Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis - Xia, Bingqing; Zeng, Ping; Xue, Yuling; Li, Qian; Xie, Jianhui; Xu, Jiamin; Wu, Wenzhen; Yang, Xiaobo (Frontiers in Medicine, 2024)
Background: Gastric cancer (GC) and type 2 diabetes(T2D) contribute to each other, but the interaction mechanisms stay undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.Methods: The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. Besides, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Afterward, hub genes were filtered through two machine learning algorithms.Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.Results: 292 and 541 DEGs were obtained from GC (GSE29272) and T2D (GSE164416) datasets, respectively. Besides, 2704 and 336 module genes were identified from GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through PPI network, 10 genes were identified as candidate hub genes. Machine learning was further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1 and COL6A3 as hub genes.4 Conclusions: "ECM-receptor interaction", "AGE-RAGE signaling pathway in diabetic complications", "aging" and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1 and COL6A3 were identified as shared genes and potential therapeutic targets for people suffered from GC and T2D.