Identification of mitochondrial metabolism and succinylation-related genes as novel biomarkers in diabetic kidney disease via bioinformatics analysis and experimental validation

Succinylation, a novel post-translational modification, has been recognized to play a critical role in metabolic regulation. However, the function of succinylation-related in mitochondrial metabolism and its regulatory mechanisms underlying Diabetic Kidney Disease (DKD) remain poorly understood. Differentially expressed genes (DEGs) and module genes through weighted gene co-expression network analysis (WGCNA) were identified from GSE96804 GEO database, and these two gene sets were then integrated. Subsequently, overlapping potential targets genes were acquired by intersecting the integrated gene set with mitochondria-related and succinylation-related genes. Multiple machine learning algorithms were then applied to screen candidate biomarkers.The external dataset GSE104954 was used to validate the identified biomarkers. The Nephroseq v5 database was corroborated the correlation between biomarkers and clinical data. The Kidney Integrative Transcriptomics (KIT) database was employed to explore the distribution of core genes across 12 cell populations. The CIBERSORT algorithm was used to assess immune cell infiltration. Furthermore, Potential drug molecules interacting with core genes were screened using the DSigDB database on the Enrichr platform, and molecular docking was performed using CB-Dock2 to evaluate binding affinity. The expression of characteristic genes in Type 2 diabetes mellitus (T2DM) mice model was ultimately validated by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). A total of 31 overlapping genes were ultimately identified. Based on the protein–protein interaction (PPI) network, the top 10 most significant genes were screened. Three machine learning approaches were further applied to narrow these genes down to four core genes.The identified biomarkers were validated in the external dataset GSE104954, and receiver operating characteristic (ROC) curve analysis was performed to evaluate their diagnostic efficacy for DKD (all AUC > 0.7). The signature genes exhibited significant correlations with immune cell infiltration. Malate dehydrogenase 2 (MDH2), Pyruvate carboxylase (PC) and hydroxyacyl-CoA dehydrogenase beta subunit (HADHB) were shown to interact with multiple drugs. qRT‑PCR confirmed differential mRNA expression for all four genes. IHC further verified decreased protein levels for SDHB, PC, and MDH2, whereas HADHB protein showed no significant change, suggesting potential post‑transcriptional or activity‑level regulation. This study identified four mitochondrial succinylation‑related biomarker whose transcriptional changes are associated with DKD, providing new insights and potential directions for diagnosis and treatment.