Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning...

Area of Science:

• Biochemistry
• Genetics
• Molecular Biology

Background:

• Osteoarthritis (OA) is the most common joint disorder, with mitochondrial dysfunction implicated in its development.
• Understanding the precise role of mitochondria in OA pathogenesis is crucial for identifying therapeutic targets.

Purpose of the Study:

• To investigate the critical role of mitochondria in the molecular mechanisms driving osteoarthritis.
• To identify key mitochondria-related genes and pathways involved in OA development.

Main Methods:

• Acquired and analyzed seven bulk RNA-sequencing datasets from the Gene Expression Omnibus (GEO) database.
• Employed gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to identify hub mitochondria-related genes.
• Conducted pathway enrichment, immune infiltration analysis, gene-disease association, mRNA-miRNA network construction, and single-cell RNA sequencing analysis.

Main Results:

• Significantly activated mitochondria-related pathways were observed in osteoarthritis.
• Identified nine hub mitochondria-related genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, PDK4) that form predictive models for OA.
• These genes are associated with macrophages and metabolism-related isoforms, with expression varying during cell differentiation and significantly elevated in OA tissues.

Conclusions:

• The identified hub genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, PDK4) represent potential mitochondrial targets for osteoarthritis research.
• Classifying mitochondria-associated isoforms may facilitate personalized treatment strategies for osteoarthritis patients.