Construction of a diagnostic model utilizing m7G regulatory factors for the characterization of diabetic nephropathy and the immune microenvironment

Diabetic nephropathy (DN), a prevalent and severe complication of diabetes, is associated with poor prognosis and limited treatment options. N7-Methylguanosine (m7G) modification plays a crucial role in regulating RNA structure and function, linking it closely to metabolic disorders. However, despite its biological significance, the interplay between m7G methylation and immune status in DN remains largely unexplored. Leveraging data from the GEO database, we conducted consensus clustering of m7G regulators in DN patients to identify distinct molecular subtypes. To construct and validate m7G-related prognostic features and risk scores, we integrated multiple machine learning approaches, including Support Vector Machine-Recursive Feature Elimination, Random Forest, LASSO, Cox regression, and ROC curves analysis. In addition, we employed GSVA, ssGSEA, CIBERSORT, and Gene Set Enrichment Analysis to investigate the associated biological pathways and the immune landscape, providing deeper insights into the role of m7G methylation in DN. Based on the expression levels of 18 m7G-related regulatory factors, we identified nine key regulators. Through machine learning techniques, we identified four significant regulators (METTL1, CYFIP2, EIF3D, and NUDT4). Consensus clustering classified these genes into two distinct m7G-related clusters. To characterize these subtypes, we conducted immune infiltration analysis, differential expression analysis, and enrichment analysis, uncovering significant biological differences between the clusters. Additionally, we developed an m7G-related risk scoring model using the PCA algorithm. The differential expression of the four key regulators was further validated through in vivo experiments, reinforcing their potential role in disease progression. The m7G-related genes METTL1, CYFIP2, EIF3D, and NUDT4 may serve as potential diagnostic biomarkers for DN, providing new insights into its molecular mechanisms and immune landscape.