Bioinformatics Analysis and Identification of Potential Genes Associated with Pathogenesis and Prognosis of Gastric Cancer

Abstract

Objective

Gastric cancer (GC) is a deadly cancer and a challenging public health problem globally. This study aimed to analyze potential genes associated with pathogenesis and prognosis of gastric cancer.

Methods

This work selected the overlapping differentially expressed genes (DEGs) in GC from four datasets, the GSE29272, GSE29998, GSE54129 and GSE118916 Gene Expression Omnibus databases. These DEGs were used to carry out comprehensive bioinformatic analysis to analyze the related functions and pathways enriched, the relative expression levels and immune infiltrates, the prognostic characteristics and the interaction network.

Results

In total, 55 DEGs increased while 98 decreased in their expression levels. For those DEGs with increased expression, they were mostly concentrated on “focal adhesion” and “ECM-receptor interaction”, whereas DEGs with decreased expression were mostly associated with “gastric acid secretion” and “drug metabolism cytochrome P450”. MCODE and ClueGO results were then integrated to screen 10 hub genes, which were FN1, COL1A1, COL3A1, BGN, TIMP1, COL1A2, LUM, VCAN, COL5A2 and SPP1. Survival analysis revealed that higher expression of the ten hub genes significantly predicted lower overall survival of GC patients. TIMP1 was most significantly related to neutrophils, CD8⁺ T cells, as well as dendritic cells, while LUM was most significantly related to macrophages.

Conclusion

Immunohistochemistry results and functional testing showed that the expression of COL5A2 was elevated in GC and that it might be a key gene in GC tumorigenesis.

Keywords

gastric cancer / differentially expressed genes / bioinformatics analysis / prognosis / immune infiltrate

References

[1] BrayF, FerlayJ, SoerjomataramI, et al.. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424

[2] WangTT, ZhaoY L, PengL S, et al.. Tumour-activated neutrophils in gastric cancer foster immune suppression and disease progression through GM-CSF-PD-L1 pathway. Gut, 2017, 66(11): 1900-1911

[3] YoonH, KimN. Diagnosis and management of high risk group for gastric cancer. Gut Liver, 2015, 9(1): 5-17

[4] JiangP, LiuXS. Big data mining yields novel insights on cancer. Nat Genet, 2015, 47(2): 103-104

[5] CloughE, BarrettT. The Gene Expression Omnibus Database. Methods Mol Biol, 2016, 1418: 93-110

[6] SongE, SongW, RenM, et al.. Identification of potential crucial genes associated with carcinogenesis of clear cell renal cell carcinoma. J Cell Biochem, 2018, 119(7): 5163-5174

[7] BarrettT, WilhiteSE, LedouxP, et al.. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res, 2013, 41: D991-D995 Database issue

[8] DennisGJ, ShermanBT, HosackD A, et al.. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol, 2003, 4(5): P3

[9] AshburnerM, BallCA, BlakeJA, et al.. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000, 25(1): 25-29

[10] KanehisaM, FurumichiM, TanabeM, et al.. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res, 2017, 45(D1): D353-D361

[11] FranceschiniA, SzklarczykD, FrankildS, et al.. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res, 2013, 41: D808-D815 Database issue

[12] YuG, WangL G, HanY, et al.. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 2012, 16(5): 284-287

[13] BindeaG, MlecnikB, HacklH, et al.. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, 2009, 25(8): 1091-1093

[14] RhodesD R, YuJ, ShankerK, et al.. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia, 2004, 6(1): 1-6

[15] TangZ, LiC, KangB, et al.. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res, 2017, 45(W1): W98-W102

[16] CeramiE, GaoJ, DogrusozU, et al.. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov, 2012, 2(5): 401-404

[17] UhlenM, OksvoldP, FagerbergL, et al.. Towards a knowledge-based Human Protein Atlas. Nat Biotechnol, 2010, 28(12): 1248-1250

[18] SedgwickP. How to read a Kaplan-Meier survival plot. BMJ, 2014, 349: g5608

[19] LiT, FanJ, WangB, et al.. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res, 2017, 77(21): e108-e110

