Keywords
Expression
GAPDH
Biomarker
Submitted : 2024-08-26
Accepted : 2024-09-10
Published : 2024-09-25
Abstract
Objective: To investigate the expression levels of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and explore its prognostic value across 24 different human cancers. This investigation was conducted using comprehensive bioinformatics and in vitro approaches, incorporating multiple layers of analysis. Methods: GAPDH expression and methylation levels were assessed using bioinformatics tools and validated in cell lines through RNA-seq and targeted bisulfite-seq analyses. The potential prognostic significance of GAPDH was evaluated using the KM plotter. Additionally, cBioPortal was employed to investigate genetic alterations associated with this gene. Pathway analysis was conducted using DAVID. Furthermore, a correlation analysis between GAPDH expression and CD8+ T immune cells was performed using TIMER and CDT. Finally, a gene-drug interaction network analysis was conducted using Cytoscape to examine the relationship between GAPDH and various drugs. Results: GAPDH was found to be commonly upregulated in 24 types of human cancers, with its upregulation significantly correlated with poor relapse-free survival (RFS) and overall survival (OS) in BLCA, CESC, HNSC, KIRP, LIHC, and LUAD. This suggests that GAPDH plays a significant role in the development of these cancers. GAPDH upregulation was also associated with various clinicopathological features in patients with BLCA, CESC, HNSC, KIRP, LIHC, and LUAD. Pathway analysis revealed GAPDH’s involvement in diverse pathways. Additionally, notable correlations were observed between GAPDH expression and its promoter methylation level, genetic alterations, and CD8+ T immune cell levels. Moreover, several regulatory drugs targeting GAPDH were identified, with the potential to modulate its expression and potentially prevent conditions such as BLCA, CESC, HNSC, KIRP, LIHC, and LUAD. Conclusion: Based on our findings, GAPDH emerges as a promising diagnostic and prognostic biomarker for BLCA, CESC, HNSC, KIRP, LIHC, and LUAD.
References
Brucher BL, Kitajima M, Siewert JR, 2014, Undervalued Criteria in the Evaluation of Multimodal Trials for Upper GI Cancers. Cancer Invest, 32(10): 497–506. https://doi.org/10.3109/07357907.2014.958497
Feng Y, Spezia M, Huang S, et al., 2018, Breast Cancer Development and Progression: Risk Factors, Cancer Stem Cells, Signaling Pathways, Genomics, and Molecular Pathogenesis. Genes Dis, 5(2): 77–106. https://doi.org/10.1016/j.gendis.2018.05.001
Stein KD, Syrjala KL, Andrykowski MA, 2008, Physical and Psychological Long-Term and Late Effects of Cancer. Cancer, 112(11 Suppl): 2577–2592. https://doi.org/10.1002/cncr.23448
Anand P, Kunnumakkara AB, Sundaram C, et al., 2008, Cancer is A Preventable Disease That Requires Major Lifestyle Changes. Pharm Res, 25(9): 2097–2116. https://doi.org/10.1007/s11095-008-9661-9
Lahtz C, Pfeifer GP, 2011, Epigenetic Changes of DNA Repair Genes in Cancer. J Mol Cell Biol, 3(1): 51–58. https://doi.org/10.1093/jmcb/mjq053
Siegel RL, Miller KD, Jemal A, 2020, Cancer Statistics, 2020. CA Cancer J Clin, 70(1): 7–30. https://doi.org/10.3322/caac.21590
Khurana E, Fu Y, Chakravarty D, et al., 2016, Role of Non-Coding Sequence Variants in Cancer. Nat Rev Genet, 17(2): 93–108. https://doi.org/10.1038/nrg.2015.17
Sharma A, Jiang C, De S, 2018, Dissecting the Sources of Gene Expression Variation in A Pan-Cancer Analysis Identifies Novel Regulatory Mutations. Nucleic Acids Res, 46(9): 4370–4381. https://doi.org/10.1093/nar/gky271
Lazarev VF, Guzhova IV, Margulis BA, 2020, Glyceraldehyde-3-Phosphate Dehydrogenase is a Multifaceted Therapeutic Target. Pharmaceutics, 12(5): 416. https://doi.org/10.3390/pharmaceutics12050416
Mori R, Wang Q, Danenberg KD, et al., 2008, Both Beta-Actin and GAPDH are Useful Reference Genes for Normalization of Quantitative RT-PCR in Human FFPE Tissue Samples of Prostate Cancer. Prostate, 68(14): 1555–1560. https://doi.org/10.1002/pros.20815
Murthi P, Fitzpatrick E, Borg AJ, et al., 2008, GAPDH, 18S rRNA and YWHAZ are Suitable Endogenous Reference Genes for Relative Gene Expression Studies in Placental Tissues from Human Idiopathic Fetal Growth Restriction. Placenta, 29(9): 798–801. https://doi.org/10.1016/j.placenta.2008.06.007
Epner DE, Partin AW, Schalken JA, et al., 1993, Association of Glyceraldehyde-3-Phosphate Dehydrogenase Expression with Cell Motility and Metastatic Potential of Rat Prostatic Adenocarcinoma. Cancer Res, 53(9): 1995–1997.
