ArticlePloS one2025
Association between hyperlipidemia and nephrolithiasis: A comprehensive bioinformatics analysis deciphering the potential common denominator pathogenesis.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- ZJU index and prevalence of kidney stones in US adults: evidence for a threshold association from NHANES 2007-2018.Translational andrology and urology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveEvidence suggests that nephrolithiasis and hyperlipidemia are linked. The study is designed to identify diagnostic biomarkers for nephrolithiasis in conjunction with hyperlipidemia using bioinformatics analysis, while exploring the potential common denominator pathogenesis.
methodsThe NCBI Gene Expression Omnibus (GEO) database provided separate datasets for nephrolithiasis and hyperlipidemia. We employed the R limma package to detect differentially expressed genes (DEGs), which were subsequently analyzed for enrichment using Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Immune cell infiltration was analyzed by the CIBERSORT method. The WGCNA-R package clustered genes with similar expression profiles, followed by an analysis of the associations between the modules and specific traits or phenotypes. The STRING database was utilized to establish a protein-protein interaction (PPI) network and key functional modules, which were then analyzed using Cytoscape software. Diagnostic genes for both diseases were screened from core hub genes using least absolute shrinkage and selection operator (Lasso) regression. Subsequently, we generated receiver operating characteristic (ROC) curves to validate the predictive ability of these diagnostic genes for diagnosing nephrolithiasis in combination with hyperlipidemia. Lastly, the Network Analyst platform facilitated the construction of transcription factor-gene (TF-gene) and TF-miRNA regulatory networks.
resultsBased on datasets of nephrolithiasis and hyperlipidemia, we identified 167 DEGs and 74 hub genes through WGCNA. Using PPI networks and machine learning techniques, we recognized three frequently diagnostic genes (HSP90AB1, HSPA5, and STUB1), which demonstrated high diagnostic validity. The functional enrichment of these three diagnostic genes primarily involved pathways related to cellular metabolism.
conclusionsOur study identified three candidate diagnostic genes that can predict nephrolithiasis in conjunction with hyperlipidemia, providing a solid foundation for further exploration into the pathogenesis of nephrolithiasis and hyperlipidemia.
Indexed as
Identifiers
What Socratic 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 Socratic graph.