Identification of Potential Biomarkers and Therapy Targets involved in Gastric Cancer Using Bioinformatics Analysis

Abstract

Gastric cancer remains one of the five major malignant tumors in the world, posing a great threat to public life and health. As gene sequencing technology develops, it is urgent to find out specific molecular markers for cancer therapy. In this study, datasets of GSE13911, GSE30727, GSE63089 and GSE118916 were investigated by bioinformatics analysis, and differentially expressed genes (DEGs) between GC tissues and normal tissues were screened for potential cancer therapeutic targets. Furthermore, the GSE63089 dataset was analyzed by Weighted Gene Co-expression Network Analysis (WGCNA), and the highly related genes were clustered. Then, the hub genes were searched using co-expression network and Molecular Complex Detection (MCODE) plug-in from Cytoscape software. Finally, ASPM, COL11A1 and CDC20 were obtained by intersection of hub genes and DEGs. The expressions of ASPM, COL11A1 and CDC20 gene in gastric cancer tissues and normal tissues from TCGA database were detected. For these genes, the least absolute shrink and selection operator (LASSO) Cox expression analysis was used to establish the prognostic risk model. COL11A1 and CDC20 genes were identified as candidate prognostic risk markers for GC. Analysis using qRT-PCR has shown that COL11A1 and CDC20 were significant differentially expressed between gastric cancer tissues and normal gastric tissues (P < 0.01). In conclusion, our study identifies specific DEGs involved in ECM process and metabolism by cytochrome P450 process, and these DEGs may be potential targets for GC therapy. The model constructed by COL11A1 and CDC20 genes can predict the prognosis risk of GC patients. Taken together, these findings provide reference for further analyses of key alterations during GC progression.