ArticleInternational journal of general medicine2026
Integrated Transcriptomic and Machine Learning Analyses Identify KCNN3 and TLR10 as Candidate Cell-Type-Associated Molecules in Idiopathic Membranous Nephropathy.
Article in International journal of general medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: 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. Patients and Methods: 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. Results: 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. Conclusion: KCNN3 and TLR10 may serve as cell type-associated candidate molecules in IMN, warranting further investigation into their functions and underlying mechanisms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.