Integrated Transcriptomic and Machine Learning Analyses Identify KCNN3 and TLR10 as Candidate Cell-Type-Associated...

Idiopathic membranous nephropathy (IMN) is a common immune-mediated glomerular disease, but the key molecules driving its progression remain unclear. This study integrated bulk RNA sequencing (RNA-seq), machine learning, and single-cell RNA sequencing (scRNA-seq) to screen and preliminarily validate key molecules, providing a basis for subsequent research.

We analyzed our published urinary bulk RNA-seq data, as well as IMN kidney datasets obtained from the GEO database. Differentially expressed genes were identified and subjected to enrichment analysis. Three machine learning algorithms screened key genes. ScRNA-seq data were used to identify the cell types expressing the key genes and to perform CellChat analysis. Immunohistochemistry validated key gene expression, and immunofluorescence explored their cellular localization. External GEO datasets validated expression differences and diagnostic performance.

We identified 94 genes commonly upregulated in both urine and kidney tissues, enriched in transmembrane transport pathways. Two key genes, KCNN3 and TLR10, were identified by machine learning. Single-cell analysis suggested KCNN3 enrichment in podocytes and TLR10 in dendritic cells (DCs), and CellChat analysis predicted potential crosstalk between these cell types through the CXCL12-CXCR4 axis. Immunohistochemistry confirmed their elevated expression in IMN kidneys. Immunofluorescence showed spatial associations of KCNN3 with podocytes and TLR10 with DCs. External validation showed expression trends of KCNN3 and TLR10 were not entirely consistent across datasets, with combined AUCs of 0.705 and 0.748 in two validation sets, showing no significant improvement over single-gene models.

KCNN3 and TLR10 may serve as cell type-associated candidate molecules in IMN, warranting further investigation into their functions and underlying mechanisms.