Characterizing programmed cell death features in osteoarthritis through integrative multiomics and machine learning...

Area of Science:

• Biochemistry
• Genetics
• Immunology

Background:

• Programmed cell death (PCD) is vital for tissue homeostasis but its role in osteoarthritis (OA) is unclear.
• Signaling molecules impact metabolism and disease, yet their specific involvement in OA pathogenesis requires elucidation.

Purpose of the Study:

• To systematically evaluate the predictive value, genetic alterations, and therapeutic implications of PCD-associated genes in OA.
• To investigate the diagnostic efficacy and immune infiltration related to PCD genes in OA.

Main Methods:

• Multiomics analyses including transcriptomic and single-cell transcriptome data were performed.
• Weighted gene co-expression network analysis and machine learning models were employed to identify key PCD genes.
• An in vitro OA model using hypoxic ATDC5 chondrocytes was utilized to study gene expression and function.

Main Results:

• S100A9, PMAIP1, and EDA2R were upregulated in OA, suggesting them as risk factors, while FASN was downregulated, indicating a protective role.
• PCD demonstrated reliable diagnostic accuracy for OA.
• Downregulation of S100A9, PMAIP1, EDA2R and overexpression of FASN ameliorated hypoxia-induced cellular damage and inflammation in vitro.

Conclusions:

• PCD genes have significant roles in OA pathogenesis and progression.
• AI-driven analysis of PCD genes offers potential for individualized OA vulnerability assessments and therapeutic strategies.
• Abnormal expression of four hub PCD genes provides new research avenues for OA and immune-mediated diseases.