Identification of Cystatin 3 as a Potential Diagnostic Biomarker for Osteoporosis Using Machine Learning

Area of Science:

• Biochemistry
• Molecular Biology
• Immunology

Background:

• Osteoporosis (OP) is a prevalent systemic bone metabolic disease with unclear pathogenesis.
• The role of Cystatin 3 (CST3), a cysteine protease inhibitor, in OP remains largely uninvestigated.
• Understanding CST3's involvement could elucidate OP mechanisms and identify diagnostic markers.

Purpose of the Study:

• To explore the diagnostic value of CST3 in osteoporosis.
• To investigate the potential molecular mechanisms underlying CST3's role in OP pathogenesis.
• To identify novel biomarkers for osteoporosis diagnosis and progression.

Main Methods:

• Differential gene expression analysis of transcriptome data from OP patients and healthy controls.
• Machine learning algorithms (LASSO, Random Forest) for characteristic gene identification.
• Validation in a rat OP model using qRT-PCR and analysis of immune cell infiltration via CIBERSORT.

Main Results:

• CST3 and FLJ36848 identified as characteristic genes with high diagnostic efficacy (AUC > 0.9).
• CST3 expression was upregulated in bone marrow mesenchymal stem cells (BMSCs) of OP rats, correlating with downregulated bone formation markers (ALP, OCN).
• Increased infiltration of M2 macrophages and NK cells observed in high CST3 expression groups, suggesting immune microenvironment involvement.

Conclusions:

• CST3 is a promising diagnostic biomarker for osteoporosis and may be a therapeutic target.
• CST3's mechanism in OP involves extracellular matrix regulation, collagen metabolism, and immune cell infiltration.
• Further in-depth research and clinical trials are needed to validate findings and elucidate specific molecular pathways.