[20] HuY, YingM, HuangC, et al.. Oncologic outcomes of laparoscopy-assisted gastrectomy for advanced gastric cancer: a large-scale multicenter retrospective cohort study from China. Surg Endosc, 2014, 28(7): 2048-2056

[21] KarimiP, IslamiF, AnandasabapathyS, et al.. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomarkers Prev, 2014, 23(5): 700-713

[22] TokumaruY, OshiM, KatsutaE, et al.. KRAS signaling enriched triple negative breast cancer is associated with favorable tumor immune microenvironment and better survival. Am J Cancer Res, 2020, 10(3): 897-907

[23] ChenW, ZhengR, BaadeP D, et al.. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132

[24] GaoW, LiuY, QinR, et al.. Silence of fibronectin 1 increases cisplatin sensitivity of non-small cell lung cancer cell line. Biochem Biophys Res Commun, 2016, 476(1): 35-41

[25] KujawaK A, Zembala-NożyńskaE, CortezA J, et al.. Fibronectin and Periostin as Prognostic Markers in Ovarian Cancer. Cells, 2020, 9(1): 149

[26] SchulzG B, GrimmT, SersC, et al.. Prognostic value and association with epithelial-mesenchymal transition and molecular subtypes of the proteoglycan biglycan in advanced bladder cancer. Urol Oncol, 2019, 37(8): 530-539

[27] BatraJ, RobinsonJ, SoaresAS, et al.. Matrix metalloproteinase-10 (MMP-10) interaction with tissue inhibitors of metalloproteinases TIMP-1 and TIMP- 2: binding studies and crystal structure. J Biol Chem, 2012, 287(19): 15935-15946

[28] SongG, XuS, ZhangH, et al.. TIMP1 is a prognostic marker for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway. J Exp Clin Cancer Res, 2016, 35(1): 148

[29] RoweGC, RaghuramS, JangC, et al.. PGC-1α induces SPP1 to activate macrophages and orchestrate functional angiogenesis in skeletal muscle. Circ Res, 2014, 115(5): 504-517

[30] SongSZ, LinS, LiuJN, et al.. Targeting of SPP1 by microRNA-340 inhibits gastric cancer cell epithelial-mesenchymal transition through inhibition of the PI3K/AKT signaling pathway. J Cell Physiol, 2019, 234(10): 18587-18601

[31] ZhangZ, ZhangJ, MiaoL, et al.. Interleukin-11 promotes the progress of gastric carcinoma via abnormally expressed versican. Int J Biol Sci, 2012, 8(3): 383-393

[32] KimNS, LeeHH, JungCK, et al.. Versican expression in tumor epithelial cells is correlated with a good prognosis in gastric cancer. Anticancer Res, 2014, 34(10): 5613-5619

[33] AppunniS, AnandV, KhandelwalM, et al.. Small Leucine Rich Proteoglycans (decorin, biglycan and lumican) in cancer. Clin Chim Acta, 2019, 491: 1-7

[34] ChenX, LiX, HuX, et al.. LUM Expression and Its Prognostic Significance in Gastric Cancer. Front Oncol, 2020, 10: 605

[35] Ricard-BlumS. The collagen family. Cold Spring Harb Perspect Biol, 2011, 3(1): a4978

[36] LiuX, XuY, ZhouQ, et al.. PI3K in cancer: its structure, activation modes and role in shaping tumor microenvironment. Future Oncol, 2018, 14(7): 665-674

[37] WuJ, LiuJ, WeiX, et al.. A feature-based analysis identifies COL1A2 as a regulator in pancreatic cancer. J Enzyme Inhib Med Chem, 2019, 34(1): 420-428

[38] YuanL, ShuB, ChenL, et al.. Overexpression of COL3A1 confers a poor prognosis in human bladder cancer identified by co-expression analysis. Oncotarget, 2017, 8(41): 70508-70520

[39] ChenZ, SouttoM, RahmanB, et al.. Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia. Genes Chromosomes Cancer, 2017, 56(7): 535-547