Iranian journal of basic medical sciences · 2026
A lipid metabolism-based gene signature defines diagnosis and molecular heterogeneity in diabetic nephropathy.
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Mei Qingbu, Liu Xinyu, Xu Jing, Li Shuang, Wang Tao, Yuan Hui
Аннотация
OBJECTIVES: To identify lipid metabolism-related biomarkers of diabetic nephropathy (DN), develop and validate a diagnostic model, and further explore immune infiltration patterns and molecular subtypes of DN.
MATERIALS AND METHODS: Multiple public transcriptomic datasets were integrated to identify lipid metabolism-related biomarkers for DN using differential expression analysis, weighted gene co-expression network analysis, and machine learning. A diagnostic model was then constructed from these biomarkers, and its performance was evaluated using ROC curve and nomogram analyses, followed by validation in an independent cohort. In addition, immune cell infiltration was assessed with the CIBERSORT algorithm, and consensus clustering identified molecular subtypes of DN. Finally, the findings were validated in a model of type 2 diabetes.
RESULTS: A total of 28 lipid metabolism-related differentially expressed genes were identified, and five hub biomarkers (G0S2, PTGDS, CA2, CYP27B1, and HSD17B14) were screened and demonstrated favorable diagnostic performance in both training and validation cohorts. Functional enrichment analysis revealed that these genes were mainly involved in fatty acid metabolism and PPAR signaling pathways. Immune infiltration analysis revealed significant correlations between dysregulated lipid metabolism and immune cell infiltration in DN. Consensus clustering identified distinct molecular subtypes of DN with different lipid metabolic characteristics. Critically, bioinformatic predictions were validated in db/db mice, showing significantly elevated mRNA and protein levels of all five biomarkers in DN kidneys.
CONCLUSION: Our findings reveal a validated lipid-metabolism-based diagnostic signature with potential for early detection, molecular stratification, and mechanistic investigation.
MATERIALS AND METHODS: Multiple public transcriptomic datasets were integrated to identify lipid metabolism-related biomarkers for DN using differential expression analysis, weighted gene co-expression network analysis, and machine learning. A diagnostic model was then constructed from these biomarkers, and its performance was evaluated using ROC curve and nomogram analyses, followed by validation in an independent cohort. In addition, immune cell infiltration was assessed with the CIBERSORT algorithm, and consensus clustering identified molecular subtypes of DN. Finally, the findings were validated in a model of type 2 diabetes.
RESULTS: A total of 28 lipid metabolism-related differentially expressed genes were identified, and five hub biomarkers (G0S2, PTGDS, CA2, CYP27B1, and HSD17B14) were screened and demonstrated favorable diagnostic performance in both training and validation cohorts. Functional enrichment analysis revealed that these genes were mainly involved in fatty acid metabolism and PPAR signaling pathways. Immune infiltration analysis revealed significant correlations between dysregulated lipid metabolism and immune cell infiltration in DN. Consensus clustering identified distinct molecular subtypes of DN with different lipid metabolic characteristics. Critically, bioinformatic predictions were validated in db/db mice, showing significantly elevated mRNA and protein levels of all five biomarkers in DN kidneys.
CONCLUSION: Our findings reveal a validated lipid-metabolism-based diagnostic signature with potential for early detection, molecular stratification, and mechanistic investigation.