ArticleBMC medical genomics2022
Identification of the pivotal role of SPP1 in kidney stone disease based on multiple bioinformatics analysis.
Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 18 citations in OpenAlex.
- FABP3 deficiency exacerbates renal Randall plaque formation: insights from single-cell RNA transcriptomic analysis.Molecular and cellular biochemistry · 2026Article
- Identification of Biomarker in Kidney Stone Disease by Integrating Transcriptomics and Olink Proteomics: A Case-Control Study.Journal of inflammation research · 2026Article
- A Multiomics Study of Circulating Proteins and Kidney Stone Risk.Journal of the American Society of Nephrology : JASN · 2025Article
- Multi-omic and spatial analysis of mouse kidneys highlights sex-specific differences in gene regulation across the lifespan.Nature genetics · 2025Article
- Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics.Nature communications · 2025Article
- Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD).Scientific reports · 2025Article
- The multifaceted role of macrophages in kidney physiology and diseases.Frontiers in immunology · 2025Review
- Bioinformatics identifies key genes and potential therapeutic targets in the pathological mechanism of oxidative stress in Randall's plaque.Scientific reports · 2024Article
- Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.Urolithiasis · 2024Article
- Osteopontin/SPP1: a potential mediator between immune cells and vascular calcification.Frontiers in immunology · 2024Review
- The Protective Role of Dietary Polyphenols in Urolithiasis: Insights into Antioxidant Effects and Mechanisms of Action.Nutrients · 2023Review
- Protein network analysis and functional enrichment via computational biotechnology unravel molecular and pathogenic mechanisms of kidney stone disease.Biomedical journal · 2023Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKidney stone disease (KSD) is a multifactorial disease involving both environmental and genetic factors, whose pathogenesis remains unclear. This study aims to explore the hub genes related to stone formation that could serve as potential therapeutic targets.
methodsBased on the GSE73680 dataset with 62 samples, differentially expressed genes (DEGs) between Randall's plaque (RP) tissues and normal tissues were screened and weighted gene co-expression network analysis (WGCNA) was applied to identify key modules associated with KSD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological functions. The protein-protein interaction (PPI) network was constructed to identify hub genes. Meanwhile, CIBERSORT and ssGSEA analysis were used to estimate the infiltration level of the immune cells. The correlations between hub genes and immune infiltration levels were also investigated. Finally, the top hub gene was selected for further GSEA analysis.
resultsA total of 116 DEGs, including 73 up-regulated and 43 down-regulated genes, were screened in the dataset. The red module was identified as the key module correlated with KSD. 53 genes were obtained for functional enrichment analysis by taking the intersection of DEGs and genes in the red module. GO analysis showed that these genes were mainly involved in extracellular matrix organization (ECM) and extracellular structure organization, and others. KEGG analysis revealed that the pathways of aldosterone-regulated sodium reabsorption, cell adhesion molecules, arachidonic acid (AA) metabolism, and ECM-receptor interaction were enriched. Through PPI network construction, 30 hub genes were identified. CIBERSORT analysis revealed a significantly increased proportion of M0 macrophages, while ssGSEA revealed no significant differences. Among these hub genes, SPP1, LCN2, MMP7, MUC1, SCNN1A, CLU, SLP1, LAMC2, and CYSLTR2 were positively correlated with macrophages infiltration. GSEA analysis found that positive regulation of JNK activity was enriched in RP tissues with high SPP1 expression, while negative regulation of IL-1β production was enriched in the low-SPP1 subgroup.
conclusionsThere are 30 hub genes associated with KSD, among which SPP1 is the top hub gene with the most extensive links with other hub genes. SPP1 might play a pivotal role in the pathogenesis of KSD, which is expected to become a potential therapeutic target, while its interaction with macrophages in KSD needs further investigation.
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