aceTargeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis
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 gen…
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By August 2026, a multi-omics study combined single-cell and bulk RNA-seq from GEO with three machine learning algorithms to screen for kidney stone drivers, identifying GLS and LPIN2 as upregulated in fibroblasts and building a risk prediction nomogram.
The work matters because it links computational gene prioritization to functional validation in human renal fibroblasts, showing GLS and LPIN2 modulate myofibroblast transition, ROS, calcium deposition and osteogenic markers, while uncertainty remains about in vivo efficacy, patient heterogeneity, and whether digoxin or other predicted drugs will translate clinically.
- Single-cell analysis of GEO kidney stone datasets identified nine cell types with a significantly reduced proportion of fibroblasts observed in the KS group.
- Nineteen candidate genes were enriched in metabolism, ion balance and cell growth pathways, with PPI and ceRNA networks centred on NEAT1, XIST, hsa-miR-15a-5p and hsa-miR-15b-5p.
- In vitro CaOx-induced human renal fibroblasts showed that knockdown of GLS or LPIN2 reduced alpha-SMA, collagen I, ROS and calcium deposition, while overexpression exacerbated these effects and enhanced Runx2, Osterix, OPN, OCN and ALP activity.
Integrated single-cell analysis and three machine learning algorithms identified GLS and LPIN2 as upregulated key genes in kidney stones that promote fibroblast-to-myofibroblast transition, ROS production, calcium deposition and osteogenic-like differentiation, supporting a risk prediction nomogram and drug prediction.
The rundown
Researchers downloaded single-cell and bulk RNA-seq kidney stone datasets from GEO and performed single-cell heterogeneity analysis, identifying nine cell types and 19 candidate genes concentrated in metabolism and ion balance pathways.
Three machine learning algorithms screened GLS and LPIN2 as key upregulated genes, followed by construction of risk prediction nomograms, immune infiltration analysis, and regulatory network analysis, with digoxin predicted as a potential therapeutic agent.
Validation in calcium oxalate-induced human renal fibroblasts and osteogenic medium induction used qRT-PCR, Western blot, CCK-8, ROS detection, Alizarin Red S staining and ALP activity to show knockdown attenuated and overexpression enhanced fibroblast activation and mineralization.
Sources
- Peer-reviewedUrolithiasis2026-08-22
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