Integrative analysis reveals shared biomarkers and pathways in osteoporosis and rheumatoid arthritis using machine learning and single-cell sequencing
- 1Department of Orthopedics, Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China, Changsha, Hunan, China
Abstract
Background: Osteoporosis (OP) and Rheumatoid Arthritis (RA) are prevalent chronic diseases characterized by bone loss and immune dysregulation. Evidence suggests that patients with RA are at increased risk of developing OP; however, the shared molecular mechanisms linking these conditions remain incompletely understood. Methods: Publicly available bulk transcriptomic and single-cell RNA sequencing (scRNA-seq) datasets for OP and RA were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to identify shared gene modules associated with both diseases. Functional enrichment and protein–protein interaction analyses were performed to characterize common biological pathways. Machine learning algorithms, including Random Forest (RF) and Support Vector Machine (SVM), were used to identify and validate shared hub genes and to construct diagnostic models. Immune cell infiltration was evaluated using CIBERSORT, and the cellular localization of hub genes was further validated using scRNA-seq analysis. Results: WGCNA identified 329 genes shared between OP- and RA-associated modules, which were significantly enriched in muscle-related processes, cytokine signaling, and immune–inflammatory pathways. RF analysis identified three shared candidate genes: ANO5, IL27RA, and CNTN3. Among these, ANO5 and IL27RA demonstrated consistent expression evidence and literature support, whereas CNTN3 emerged as a computationally nominated candidate requiring cautious interpretation and experimental validation. An SVM-based model incorporating these genes demonstrated robust predictive accuracy in both OP and RA validation cohorts. Immune infiltration analysis revealed distinct yet coordinated immune landscapes, with hub genes showing significant correlations with T cells, macrophages, B cells, and plasma cells. ScRNA-seq analysis revealed that ANO5 was predominantly expressed in macrophages and fibroblasts, IL27RA was mainly enriched in T cells, whereas CNTN3 showed minimal enrichment at the single-cell level in both OP bone and RA synovial tissues. Conclusion: This integrative bioinformatics and machine learning study identifies shared molecular signatures and immune-related mechanisms underlying OP and RA. ANO5 and IL27RA emerge as robust candidate regulators linking immune dysregulation and bone remodeling, whereas CNTN3—although identified by machine learning—requires cautious interpretation and further investigation due to limited supporting evidence. These findings provide novel insights into the pathogenesis of RA-associated OP and highlight potential biomarkers and therapeutic targets for their comorbidity.
Related
- Integrating transcriptomic analysis to explore biomarkers and therapeutic targets for rheumatoid arthritis: a case-control and systems biology approach.
- Machine learning-based identification of CXCL13 and CXCL10 as key biomarkers in rheumatoid arthritis
- Histone lactylation in fibroblast-like synoviocytes drives CXCL1-mediated immune cell recruitment in rheumatoid arthritis
- A pathway-guided kernel MLP architecture for interpretable cross-disease transfer from rheumatoid arthritis to hematological malignancies
- Differential Expression of Cytotoxic-Associated Markers in Circulating CD4+CD28− T Cells and NK Cells in Rheumatoid Arthritis
BibTeX
@article{zhao2026integrative,
title = {{Integrative analysis reveals shared biomarkers and pathways in osteoporosis and rheumatoid arthritis using machine learning and single-cell sequencing}},
author = {Jin Zhao and Yixuan Chen and Fei Yang and Cheng Tian and Wenyan Luo and Zhou Su and Bohuan Yang and Wei Liu and Hongbo Yang},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0362219},
url = {https://doi.org/10.1371/journal.pone.0362219},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrative analysis reveals shared biomarkers and pathways in osteoporosis and rheumatoid arthritis using machine learning and single-cell sequencing
AU - Jin Zhao
AU - Yixuan Chen
AU - Fei Yang
AU - Cheng Tian
AU - Wenyan Luo
AU - Zhou Su
AU - Bohuan Yang
AU - Wei Liu
AU - Hongbo Yang
PY - 2026
DA - 2026/11/04
JO - PLOS One
DO - 10.1371/journal.pone.0362219
UR - https://doi.org/10.1371/journal.pone.0362219
AB - Background: Osteoporosis (OP) and Rheumatoid Arthritis (RA) are prevalent chronic diseases characterized by bone loss and immune dysregulation. Evidence suggests that patients with RA are at increased risk of developing OP; however, the shared molecular mechanisms linking these conditions remain incompletely understood. Methods: Publicly available bulk transcriptomic and single-cell RNA sequencing (scRNA-seq) datasets for OP and RA were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to identify shared gene modules associated with both diseases. Functional enrichment and protein–protein interaction analyses were performed to characterize common biological pathways. Machine learning algorithms, including Random Forest (RF) and Support Vector Machine (SVM), were used to identify and validate shared hub genes and to construct diagnostic models. Immune cell infiltration was evaluated using CIBERSORT, and the cellular localization of hub genes was further validated using scRNA-seq analysis. Results: WGCNA identified 329 genes shared between OP- and RA-associated modules, which were significantly enriched in muscle-related processes, cytokine signaling, and immune–inflammatory pathways. RF analysis identified three shared candidate genes: ANO5, IL27RA, and CNTN3. Among these, ANO5 and IL27RA demonstrated consistent expression evidence and literature support, whereas CNTN3 emerged as a computationally nominated candidate requiring cautious interpretation and experimental validation. An SVM-based model incorporating these genes demonstrated robust predictive accuracy in both OP and RA validation cohorts. Immune infiltration analysis revealed distinct yet coordinated immune landscapes, with hub genes showing significant correlations with T cells, macrophages, B cells, and plasma cells. ScRNA-seq analysis revealed that ANO5 was predominantly expressed in macrophages and fibroblasts, IL27RA was mainly enriched in T cells, whereas CNTN3 showed minimal enrichment at the single-cell level in both OP bone and RA synovial tissues. Conclusion: This integrative bioinformatics and machine learning study identifies shared molecular signatures and immune-related mechanisms underlying OP and RA. ANO5 and IL27RA emerge as robust candidate regulators linking immune dysregulation and bone remodeling, whereas CNTN3—although identified by machine learning—requires cautious interpretation and further investigation due to limited supporting evidence. These findings provide novel insights into the pathogenesis of RA-associated OP and highlight potential biomarkers and therapeutic targets for their comorbidity.
SN - 1932-6203
ER -