Zhang Ping, Du Yijun, Zhong Xing, Wang Yue, Pan Tianrong
Department of Endocrinology, The Second Affiliated Hospital of Anhui Medical University, No. 678 Furong Road, Jingkai District, Hefei, 230601, Anhui Province, China.
Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, No. 678 Furong Road, Jingkai District, Hefei, 230601, Anhui Province, China.
Sci Rep. 2025 Jul 15;15(1):25439. doi: 10.1038/s41598-025-10200-0.
Type 2 diabetes (T2D) and sarcopenia (SA) commonly co-occur in clinical settings. This study aims to identify overlapping biomarkers for T2D and SA, thereby advancing the understanding of shared pathophysiological mechanisms. Gene expression data from the NCBI GEO database were analyzed to detect differentially expressed genes (DEGs) in T2D and SA using the limma package. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to identify significant gene modules in each condition, followed by functional enrichment analysis. A risk assessment model was established and evaluated through Support Vector Machine (SVM) analysis. Additionally, regulatory networks, including miRNAs and transcription factors, were constructed to investigate gene regulation. qRT‒PCR and western blotting were employed to validate the expression of these biomarkers in the muscle tissues of db/db mice. A total of 330 DEGs were identified in the T2D dataset, while 1054 were found in the SA dataset, with 50 overlapping genes. Key modules in each condition highlighted 30 shared genes, which were enriched in biological processes and pathways related to metabolic and immune functions. Fourteen intersecting hub genes exhibited significant differential expression across the disease datasets, supporting the development of a robust risk classification model. This model demonstrated strong predictive performance, with AUC values of 0.944 for T2D and 0.940 for SA. BDH1, FGF9, and LDHA were identified as key biomarkers through bioinformatics analysis and experimental validation. The identification of BDH1, FGF9, and LDHA offers both diagnostic value and potential therapeutic targets for T2D and SA, thus clarifying the shared pathogenesis of these diseases.
2型糖尿病（T2D）和肌肉减少症（SA）在临床环境中常同时出现。本研究旨在识别T2D和SA的重叠生物标志物，从而加深对共同病理生理机制的理解。利用limma软件包分析来自NCBI GEO数据库的基因表达数据，以检测T2D和SA中差异表达基因（DEG）。应用加权基因共表达网络分析（WGCNA）来识别每种情况下的显著基因模块，随后进行功能富集分析。通过支持向量机（SVM）分析建立并评估风险评估模型。此外，构建包括miRNA和转录因子在内的调控网络，以研究基因调控。采用qRT-PCR和蛋白质免疫印迹法验证这些生物标志物在db/db小鼠肌肉组织中的表达。在T2D数据集中共鉴定出330个DEG，而在SA数据集中发现1054个，其中有50个重叠基因。每种情况下的关键模块突出显示了30个共享基因，这些基因富集于与代谢和免疫功能相关的生物学过程和途径。14个交叉枢纽基因在疾病数据集中表现出显著差异表达，支持了一个强大的风险分类模型的开发。该模型表现出强大的预测性能，T2D的AUC值为0.944，SA的AUC值为0.940。通过生物信息学分析和实验验证，确定BDH1、FGF9和LDHA为关键生物标志物。BDH1、FGF9和LDHA的鉴定为T2D和SA提供了诊断价值和潜在治疗靶点，从而阐明了这些疾病的共同发病机制。
