Comprehensive analysis of molecular mechanisms underlying kidney stones: gene expression profiles and potential diagnostic markers.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39606015", "kind": "science-record", "revision": 4, "content_hash": "sha256:afac45d554ef5030009d7a83c5d1dbead65d91c85c23f121d125b6648129f21f", "created_at": "2026-11-01T20:29:52Z", "updated_at": "2026-11-01T22:55:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of molecular mechanisms underlying kidney stones: gene expression profiles and potential diagnostic markers.", "doi": "10.3389/fgene.2024.1440774", "pmid": "39606015", "authors": ["Kaisaier Aji", "Aierken Aikebaier", "Asimujiang Abula", "Guang Lu Song"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Background: The study aimed to investigate the molecular mechanisms underlying kidney stones by analyzing gene expression profiles. They focused on identifying differentially expressed genes (DEGs), performing gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and screening optimal feature genes using various machine learning algorithms.\nMethods: Data from the GSE73680 dataset, comprising normal renal papillary tissues and Randall's Plaque (RP) tissues, were downloaded from the GEO database. DEGs were identified using the limma R package, followed by GSEA and WGCNA to explore functional modules. Functional enrichment analysis was conducted using KEGG and Disease Ontology. Various machine learning algorithms were used for screening the most suitable feature genes, which were then assessed for their expression and diagnostic significance through Wilcoxon rank-sum tests and ROC curves. GSEA and correlation analysis were performed on optimal feature genes, and immune cell infiltration was assessed using the CIBERSORT algorithm.\nResults: 412 DEGs were identified, with 194 downregulated and 218 upregulated genes in kidney stone samples. GSEA revealed enriched pathways related to metabolic processes, immune response, and disease states. WGCNA identified modules correlated with kidney stones, particularly the yellow module. Functional enrichment analysis highlighted pathways involved in metabolism, immune response, and disease pathology. Through machine learning algorithms, KLK1 and MMP10 were identified as optimal feature genes, significantly upregulated in kidney stone samples, with high diagnostic value. GSEA further elucidated their biological functions and pathway associations.\nConclusion: The study comprehensively analyzed gene expression profiles to uncover molecular mechanisms underlying kidney stones. KLK1 and MMP10 were identified as potential diagnostic markers and key players in kidney stone progression. Functional enrichment analysis provided insights into their roles in metabolic processes, immune response, and disease pathology. These results contribute significantly to a better understanding of kidney stone pathogenesis and may inform future diagnostic and therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "Wilcoxon rank-sum tests were performed to evaluate the expression levels of optimal feature genes in kidney stone samples compared to control samples. Additionally, to further validate the predictive efficacy of these feature genes, receiver operating characteristic (ROC) curves were used for evaluation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39606015", "locator": "sections[8] \"2.6 Expression and diagnostic significance of optimal feature genes\" paragraph 2", "quote": "Wilcoxon rank-sum tests were performed to evaluate the expression levels of optimal feature genes in kidney stone samples compared to control samples. Additionally, to further validate the predictive efficacy of these feature genes, receiver operating characteristic (ROC) curves were used for evaluation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39606015", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis revealed an AUC value of 0.724 for KLK1 and 0.737 for MMP10, highlighting their substantial diagnostic value in evaluating kidney stone progression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39606015", "locator": "sections[16] \"3.5 Evaluation of expression and diagnostic significance of optimal feature genes\" paragraph 2", "quote": "The analysis revealed an AUC value of 0.724 for KLK1 and 0.737 for MMP10, highlighting their substantial diagnostic value in evaluating kidney stone progression (Figures 6C, D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The analysis revealed an AUC value of 0.724 for KLK1 and 0.737 for MMP10, highlighting their substantial diagnostic value in evaluating kidney stone progression.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39606015"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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