Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multiple machine learning approaches

This study suggests that JUN, PTGDS, and SLC22A17 may be potential biomarkers for DKD with VC, involving immune, metabolic, and inflammatory processes. These findings provide new targets for early diagnosis of DKD with VC and offer a novel perspective for applying bioinformatics combined with machine learning in discovering diagnostic biomarkers for diseases.