Tang Z, Yuan S, Hu Y, et al., 2012, Over-Expression of GAPDH in Human Colorectal Carcinoma as A Preferred Target of 3-Bromopyruvate Propyl Ester. J Bioenerg Biomembr, 44(1): 117–125. https://doi.org/10.1007/s10863-012-9420-9
Seykora JT, Jih D, Elenitsas R, et al., 2003, Gene Expression Profiling of Melanocytic Lesions. Am J Dermatopathol, 25(1): 6–11. https://doi.org/10.1097/00000372-200302000-00002
Giricz O, Lauer-Fields JL, Fields GB, 2008, The Normalization of Gene Expression Data in Melanoma: Investigating the Use of Glyceraldehyde 3-Phosphate Dehydrogenase and 18S Ribosomal RNA as Internal Reference Genes for Quantitative Real-Time PCR. Anal Biochem, 380(1): 137–139. https://doi.org/10.1016/j.ab.2008.05.024
Zhou J, Dudley ME, Rosenberg SA, et al., 2005, Persistence of Multiple Tumor-Specific T-Cell Clones is Associated with Complete Tumor Regression in a Melanoma Patient Receiving Adoptive Cell Transfer Therapy. J Immunother, 28(1): 53–62. https://doi.org/10.1097/00002371-200501000-00007
Chandrashekar DS, Bashel B, Balasubramanya SAH, et al., 2017, UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia, 19(8): 649–658. https://doi.org/10.1016/j.neo.2017.05.002
Maciejczyk A, Szelachowska J, Czapiga B, et al., 2013, Elevated BUBR1 Expression is Associated with Poor Survival in Early Breast Cancer Patients: 15-Year Follow-Up Analysis. J Histochem Cytochem, 61(5): 330–339. https://doi.org/10.1369/0022155413480148
Koch A, De Meyer T, Jeschke J, et al., 2015, MEXPRESS: Visualizing Expression, DNA Methylation and Clinical TCGA Data. BMC Genomics, 16(1): 636. https://doi.org/10.1186/s12864-015-1847-z
Cerami E, Gao J, Dogrusoz U, et al., 2012, The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discov, 2(5): 401–404. https://doi.org/10.1158/2159-8290.CD-12-0095
Tang Z, Li C, Kang B, et al., 2017, GEPIA: A Web Server for Cancer and Normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res, 45(W1): W98–W102. https://doi.org/10.1093/nar/gkx247
von Mering C, Huynen M, Jaeggi D, et al., 2003, STRING: A Database of Predicted Functional Associations Between Proteins. Nucleic Acids Res, 31(1): 258–261. https://doi.org/10.1093/nar/gkg034
Shannon P, Markiel A, Ozier O, et al., 2003, Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res, 13(11): 2498–2504. https://doi.org/10.1101/gr.1239303
Huang DW, Sherman BT, Tan Q, et al., 2007, The DAVID Gene Functional Classification Tool: A Novel Biological Module-Centric Algorithm to Functionally Analyze Large Gene Lists. Genome Biol, 8(9): R183. https://doi.org/10.1186/gb-2007-8-9-r183
Li T, Fu J, Zeng Z, et al., 2020, TIMER2.0 for Analysis of Tumor-Infiltrating Immune Cells. Nucleic Acids Res, 48(W1): W509–W514. https://doi.org/10.1093/nar/gkaa407
Mattingly CJ, Colby GT, Forrest JN, et al., 2003, The Comparative Toxicogenomics Database (CTD). Environ Health Perspect, 111(6): 793–795. https://doi.org/10.1289/ehp.6028
Rio DC, Ares M Jr, Hannon GJ, et al., 2010, Purification of RNA Using TRIzol (TRI Reagent). Cold Spring Harb Protoc, 2010(6): pdb.prot5439. https://doi.org/10.1101/pdb.prot5439
Ghatak S, Muthukumaran RB, Nachimuthu SK, 2013, A Simple Method of Genomic DNA Extraction From Human Samples for PCR-RFLP Analysis. J Biomol Tech, 24(4): 224–231. https://doi.org/10.7171/jbt.13-2404-001
Ziai J, Gilbert HN, Foreman O, et al., 2018, CD8+ T Cell Infiltration in Breast and Colon Cancer: A histologic and Statistical Analysis. PLoS One, 13(1): e0190158. https://doi.org/10.1371/journal.pone.0190158
