Diverse Programmed Cell Death Patterns and Diagnostic Value in Osteoarthritis Analyzed by Integrative Multiomics Data

Area of Science:

• Genomics and Molecular Biology
• Immunology
• Biomarker Discovery

Background:

• Osteoarthritis (OA) causes chronic pain and disability, significantly impacting quality of life.
• Programmed cell death (PCD) is implicated in OA pathogenesis, but its patterns and roles are not fully understood.
• A comprehensive analysis of PCD patterns in OA is needed to elucidate their potential diagnostic and therapeutic value.

Purpose of the Study:

• To comprehensively analyze programmed cell death (PCD) patterns in osteoarthritis (OA) using multi-omics data.
• To identify differentially expressed genes (DEGs) related to PCD in OA.
• To develop a diagnostic model for OA based on hub PCD-related genes.

Main Methods:

• Utilized batch transcriptomic data from the GEO database for OA analysis.
• Performed differential expression analysis across 13 PCD patterns and unsupervised clustering to define OA molecular subtypes.
• Employed machine learning algorithms to identify hub PCD-related DEGs and construct a diagnostic model; validated findings using scRNA-seq and animal models.

Main Results:

• Identified 61 PCD-related DEGs and two distinct OA molecular subtypes, one enriched with immune pathways.
• Developed a highly accurate OA diagnostic model (AUC = 0.993) based on ten identified hub PCD-related DEGs.
• Confirmed elevated PCD scores in OA chondrocytes and validated hub gene expression in OA cells and rat models.

Conclusions:

• This multi-omics study provides preliminary insights into the diagnostic and classification potential of PCD-related genes in OA.
• Identified potential biomarkers for OA diagnosis and clinical application.
• Highlights the significant role of PCD in OA pathogenesis and suggests avenues for future research.