Xia Bingqing, Zeng Ping, Xue Yuling, Li Qian, Xie Jianhui, Xu Jiamin, Wu Wenzhen, Yang Xiaobo
The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Med (Lausanne). 2024 Jun 6;11:1382004. doi: 10.3389/fmed.2024.1382004. eCollection 2024.
Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.
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. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.
A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in 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 the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.
"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 suffering from GC and T2D.
胃癌（GC）与2型糖尿病（T2D）相互影响，但其相互作用机制仍未明确。本研究旨在探索胃癌与2型糖尿病之间的共享基因及相互作用机制。
基因表达综合数据库（GEO）作为胃癌和2型糖尿病数据集的来源。利用差异表达基因（DEGs）和加权基因共表达网络分析（WGCNA）来识别代表性基因。此外，将两种疾病的代表性基因中的重叠基因用于功能富集分析和蛋白质-蛋白质相互作用（PPI）网络分析。接下来，通过两种机器学习算法筛选出枢纽基因。最后，利用癌症基因组图谱（TCGA）数据库的数据进行外部验证。
分别从胃癌（GSE29272）和2型糖尿病（GSE164416）数据集中获得了292个和541个差异表达基因。此外，在胃癌和2型糖尿病中分别鉴定出2704个和336个模块基因。两者交集后，鉴定出104个相互作用基因。富集分析表明，“细胞外基质-受体相互作用”、“糖尿病并发症中的晚期糖基化终末产物-受体（AGE-RAGE）信号通路”、“衰老”和“细胞对铜离子的反应”是共同通路。通过PPI网络，鉴定出10个基因作为候选枢纽基因。机器学习进一步选择了骨桥蛋白（BGN）、核心蛋白聚糖（VCAN）、纤连蛋白1（FN1）、成纤维细胞生长因子结合蛋白1（FBLN1）、IV型胶原α5链（COL4A5）、I型胶原α1链（COL1A1）和VI型胶原α3链（COL6A3）作为枢纽基因。
“细胞外基质-受体相互作用”、“糖尿病并发症中的AGE-RAGE信号通路”、“衰老”和“细胞对铜离子的反应”被揭示为可能的相互作用机制。BGN、VCAN、FN1、FBLN1、COL4A5、COL1A1和COL6A3被鉴定为胃癌和2型糖尿病患者的共享基因及潜在治疗靶点。
