Objective:

To identify and validate ZFYVE27 as a diagnostic biomarker for osteoporosis using bioinformatics and machine learning techniques.

Approach:

• Differential Gene Expression Analysis: Identified differentially expressed genes (DEGs) by comparing transcriptomic profiles of healthy controls and osteoporotic patients.
• Functional Enrichment Analysis: Conducted functional enrichment analyses of biological processes and signaling pathways associated with DEGs.
• Weighted Gene Co-expression Network Analysis: Applied WGCNA to isolate disease-specific module genes and intersected these with DEGs to delineate OP-related DEGs.
• Machine Learning Algorithms: Utilized LASSO regression, Support Vector Machine, and Random Forest algorithms to filter and validate potential diagnostic biomarkers.
• Experimental Validation: Established an ovariectomized mouse model to validate ZFYVE27 expression levels.
• Regulatory Network Construction: Constructed a competitive endogenous RNA regulatory network to elucidate post-transcriptional regulatory mechanisms.

Key Findings:

• ZFYVE27 was validated as an optimal diagnostic biomarker for osteoporosis.
• Both mRNA and protein expression levels of ZFYVE27 were significantly upregulated in the OVX model group (p < 0.01).

Interpretation:

ZFYVE27 plays a critical role in the pathological progression of osteoporosis.

Limitations:

• The study primarily focuses on bioinformatics and machine learning without extensive clinical validation.
• Further research is needed to explore the full implications of ZFYVE27 in osteoporosis.

Conclusion:

The present study identifies ZFYVE27 as a novel biomarker for the clinical diagnosis of OP.