Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network.

Area of Science:

• Immunology
• Bioinformatics
• Systems Biology

Background:

• Traditional rheumatoid arthritis (RA) biomarker discovery often overlooks crucial interactions between biological pathways.
• Understanding pathway crosstalk is essential for a comprehensive view of RA pathogenesis.

Purpose of the Study:

• To identify dysregulated pathway sets in RA by constructing and analyzing a pathway interaction network (PIN).
• To uncover novel potential biomarkers for RA diagnosis and treatment.

Main Methods:

• Utilized RA gene expression, protein-protein interaction (PPI), and pathway data.
• Calculated pathway activity using principal component analysis and identified a seed pathway (epigenetic regulation of gene expression).
• Constructed a PIN and extracted dysregulated pathways using support vector machines and AUC index.

Main Results:

• The constructed PIN included 854 pathways and 1064 interactions.
• The pathway 'epigenetic regulation of gene expression' showed the most significant activity change and served as the seed pathway.
• A set of 10 dysregulated pathways was identified with an AUC of 0.8249, effectively distinguishing RA from control samples.

Conclusions:

• The identified set of 10 dysregulated pathways demonstrates potential as novel biomarkers for RA.
• This pathway-interaction-based approach offers a more holistic strategy for biomarker discovery in complex diseases like RA.