Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis

Background: Gastric cancer (GC) is one of the most prevalent cancers all over the world. The molecular mechanisms of GC remain unclear and not well understood. GC cases are majorly diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to get a better understanding of precise molecular mechanisms and enable us to identify the key genes in the carcinogenesis and progression of GC. Methods: The present study used datasets from the GEO database to screen differentially expressed genes (DEGs) between GC and normal gastric tissues. GO and KEGG enrichments were utilized to analyze the function of DEGs. The STRING database and Cytoscape software were applied to generate protein-protein network and find hub genes. The expression levels of hub genes were evaluated using data from the TCGA database. Survival analysis was conducted to evaluate the prognostic value of hub genes. The GEPIA database was involved to correlate key gene expressions with the pathological stage. Also, ROC curves were constructed to assess the diagnostic value of key genes. Results: A total of 607 DEGs were identified using three GEO datasets. GO analysis showed that the DEGs were mainly enriched in extracellular structure and matrix organization, collagen fibril organization, extracellular matrix (ECM), and integrin binding. KEGG enrichment was mainly enriched in protein digestion and absorption, ECM-receptor interaction, and focal adhesion. Fifteen genes were identified as hub genes, one of which was excluded for no significant expression between tumor and normal tissues. COL1A1, COL5A2, P4HA3, and SPARC showed high values in prognosis and diagnosis of GC. Conclusion: We suggest COL1A1, COL5A2, P4HA3, and SPARC as biomarkers for the diagnosis and prognosis of GC.