肥胖和胃癌（GC）是全球普遍的疾病。特别是，肥胖症患者的数量每年增加，而GC的发病率和死亡率较高。因此，这些条件严重影响了个人的生活质量。尽管证据表明这两个条件之间存在很强的关联，但这种合并症的基本机制仍不清楚。我们从基因表达综合数据库中获得了GSE94752和GSE54129的基因表达谱。为了研究相关的生物学过程，使用基因和基因组的基因本体论和京都百科全书进行了途径富集分析，用于肥胖和GC中共有差异表达的基因。随后，基于搜索工具来建立蛋白质 - 蛋白质相互作用（PPI）网络，以检索相互作用的基因（String）数据库，然后使用Cytoscape插件MCODE筛选该网络中的核心模块和中心基因。此外，我们仔细检查了与这些中心基因相关的转录因子，miRNA和mRNA的转录因子（TFS），miRNA和mRNA的相互作用网络。最后，我们使用不同的数据集进行了进一步的分析，以验证集线器基因的重要性。选择了246个共有差异表达的基因（209个上调和37个下调）以进行分析。功能分析强调了这两种疾病中炎症和免疫相关途径的关键作用。使用Cytoscape插件CytoHubba，鉴定了9个集线器基因，即CXCR4，CXCL8，CXCL10，IL6，TNF，CCL4，CCL4，CXCL2，CD4和CCL2。通过使用不同的数据集验证，IL6和CCL4被确认为最终集线器基因。 TF-MIRNA-MRNA调节网络表明，主要与HUB基因相关的TF包括RELA和NFKB1，而主要相关的miRNA包括Has-MiR-195-5p和Has-MIR-106A-5P。使用BiioInformitics方法，我们鉴定了两个Hub Centerrys Omnibus andnibus ofcess and gcc and for obes and for obes and for obes and gene and gene and for。此外，我们构建了一个集线器基因，TFS和miRNA网络，并确定了主要相关的TF和miRNA。这些因素可能与肥胖和GC的常见分子机制有关。

Obesity and gastric cancer (GC) are prevalent diseases worldwide. In particular, the number of patients with obesity is increasing annually, while the incidence and mortality rates of GC are ranked high. Consequently, these conditions seriously affect the quality of life of individuals. While evidence suggests a strong association between these two conditions, the underlying mechanisms of this comorbidity remain unclear.We obtained the gene expression profiles of GSE94752 and GSE54129 from the Gene Expression Omnibus database. To investigate the associated biological processes, pathway enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes for the shared differentially expressed genes in obesity and GC. A protein-protein interaction (PPI) network was subsequently established based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, followed by the screening of the core modules and central genes in this network using Cytoscape plug-in MCODE. Furthermore, we scrutinized the co-expression network and the interplay network of transcription factors (TFs), miRNAs, and mRNAs linked to these central genes. Finally, we conducted further analyses using different datasets to validate the significance of the hub genes.A total of 246 shared differentially expressed genes (209 upregulated and 37 downregulated) were selected for ensuing analyses. Functional analysis emphasized the pivotal role of inflammation and immune-associated pathways in these two diseases. Using the Cytoscape plug-in CytoHubba, nine hub genes were identified, namely, CXCR4, CXCL8, CXCL10, IL6, TNF, CCL4, CXCL2, CD4, and CCL2. IL6 and CCL4 were confirmed as the final hub genes through validation using different datasets. The TF-miRNA-mRNA regulatory network showed that the TFs primarily associated with the hub genes included RELA and NFKB1, while the predominantly associated miRNAs included has-miR-195-5p and has-miR-106a-5p.Using bioinformatics methods, we identified two hub genes from the Gene Expression Omnibus datasets for obesity and GC. In addition, we constructed a network of hub genes, TFs, and miRNAs, and identified the major related TFs and miRNAs. These factors may be involved in the common molecular mechanisms of obesity and GC.