Ma Junyang, Gao Yuan, Hou Shufu, Cui Shichang, Zhu Jiankang
Laboratory of Metabolism and Gastrointestinal Tumor, Shandong Provincial QianFoShan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
School of Clinical Medicine, Jining Medical University, Jining, China.
Curr Med Chem. 2025 Jan 17. doi: 10.2174/0109298673348645241226091059.
Gastric cancer (GC) is the fifth most common cancer globally, and the relationship between type 2 diabetes mellitus (T2DM) and cancer risk remains controversial.
We performed Mendelian randomization (MR) analysis using publicly available GWAS data to assess the causal relationship between T2DM and GC, validated by heterogeneity and pleiotropy analyses. Transcriptomic data from TCGA and GEO were analyzed to identify common differentially expressed genes (DEGs). Weighted gene co-- expression network analysis (WGCNA) was used to construct a prognostic risk model. Drug sensitivity and immune infiltration were evaluated using GDSC and ImmuCellAI, respectively. Additionally, gene mutation analysis was conducted using TCGA data.
The Mendelian randomization analysis revealed a causal relationship between T2DM and GC at the genetic level. Specifically, the causal effect of T2DM on GC was estimated with an odds ratio (OR) of 1.32 (95% CI: 1.12-1.56), while the reverse causal effect of GC on T2DM was estimated at an OR of 0.78 (95% CI: 0.67-0.91). Sensitivity analyses, including Cochran's Q test and the leave-one-out test, confirmed the robustness of these findings. We constructed a prognostic risk score consisting of three T2DM-related genes (CST2, PSAPL1, and C4orf48) based on transcriptome data analysis. Patients with high-risk scores exhibited significantly worse overall survival (OS) (p < 0.05). Cox regression analysis further confirmed the independent predictive value of the risk score for GC prognosis. Our predictive model demonstrated good performance, with an AUC of 0.786 in the training set and 0.757 in the validation set. Gene enrichment analysis indicated that the genes shared between T2DM and GC were associated with inflammatory response, immune response, and metabolic pathways. Tumor immune microenvironment analysis suggested that immune evasion mechanisms may play a key role in developing GC in patients with coexisting T2DM.
T2DM is associated with reduced GC risk. The risk score and model may help guide GC prognosis and management.
胃癌(GC)是全球第五大常见癌症,2型糖尿病(T2DM)与癌症风险之间的关系仍存在争议。
我们使用公开可用的全基因组关联研究(GWAS)数据进行孟德尔随机化(MR)分析,以评估T2DM与GC之间的因果关系,并通过异质性和多效性分析进行验证。对来自癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)的转录组数据进行分析,以识别常见的差异表达基因(DEG)。使用加权基因共表达网络分析(WGCNA)构建预后风险模型。分别使用癌症药物敏感性基因组学(GDSC)和免疫细胞人工智能(ImmuCellAI)评估药物敏感性和免疫浸润。此外,使用TCGA数据进行基因突变分析。
孟德尔随机化分析揭示了T2DM与GC在基因水平上的因果关系。具体而言,T2DM对GC的因果效应估计比值比(OR)为1.32(95%置信区间:1.12 - 1.56),而GC对T2DM的反向因果效应估计OR为0.78(95%置信区间:0.67 - 0.91)。敏感性分析,包括 Cochr an Q检验和留一法检验,证实了这些发现的稳健性。基于转录组数据分析,我们构建了一个由三个与T2DM相关的基因(CST2、PSAPL1和C4orf48)组成的预后风险评分。高风险评分的患者总体生存率(OS)显著更差(p < 0.05)。Cox回归分析进一步证实了风险评分对GC预后的独立预测价值。我们的预测模型表现良好,训练集的曲线下面积(AUC)为0.786,验证集为0.757。基因富集分析表明,T2DM和GC之间共有的基因与炎症反应、免疫反应和代谢途径相关。肿瘤免疫微环境分析表明,免疫逃逸机制可能在合并T2DM的患者发生GC中起关键作用。
T2DM与GC风险降低相关。风险评分和模型可能有助于指导GC的预后和管理。