ArticlePloS one2026
Identification of potential biomarkers for osteoporosis and chronic kidney disease through bioinformatics and machine learning algorithm.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
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7 authors.
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Abstract
objectiveThe purpose of this study is to identify hub genes associated with both osteoporosis (OP) and chronic kidney disease (CKD) through bioinformatics analysis, and to explore the potential pathogenetic mechanisms in OP and CKD through these hub genes.
methodsWe downloaded the GSE15072 and GSE56815 datasets from the GEO database as training sets, and GSE7158 and GSE70528 for validation. Differential expression genes were selected using the "limma" package, while gene co-expression networks were constructed with "WGCNA." Functional enrichment analyses were performed using "clusterProfiler." Hub genes were identified through machine learning techniques, and their diagnostic efficacy was evaluated by ROC curves plotted with the 'pROC' package. Immune infiltration was analyzed using CIBERSORT, and pan-cancer relationships were explored to identify associations between hub genes and various tumors. Potential therapeutic agents were investigated using the Drug Signatures Database (DSigDB). Experimental validation was conducted via RT-qPCR using cisplatin-induced chronic kidney disease (CKD) and ovariectomy (OVX)-induced osteoporosis models in C57BL/6J mice. After anesthesia and sacrifice, peripheral blood mononuclear cells (PBMCs) were collected to analyze the expression changes of hub genes.
resultsThis study identified four hub genes (FAM184A, NFKBIA, RP2, HIRA). All hub genes exhibited excellent diagnostic performance, with FAM184A showing the best performance. Immune infiltration analysis revealed the relationships between hub gene expression levels and various immune cells. Pan-cancer analysis revealed the expression levels of FAM184A in different tumors, and it showed that high expression of FAM184A in SARC, SKCM, and PAAD is associated with improved prognosis and reduced mortality rates. Finally, RT-qPCR analysis revealed the mRNA expression levels of the hub genes in both OP and CKD. The mRNA expression of all hub genes were downregulated in osteoporosis model mice compared with normal mice, while in CKD mice, the mRNA expression of all hub genes except FAM184A was upregulated.
conclusionsThis study identified four hub genes with significant diagnostic efficacy, suggesting they may act as crucial links between osteoporosis and chronic kidney disease. These genes offer promising targets for the treatment of both diseases. The findings of this study provide valuable insights for future research, which could further elucidate the complex pathogenetic mechanisms connecting chronic kidney disease and osteoporosis.
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