{"title":"Construction of a PANoptosis-Related Gene Signature for Diabetic Nephropathy.","authors":"Li Geng, Yingying Liu, Yunwei Sun, Yan Chen","doi":"10.1159/000546764","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Diabetic nephropathy (DN) is a serious complication of diabetes. In this study, we aimed to develop a diagnostic model for DN based on PANoptosis-related genes.</p><p><strong>Methods: </strong>PANoptosis-related differentially expressed gene (DEGs) associated with DN were identified in the GSE96804 and GSE142025 datasets. 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.</p><p><strong>Results: </strong>Six PANoptosis-related DEGs were found. Immune cell analysis showed significant differences in dendritic cells, macrophages, mast cells, and neutrophils between DN patients and controls. A diagnostic model using three genes (PDK4, YWHAH, PRKX) achieved high accuracy (area under the curve = 0.8-1.0) across datasets, with a reliable nomogram for DN prediction. Risk stratification linked higher risk scores to distinct immune infiltration patterns and enriched cellular transport and metabolic pathways in high-risk DN patients. Protein-protein interaction network and correlation analyses revealed complex gene interactions. Potential therapeutic targets (PRKX, PDK4) and drugs were identified, and quantitative PCR validated YWHAH upregulation in patient plasma samples.</p><p><strong>Conclusion: </strong>The integration of PANoptosis-related genes PDK4, YWHAH, and PRKX offers a promising diagnostic model for DN, with YWHAH potentially involved in the pathological progression of DN.</p>","PeriodicalId":17813,"journal":{"name":"Kidney & blood pressure research","volume":" ","pages":"496-512"},"PeriodicalIF":2.3000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263135/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kidney & blood pressure research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000546764","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/12 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Diabetic nephropathy (DN) is a serious complication of diabetes. In this study, we aimed to develop a diagnostic model for DN based on PANoptosis-related genes.
Methods: PANoptosis-related differentially expressed gene (DEGs) associated with DN were identified in the GSE96804 and GSE142025 datasets. 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: Six PANoptosis-related DEGs were found. Immune cell analysis showed significant differences in dendritic cells, macrophages, mast cells, and neutrophils between DN patients and controls. A diagnostic model using three genes (PDK4, YWHAH, PRKX) achieved high accuracy (area under the curve = 0.8-1.0) across datasets, with a reliable nomogram for DN prediction. Risk stratification linked higher risk scores to distinct immune infiltration patterns and enriched cellular transport and metabolic pathways in high-risk DN patients. Protein-protein interaction network and correlation analyses revealed complex gene interactions. Potential therapeutic targets (PRKX, PDK4) and drugs were identified, and quantitative PCR validated YWHAH upregulation in patient plasma samples.
Conclusion: The integration of PANoptosis-related genes PDK4, YWHAH, and PRKX offers a promising diagnostic model for DN, with YWHAH potentially involved in the pathological progression of DN.
期刊介绍：
This journal comprises both clinical and basic studies at the interface of nephrology, hypertension and cardiovascular research. The topics to be covered include the structural organization and biochemistry of the normal and diseased kidney, the molecular biology of transporters, the physiology and pathophysiology of glomerular filtration and tubular transport, endothelial and vascular smooth muscle cell function and blood pressure control, as well as water, electrolyte and mineral metabolism. Also discussed are the (patho)physiology and (patho) biochemistry of renal hormones, the molecular biology, genetics and clinical course of renal disease and hypertension, the renal elimination, action and clinical use of drugs, as well as dialysis and transplantation. Featuring peer-reviewed original papers, editorials translating basic science into patient-oriented research and disease, in depth reviews, and regular special topic sections, ''Kidney & Blood Pressure Research'' is an important source of information for researchers in nephrology and cardiovascular medicine.
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