Bioinformatics analysis identifies COL1A1, THBS2 and SPP1 as potential predictors of patient prognosis and immunotherapy response in gastric cancer

• Published in: Bioscience reports Vol. 41; no. 1
• Main Authors: Wang, Yali, Zheng, Kun, Chen, Xiuqiong, Chen, Rui, Zou, Yanmei
• Format: Journal Article
• Language: English
• Published: England Portland Press Ltd The Biochemical Society 01.01.2021; Portland Press Ltd
• Subjects: Bioinformatics; Biomarkers; Cancer; Cancer therapies; Collagen (type I); Collagen Type I, alpha 1 Chain - genetics; Computational Biology - methods; Cytochrome; Datasets; Diagnostics & Biomarkers; Extracellular matrix; Gastric cancer; Gene expression; Gene Expression & Regulation; Gene Ontology; Genes; Genomes; Genomics; Humans; Immunotherapy; Kinases; Medical prognosis; Metabolism; Microenvironments; Mutation; Online data bases; Osteopontin - genetics; Parameters; Patients; PD-L1 protein; Prognosis; Protein Interaction Maps; Proteins; Software; Stomach Neoplasms - genetics; Stomach Neoplasms - pathology; Stomach Neoplasms - therapy; Survival; Survival Analysis; Thrombospondins - genetics; Tissue inhibitor of metalloproteinase 1; gastric cancer; hub genes; Bioinformatics; immunotherapy
• ISSN: 0144-8463; 1573-4935
• DOI: 10.1042/bsr20202564

The present study aimed to use bioinformatics tools to explore pivotal genes associated with the occurrence of gastric cancer (GC) and assess their prognostic significance, and link with clinicopathological parameters. We also investigated the predictive role of COL1A1, THBS2, and SPP1 in immunotherapy. We identified differential genes (DEGs) that were up- and down-regulated in the three datasets (GSE26942, GSE13911, and GSE118916) and created protein-protein interaction (PPI) networks from the overlapping DEGs. We then investigated the potential functions of the hub genes in cancer prognosis using PPI networks, and explored the influence of such genes in the immune environment. Overall, 268 overlapping DEGs were identified, of which 230 were up-regulated and 38 were down-regulated. CytoHubba selected the top ten hub genes, which included SPP1, TIMP1, SERPINE1, MMP3, COL1A1, BGN, THBS2, CDH2, CXCL8, and THY1. With the exception of SPP1, survival analysis using the Kaplan-Meier database showed that the levels of expression of these genes were associated with overall survival. Genes in the most dominant module explored by MCODE, COL1A1, THBS2, and SPP1, were primarily enriched for two KEGG pathways. Further analysis showed that all three genes could influence clinicopathological parameters and immune microenvironment, and there was a significant correlation between COL1A1, THBS2, SPP1, and PD-L1 expression, thus indicating a potential predictive role for GC response to immunotherapy. ECM-receptor interactions and focal adhesion pathways are of great significance in the progression of GC. COL1A1, THBS2, and SPP1 may help predict immunotherapy response in GC patients.