Wang Chenfeng, Wang Huiwei, Feng Ting, Hu Yihe, Liang Feng
Department of Orthopedic Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310030, China.
Department of Dermatology and Venerology, First Hospital of Jilin University, Changchun, Jilin 130021, China.
Int J Med Sci. 2025 Jun 12;22(12):2896-2905. doi: 10.7150/ijms.111050. eCollection 2025.
Gangrene has been a problem for many people with diabetes. Besides, the relationship and pathomechanism of diabetes-induced gangrene (DG) are still unclear. The aim of this study was to investigate the causal relationship between diabetes and gangrene through Mendelian randomization (MR) and to identify potential therapeutic agents using bioinformatics analysis. Summary data from genome-wide association studies (GWAS) were utilized to evaluate the connection between two types of diabetes and gangrene risk using a two-sample MR design. Single nucleotide polymorphisms (SNPs) that were significantly associated with diabetes were selected as instrumental variables, and their validity was verified by F-statistics and other methods. Next, we used text mining and protein-protein interaction (PPI) networks to filtrate significant genes for drug-gene interaction (DGI) to identify prospective medications for the therapy of DG. Through multiple methods analysis (IVW, MR-Egger and MR-PRESSO etc.), MR analysis showed that genetic susceptibility to type 1 diabetes was related to a higher risk of gangrene risk (OR: 1.19, 95% CI: 1.04-1.36, P-value: 0.0134), while type 2 diabetes mellitus (T2DM) could also increase the gangrene risk (OR: 1.57, 95% CI: 1.05-2.33, P-value: 0.0269). The outcomes of text mining disclosed 50 genes enriched in NOD-like receptor and RAGE signaling pathways commonly associated with both diabetes and gangrene for PPI analysis. Subsequent DGI analysis revealed six genes targeted by 12 drugs (DGI score > 5), presenting them as candidates for treating DG. In conclusion, this study not only validates the causal effect of diabetes on gangrene risk but also identifies several potential therapeutic agents (CILAZAPRIL, RESATORVID, SILTUXIMAB, and OLOKIZUMAB) by integrating bioinformatics analysis, providing new directions for future clinical interventions.
坏疽一直是许多糖尿病患者面临的问题。此外，糖尿病性坏疽（DG）的关系和发病机制仍不清楚。本研究的目的是通过孟德尔随机化（MR）研究糖尿病与坏疽之间的因果关系，并利用生物信息学分析确定潜在的治疗药物。利用全基因组关联研究（GWAS）的汇总数据，采用两样本MR设计评估两种类型糖尿病与坏疽风险之间的联系。选择与糖尿病显著相关的单核苷酸多态性（SNP）作为工具变量，并通过F统计量和其他方法验证其有效性。接下来，我们使用文本挖掘和蛋白质-蛋白质相互作用（PPI）网络筛选药物-基因相互作用（DGI）的显著基因，以确定治疗DG的潜在药物。通过多种方法分析（IVW、MR-Egger和MR-PRESSO等），MR分析表明，1型糖尿病的遗传易感性与坏疽风险较高相关（OR：1.19，95%CI：1.04-1.36，P值：0.0134），而2型糖尿病（T2DM）也会增加坏疽风险（OR：1.57，95%CI：1.05-2.33，P值：0.0269）。文本挖掘结果揭示了50个在NOD样受体和RAGE信号通路中富集的基因，这些基因通常与糖尿病和坏疽相关，用于PPI分析。随后的DGI分析揭示了12种药物靶向的6个基因（DGI评分>5），将它们作为治疗DG的候选药物。总之，本研究不仅验证了糖尿病对坏疽风险的因果效应，还通过整合生物信息学分析确定了几种潜在的治疗药物（西拉普利、瑞沙托维德、西妥昔单抗和奥洛珠单抗），为未来的临床干预提供了新的方向。
