Background: In this study, we aimed to develop a diagnostic model for DN based on PANoptosis-related genes.

Methods: DEGs associated with DN were identified in the GSE96804 and GSE142025 datasets. Functional analyses included GO and KEGG. PANoptosis-related DEGs were also identified by intersecting PANoptosis-related genes with DEGs. Pairwise correlations among these genes were assessed via Pearson correlation analysis. Immune cell abundance in DN patients versus controls was compared in GSE96804. Feature genes for DN prediction were selected with machine learning, and a diagnostic model was constructed using LASSO regression. High-risk and low-risk groups were established based on risk scores, with GSEA used to explore enriched biological processes and pathways. The association between risk scores and immune cell infiltration was examined using CIBERSORT. Potential therapeutic drugs were investigated via the DGIdb database.

Results: We identified 573 DEGs_com in DN. Six PANoptosis-related DEGs were found, showing co-regulation and potential functional interactions. NMF classified patients into two clusters. Immune cell analysis revealed significant differences in abundance between DN patients and controls, with variations in dendritic cells, macrophages, mast cells, and neutrophils. Three feature genes (PDK4, YWHAH, PRKX) were selected for a diagnostic model, yielding high diagnostic accuracy for DN across multiple datasets (AUC = 0.8-1.0) and a reliable nomogram for DN prediction. Stratification based on the risk score revealed significant associations between risk levels and immune cell infiltration patterns. PPI network and correlation analyses indicated potential therapeutic targets (PRKX, PDK4) and drugs were identified.

Conclusions: The integration of PANoptosis-related genes PDK4, YWHAH, and PRKX offers a promising diagnostic model for DN, providing a novel diagnostic tool and potential targeted therapeutic strategies for DN management.

Disclosure

Y. Chen: None.

Funding

Youth of National Natural Science Foundation of China ( 82001480)

© 2024 by the American Diabetes Association
2024
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