Integration of Single-Cell Transcriptomics and Network-Based Machine Learning Identifies Bone Marrow Mesenchymal Stem Cell-Associated Candidate Biomarkers and Exploratory Immune Correlates in Osteoarthritis.

Authors

Hanyu Wang, Zhikang Chen, Shikai Chen, Honglin Pi, Qianqian Liang, Li Wang

Published in

Current gene therapy. Aug 05, 2026. Epub Aug 05, 2026.

Abstract

Osteoarthritis (OA) is a whole-joint disease involving coordinated changes in subchondral bone, Bone Marrow Mesenchymal Stem cells (BM-MSCs), and synovium. This study aimed to identify BM-MSC-associated and OA-related candidate biomarkers and evaluate their potential diagnostic and immune-related relevance.
The scRNA-seq dataset GSE147287 was used to construct an OA subchondral bonemarrow atlas, identify BM-MSC populations, perform BM-MSC sub-clustering, and derive BMMSC co-expression modules and hub genes using hdWGCNA. Three synovial transcriptomic datasets (GSE55457, GSE55235, and GSE55584) were integrated as the training cohort after datasetspecific log2 (expression + 1) transformation and ComBat correction, whereas GSE12021 was processed independently as an external validation cohort. BM-MSC hub genes were intersected with OA-related synovial DEGs and further prioritized using LASSO, SVM-RFE, and XGBoost. A combined four-gene model was constructed using Firth's penalized logistic regression. Diagnostic performance was evaluated by ROC analysis, cellular expression was validated in IL-1β-stimulated hBMSCs by qRT-PCR, and immune-cell and Hallmark pathway correlations were assessed using ssGSEA and Spearman analysis with Benjamini-Hochberg correction.
The single-cell atlas contained nine major cell types, and BM-MSC sub-clustering identified four transcriptionally distinct subclusters. Cross-tissue integration yielded 35 BM-MSCassociated and OA-related candidate genes, from which EFEMP2, CTSO, SPRY1, and NFIC were selected. In the training cohort, the AUCs were 0.890 (95% CI, 0.792-0.989) for EFEMP2, 0.896 (95% CI, 0.802-0.990) for CTSO, 0.892 (95% CI, 0.797-0.987) for SPRY1, and 0.769 (95% CI, 0.630-0.908) for NFIC. In GSE12021, the corresponding AUCs were 0.867 (95% CI, 0.698-1.000), 0.922 (95% CI, 0.801-1.000), 0.922 (95% CI, 0.797-1.000), and 0.856 (95% CI, 0.674-1.000), respectively. The combined four-gene model achieved AUCs of 0.998 (0.993-1.000) in the training cohort and 1.000 (1.000-1.000) in GSE12021. qRT-PCR confirmed the upregulation of EFEMP2 and CTSO and the downregulation of SPRY1 and NFIC in IL-1β-stimulated hBMSCs. After Benjamini- Hochberg correction, no immune-cell or Hallmark pathway correlations remained statistically significant, although several moderate exploratory trends were observed.
EFEMP2, CTSO, SPRY1, and NFIC represent BM-MSC-associated and OA-related candidate biomarkers. These findings provide a hypothesis-generating framework for characterizing OA-related stromal states and developing future tissue-fitness or patient-stratification tools, but further clinical and tissue-specific validation is required.
EFEMP2, CTSO, SPRY1, and NFIC were identified as BM-MSC-associated and OArelated candidate biomarkers. These findings provide a hypothesis-generating framework for characterizing OA-related stromal states and developing future tissue-state assessment or patientstratification tools, although further tissue-specific and clinical validation is required.

PMID:
42634547
Bibliographic data and abstract were imported from PubMed on 24 Aug 2026.

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