Identification of PANoptosis genes in w based on bioinformatics analysis and machine learning

Abstract

Background Diabetic nephropathy (DN) is a prominent etiological factor that contributes to the development of end-stage renal disease (ESRD). PANoptosis is an inflammatory programmed cell death pathway, and its involvement in the pathogenesis of DN has been demonstrated. The objective of this research was to examine the potential role of key PANoptosis-related genes in the occurrence of DN and to assess the clinical utility of these genes in predicting DN. Methods This study employed bioinformatics analysis to acquire a dataset of gene expression data for patients with DN from the Gene Expression Omnibus (GEO) database. Furthermore, we identified and functionally annotated differentially expressed genes (DEGs) and performed immune cell infiltration analysis. Consensus clustering was employed to identify molecular subtypes associated with PANoptosis. The least absolute shrinkage and selection operator (LASSO) technique was utilized to screen crucial PANoptosis genes, leading to the development of a prediction model for DN. Additionally, a clinical nomogram prediction model was constructed to validate the correlation between the core genes and DN. Finally, Mendelian randomization (MR) analysis was conducted using genome-wide association studies to ascertain the causal impact of ITM2C on DN. Results A total of eight genes (PROM1, MAFF, CLEC2B, CX3CR1, CXCL6, EVI2B, ITM2C, and VIM) associated with the incidence of DN were identified. Conclusions We successfully constructed a nomogram utilizing PANoptosis-related genes for the purpose of predicting the incidence of DN. This novel model holds potential as a valuable instrument for evaluating the imperative need for timely medical intervention to mitigate the onset of DN.