In summary, our comprehensive analysis of the CKD dataset from the GEO database revealed that the critical biomarkers DUSP1, GADD45B, IFI30, IFI44L, ATF3, and LYZ, which exhibited significant associations with CKD, collectively formed a robust disease risk prediction model for CKD using the random forest algorithm. The gene discussed is LYZ; the disease is chronic kidney disease.