Warburg O, 1956, On the Origin of Cancer Cells. Science, 123(3191): 309–314. https://doi.org/10.1126/science.123.3191.309
Wang J, Li Y, Pan L, et al., 2021, Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) Moonlights as An Adhesin in Mycoplasma hyorhinis Adhesion to Epithelial Cells as well as A Plasminogen Receptor Mediating Extracellular Matrix Degradation. Vet Res, 52(1): 80. https://doi.org/10.1186/s13567-021-00952-8
Colell A, Green DR, Ricci JE, 2009, Novel Roles for GAPDH in Cell Death and Carcinogenesis. Cell Death Differ, 16(12): 1573–1581. https://doi.org/10.1038/cdd.2009.137
Ganapathy-Kanniappan S, Kunjithapatham R, Geschwind JF, 2012, Glyceraldehyde-3-Phosphate Dehydrogenase: A Promising Target for Molecular Therapy in Hepatocellular Carcinoma. Oncotarget, 3(9): 940–953. https://doi.org/10.18632/oncotarget.623
Caradec J, Sirab N, Revaud D, et al., 2010, Is GAPDH A Relevant Housekeeping Gene for Normalisation in Colorectal Cancer Experiments? Br J Cancer, 103(9): 1475–1476. https://doi.org/10.1038/sj.bjc.6605851
Guo C, Liu S, Sun MZ, 2013, Novel Insight Into The Role of GAPDH Playing in Tumor. Clin Transl Oncol, 15(3): 167–172. https://doi.org/10.1007/s12094-012-0924-x
Nakajima H, Amano W, Fujita A, et al., 2007, The Active Site Cysteine of the Proapoptotic Protein Glyceraldehyde-3-Phosphate Dehydrogenase is Essential in Oxidative Stress-Induced Aggregation and Cell Death. J Biol Chem, 282(36): 26562–26574. https://doi.org/10.1074/jbc.M704199200
Brisuda A, Pazourkova E, Soukup V, et al., 2016, Urinary Cell-Free DNA Quantification as Non-Invasive Biomarker in Patients with Bladder Cancer. Urol Int, 96(1): 25–31. https://doi.org/10.1159/000438828
Berger AC, Korkut A, Kanchi RS, et al., 2018, A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers. Cancer Cell, 33(4): 690–705.e9. https://doi.org/10.1016/j.ccell.2018.03.014
Song J, Ye A, Jiang E, et al., 2018, Reconstruction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network Based on Competitive Endogenous RNA in CESC. J Cell Biochem, 119(8): 6665–6673. https://doi.org/10.1002/jcb.26850
Sawyers CL, 2008, The Cancer Biomarker Problem. Nature, 452(7187): 548–552. https://doi.org/10.1038/nature06913
Labrousse-Arias D, Martinez-Alonso E, Corral-Escariz M, et al., 2017, VHL Promotes Immune Response Against Renal Cell Carcinoma via NF-KappaB-Dependent Regulation of VCAM-1. J Cell Biol, 216(3): 835–847. https://doi.org/10.1083/jcb.201608024
Hou Q, Li MY, Huang WT, et al., 2017, Association Between Three VEGF Polymorphisms and Renal Cell Carcinoma Susceptibility: A Meta-Analysis. Oncotarget, 8(30): 50061–50070. https://doi.org/10.18632/oncotarget.17833
Yang X, Minn I, Rowe SP, et al., 2015, Imaging of Carbonic Anhydrase IX with An 111In-labeled Dual-motif Inhibitor. Oncotarget, 6: 33733–33742. https://doi.org/10.18632/oncotarget.5254
Lai XM, Liu SY, Tsai YT, et al., 2017, HAF Mediates the Evasive Resistance of Anti-angiogenesis TKI Through Disrupting HIF-1Alpha and HIF-2Alpha Balance in Renal Cell Carcinoma. Oncotarget, 8(30): 49713–49724. https://doi.org/10.18632/oncotarget.17923
Alabiad MA, Harb OA, Abozaid M, et al., 2021, The Diagnostic and Prognostic Roles of Combined Expression of Novel Biomarkers in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma: An Immunohistochemical Study. Iran J Pathol, 16(2): 162–173. https://doi.org/10.30699/IJP.2020.130944.2452
van der Leun AM, Thommen DS, Schumacher TN, 2020, CD8+ T Cell States in Human Cancer: Insights from Single-Cell Analysis. Nat Rev Cancer, 20(4): 218–232. https://doi.org/10.1038/s41568-019-0235-4