Evidence map›Paper›PMID 42631750›Full record

ArticleUrolithiasis2026

Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis.

Yaofeng Wang, Yanchao Gong, Yuankai Li, Shiying Shao, Zhenhua Li, Mingyue Tian

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Article in Urolithiasis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yaofeng WangDepartment of Urology, The Third Afliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
Yanchao GongDepartment of Urology, The Third Afliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
Yuankai LiDepartment of Urology, The Third Afliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
Shiying ShaoDepartment of Urology, The Third Afliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
Zhenhua LiDepartment of Sleep Medicine, Zhengzhou TCM hospital, Zhengzhou, Zhengzhou, China. 15938766349@163.com.
Mingyue TianDepartment of Pain Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. tianmingyue0115@163.com.

Funding

Health Commission of Henan Province LHGJ20250266Science and Technology Department of Henan Province 262300422312
6 · The paper itself

Abstract

Kidney stones (KS) are a common urological condition, the aetiology of which remains incompletely understood. This study aimed to investigate the key cell types involved in the formation of kidney stones and the molecular mechanisms associated with calcium metabolism. Single-cell and bulk RNA-seq datasets related to kidney stones were downloaded from the GEO database. Single-cell analysis was performed to explore the heterogeneity of kidney stones and identify differentially expressed genes (DEGs). Candidate genes were identified by integrating analysis, followed by functional enrichment analysis. Three machine learning algorithms were applied to screen for key genes, and then we construct risk prediction nomograms. Immune cell infiltration and regulatory network analyses were conducted, and potential therapeutic drugs were predicted. In vitro experiments were conducted using calcium oxalate (CaOx)-induced human renal fibroblasts (HRF), and gene function was validated via qRT-PCR, Western blot, CCK-8 assay, ROS detection and Alizarin Red S staining, alongside knockdown and overexpression experiments. Additionally, osteogenic medium (OM) was applied to evaluate the roles of GLS and LPIN2 in osteogenic-like differentiation of HRF, assessed by osteogenic marker expression (Runx2, Osterix, OPN, OCN), alkaline phosphatase (ALP) activity/staining, and Alizarin Red S mineralization assay. A total of nine cell types were identified, with a significantly reduced proportion of fibroblasts observed in the KS group. Nineteen candidate genes were screened, primarily concentrated in pathways related to metabolism, ion balance and cell growth. Machine learning identified GLS and LPIN2 as key genes, both of which were upregulated in KS at both the transcriptomic and single-cell levels. PPI and ceRNA network analyses revealed a regulatory network centred on NEAT1, XIST, hsa-miR-15a-5p and hsa-miR-15b-5p. Immune infiltration analysis indicated that the expression of GLS and LPIN2 was associated with various immune cell types. In vitro experiments confirmed that CaOx upregulated GLS and LPIN2; their knockdown attenuated fibroblast-to-myofibroblast transition (α-SMA, collagen I), reduced ROS levels and decreased calcium deposition, whilst their overexpression exacerbated these effects. Furthermore, OM induction significantly upregulated GLS and LPIN2 expression in HRF. Knockdown of GLS or LPIN2 attenuated OM-induced osteogenic-like differentiation, as evidenced by reduced expression of Runx2, Osterix, OPN and OCN, decreased ALP activity, and diminished mineralization, whereas their overexpression further enhanced these osteogenic responses. This study identified GLS and LPIN2 as key genes promoting fibroblast activation, calcium deposition and osteogenic differentiation in KS, constructed a risk prediction model, and identified digoxin as a potential therapeutic agent, providing new strategies for the precise diagnosis and treatment of KS.

Indexed as

FibroblastsKidneyKidney CalculiCell DifferentiationCells, CulturedHumansMultiomicsOsteogenesisCalcium metabolismFibroblastsImmune infiltrationKidney stoneMachine learningSingle-cell RNA sequencing

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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.