Identification of Chronic Stress-Related Biomarkers for Potential Diagnosis and Treatment of Osteoporosis: An Integrated Multi-omic and Clinical Data Analysis.

OBJECTIVE

The mechanisms through which chronic stress-related genes influence the diagnosis of osteoporosis (OP), where chronic stress serves as a risk factor, remain unclear. This study aimed to identify chronic stress-related biomarkers using multi-omics approaches and explore their potential applications in the diagnosis and treatment of OP.

METHODS

Bulk RNA sequencing (bulk RNA-seq) and single-cell RNA sequencing (scRNA-seq) data from public databases were integrated. Differential expression analysis, weighted gene co-expression network analysis, and machine learning algorithms (such as receiver operating characteristic curve analysis) were employed to screen candidate biomarkers. A competing endogenous RNA (ceRNA) network and a transcription factor-mRNA (TF-mRNA) regulatory network were constructed to predict targeted drugs. Key cell types and pseudotemporal changes were analyzed via scRNA-seq, and reverse transcription quantitative polymerase chain reaction was used to validate biomarker expression.

RESULTS

Two highly expressed candidate biomarkers, CD34 and VAMP1, were initially identified, through bioinformatics analysis, but subsequent experimental validation confirmed only CD34 as a robust marker for OP. The ceRNA network comprised 2 mRNAs, 5 miRNAs, and 14 lncRNAs, and predicted 173 TFs. Five potential therapeutic drugs (including botulinum toxin type B and prednisolone) were screened. scRNA-seq revealed B cells and T cells as key cell types, with CD34 showing dynamic expression during B cell differentiation.

CONCLUSION

This study systematically elucidates the role of chronic stress-related biomarkers in OP, providing novel diagnostic targets (CD34) and predicting potential therapeutic agents. Although VAMP1 showed diagnostic potential in silico, its lack of validation underscores the need for larger cohort studies. Analysis at the single-cell level provides a preliminary landscape to explore potential cell types and differentiation mechanisms, offering a theoretical foundation for the precise diagnosis and treatment of OP.