Evidence map›Paper›PMID 42801211›Full record

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.

Binran Zhao, Fugang Liu, Boji Xie, Shuting Pang, Qiuyan Tan, Shanshan Li, Mingxing Wei, Yian Huang, Yuting Wei, Wenqi Luo and 5 more

Abstract read
In one paragraph

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.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Binran Zhao *Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.
Fugang Liu *Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.
Boji Xie *Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.
Shuting PangDepartment of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.ORCID 0009-0001-2481-4581
Qiuyan TanDepartment of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.
Shanshan LiDepartment of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.
Mingxing WeiDepartment of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.
Yian HuangCentre for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, 530021, People's Republic of China.
Yuting WeiCentre for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, 530021, People's Republic of China.
Wenqi LuoCentre for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, 530021, People's Republic of China.
Peng WangCentre for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, 530021, People's Republic of China.
Jinxia SuDepartment of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.
Bijun LiDepartment of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.
Rirong YangCentre for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, 530021, People's Republic of China.
Wei LiDepartment of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

CXCL12-CXCR4 axisdendritic cellsidiopathic membranous nephropathymachine learningpodocytestranscriptomics

Identifiers

PMID42801211
PMCPMC13615809

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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.