Objective  To screen mitochondrial function-related feature genes in the skeletal muscle of type 2 diabetes mellitus (T2DM) patients and construct a diagnostic model based on bioinformatics and machine learning techniques, in order to provide new insights for the clinical diagnosis of T2DM.

Methods  T2DM datasets from the Gene Expression Omnibus (GEO) database were integrated and processed using R 4.3.2 software for data normalization and batch effect correction. Differentially expressed genes were screened and intersected with mitochondrial genes from the MitoCarta 3.0 database. Key genes were identified through a combination of three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM). The reliability of feature genes was evaluated using Gaussian mixture models (GMM), and a logistic regression diagnostic model was constructed. The model performance was assessed using the receiver operating characteristic curve (ROC) and externally validated in independent datasets.

Results  A total of 23 T2DM-related mitochondrial differential genes were obtained. MRPS10, SLC25A5, and TRNT1 were identified by machine learning algorithm as the core feature genes, which were significantly enriched in oxidative phosphorylation and fatty acid metabolism pathways. The diagnostic model achieved an area under curve (AUC) of 0.958 in the training set, with excellent performance in validation sets GSE29221 (AUC=1.000) and GSE25724 (AUC=0.847).

Conclusion  The mitochondrial function genes MRPS10, SLC25A5, and TRNT1 identified in this study demonstrated potential as diagnostic biomarkers for T2DM. These findings not only provide new candidate biomarkers for diagnosing T2DM, but also lay a foundation for in-depth analysis of the molecular mechanisms of mitochondrial dysfunction in the pathogenesis of T2DM.

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