International urology and nephrology · 2026
Integrated single-cell and bulk transcriptomic analysis identifies epithelial cell-related biomarkers and immune subtypes in focal segmental glomerulosclerosis.
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Zhang Jingke, Ning Han, Hou Yun, Tang Ning
Аннотация
BACKGROUND: Focal segmental glomerulosclerosis (FSGS) is a major cause of nephrotic syndrome and chronic kidney disease, characterized by podocyte injury and glomerular scarring. Its heterogeneity complicates early diagnosis and treatment.
METHODS: We integrated bulk and single-cell RNA-seq datasets (GSE200818, GSE200828, and GSE176465) to investigate epithelial cell-related molecular alterations in FSGS. Differentially expressed genes (DEGs) were identified and analyzed for functional enrichment. Machine learning algorithms (random forest and support vector machine) were applied to construct a diagnostic gene signature. Immune cell infiltration was assessed using ssGSEA, and molecular subtypes were defined via consensus clustering. Independent datasets (GSE133288 and GSE108109) were used for validation.
RESULTS: A total of 133 epithelial cell-associated DEGs were identified. Enrichment analysis indicated transcriptional regulation and viral infection pathways. Three diagnostic genes-B4GALT5, CSRNP1, and CYP3A5-were selected and shown to have excellent diagnostic accuracy (AUC > 0.97) in both discovery and validation cohorts, including podocyte-enriched samples. Immune profiling revealed increased infiltration of activated CD8⁺ T cells, NK cells, and dendritic cells in FSGS, with B4GALT5 positively correlated with multiple immune subsets. Consensus clustering stratified patients into two molecular subtypes with distinct immune features.
CONCLUSIONS: This integrative analysis identifies epithelial cell-related biomarkers and immune-associated subtypes in FSGS, providing promising tools for diagnosis and patient stratification. While robust across multiple datasets, further experimental validation is needed to confirm mechanistic relevance.
METHODS: We integrated bulk and single-cell RNA-seq datasets (GSE200818, GSE200828, and GSE176465) to investigate epithelial cell-related molecular alterations in FSGS. Differentially expressed genes (DEGs) were identified and analyzed for functional enrichment. Machine learning algorithms (random forest and support vector machine) were applied to construct a diagnostic gene signature. Immune cell infiltration was assessed using ssGSEA, and molecular subtypes were defined via consensus clustering. Independent datasets (GSE133288 and GSE108109) were used for validation.
RESULTS: A total of 133 epithelial cell-associated DEGs were identified. Enrichment analysis indicated transcriptional regulation and viral infection pathways. Three diagnostic genes-B4GALT5, CSRNP1, and CYP3A5-were selected and shown to have excellent diagnostic accuracy (AUC > 0.97) in both discovery and validation cohorts, including podocyte-enriched samples. Immune profiling revealed increased infiltration of activated CD8⁺ T cells, NK cells, and dendritic cells in FSGS, with B4GALT5 positively correlated with multiple immune subsets. Consensus clustering stratified patients into two molecular subtypes with distinct immune features.
CONCLUSIONS: This integrative analysis identifies epithelial cell-related biomarkers and immune-associated subtypes in FSGS, providing promising tools for diagnosis and patient stratification. While robust across multiple datasets, further experimental validation is needed to confirm mechanistic relevance.