. 2023; Wang et al. 2017). PPI analysis found THRAP3 to interact with 43 proteins (PPI analysis) that are implicated in RNA processing, RNA splicing and RNA synthesis. Some of the proteins that were found to interact with THRAP3 were previously implicated in tumorigenesis and chemotherapy resistance. For instance, nuclear mediator of RNA polymerase II transcription subunit 12 (MED12) activates TGF beta signaling and activate the expression of TGF beta target genes like vimentin, promoting chemotherapy resistance, and reduced the expression of the cell cycle inhibitor P16, supporting tumorigenesis (Gonzalez et al. 2022; Zhang et al. 2020b). In addition, nuclear receptor coactivator 3 (NCOA3) drives tumorigenesis through modulating the ErbB, AKT, ERK, and β-catenin pathways, and induces chemotherapy resistance by activating expression of other drug resistance-promoting transcriptional factors such as E2F-1, AP-1, NF-κB and STAT6 (Li et al. 2018, 2022). Furthermore, pre-mRNA-processing factor 6 (PRPF6) induces tumorigenesis via enhancing the activation of oncogenic androgen receptor signaling; and promotes chemotherapy resistance through SNHG16-L/CEBPB/GATA3 axis and by activating the signaling of NFκB, ERK, and c-MYC (Liu et al. 2021; Song et al. 2020; Wang et al. 2021). Chromodomain-helicase-DNA-binding protein 4 (CHD4) drive tumorigenesis and chemotherapy resistance by silencing the expression of tumor suppressor genes, such as WIF1, TIMP2, TIMP3, P16 and MLH1 (D'Alesio et al. 2016; Xia et al. 2017; Xu et al. 2020). Overall, the association of THRAP3 with the above cancer-dependent proliferation and survival pathways and the interaction of THRAP3 with tumorigenesis and chemotherapy resistance-promoting proteins may explain the association of THRAP3 with the blood cancer cells and with short OS of the blood cancers, and propose THRAP3 as therapeutic target.

Although THRAP3 interacts with MED12, NCOA3, PRPF6 and CHD4, it remains unclear how this interaction is involved in the mechanism of action through which tumorigenesis and chemotherapy resistance occur. However, it perhaps could be speculated that transcription, alternative mRNA splicing, and mRNA stabilization are implicated in the mechanism of action because THRAP3, MED12, NCOA3, PRPF6 and CHD4 are key player in these biological processes.

Overall, for major points could perhaps explain why THARP3, STMN1 and GNA13 are important in the studied blood cancers. First, they are overexpressed in the blood cancer cells compared with normal blood cells. Second, they are associated with rapid progression of the blood cancers and are predictive of poor prognosis. Third, previous studies reported that these proteins drive the proliferation, survival and drug resistance of cancer cells. Fourth, THRAP3, in particular, was found here to be associated with pathways and to interact with proteins that drive the proliferation and survival of the blood cancer cells. These four points support each other and give insights onto each other. For example, the association of these three proteins with fast progression and poor prognosis of the blood cancers is possibly explainable by their overexpression in the blood cancer cells compared with normal blood cells and by their roles in driving growth, survival and drug resistance of cancer cells. Collectively, these four points provide a rational basis for the implication of THARP3, STMN1 and GNA13 in the progression and prognosis of the blood cancers; and argue that these proteins, in particular THRAP3, are of major interest to be studied further to evaluate their utilities as prognostic markers and therapeutic targets.

Some limitations should be considered when reading the present work. For instance, the expression analysis of THRAP3, STMN1 and GNA13 using western blotting was performed on cell lines of BL, DLBCL and AML, rather than clinical samples from blood cancer patients. Therefore, future work should validate the expression of the three proteins in clinical samples from BL, DLBCL and AML patients using protein-measuring techniques, such as flow-cytometry and immunohistochemistry. In addition, it would also be interesting to relate the measured expression of THRAP3, STMN1 and GNA13 to the clinical outcomes of the patients; such as overall survival, progression-free survival, cancer stage and response to chemotherapy (e.g., good response, refractory and relapse) in order to validate their prognostic utilities. Another limitation of the study is that the functional implication of THRAP3, STMN1 and GNA13 in the progression of BL, DLBCL and AML was performed using only in silico approaches. Therefore, future work should focus on performing functional analysis using in vitro and in vivo methods (for example, specific inhibitors or siRNA of THRAP3, STMN1 and GNA13) to validate the possible therapeutic utility and involvement of the three proteins in the progression of blood cancer.

Conclusion

Overall, the current study reported an overexpression of THRAP3, STMN1 and GNA13 in blood cancer cells but not in normal blood cells as shown by the western blotting analysis. Furthermore, the transcript expression of these proteins was also found to be associated with different kinds of cancer as reported by the analysis conducted using TCGA and GTEx computed through GEPIA, suggesting these proteins to be cancer-associated proteins. In addition, the Kaplan–Meier curve analysis showed the up-regulated mRNA expression of THRAP3, STMN1 and GNA13 to be predictive of poor prognosis (short OS) in patients with the blood cancers, highlighting a possible implication of these proteins in the progression and prognosis of the blood cancers. Functional profiling conducted by gProfiler coupled with four databases (WP, KEGG, Reactome, and GO) and PPI analysis using STRING with cytoscape reported THRAP3 to be associated with cancer-dependent proliferation and survival pathways, providing insights into the possible implication of THRAP3 in the progression of the blood cancers. Additional work using clinical samples from blood cancer patients is needed to further investigate and validate the potential of THRAP3, STMN1 and GNA13 as prognostic markers in the blood cancers. Moreover, additional in vitro and in vivo functional investigations of THRAP3 are required to validate its potential implication in the progression of the blood cancers and its utility as therapeutic target.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 2591 KB) (2.5MB, docx)

Acknowledgements

The author would like to thank the Deanship of Graduate Studies and Scientific Research at Majmaah University in Saudi Arabia for supporting this study under a project number [R-2024-1303].

Data availability

Data are availabe as supplementary materials.

Declarations

Conflict of interests

The author has no competing interests to declare that are relevant to the content of this article.

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Supplementary Materials

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Data Availability Statement

Data are availabe as supplementary materials.

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