Identification of differential modules in ankylosing spondylitis using systemic module inference and the attract...

Area of Science:

• * Computational biology
• * Network medicine
• * Bioinformatics

Background:

• * Ankylosing spondylitis (AS) is a chronic inflammatory disease.
• * Understanding the molecular mechanisms underlying AS is crucial for developing targeted therapies.

Purpose of the Study:

• * To identify differential gene modules in AS by integrating network analysis, module inference, and the attract method.
• * To discover potential molecular markers for AS target therapy.

Main Methods:

• * Construction of disease objective network (DON) and healthy objective network (HON) using gene expression data and protein-protein interaction networks.
• * Module detection via a clique-merging algorithm.
• * Seed module evaluation using Jaccard score and module correlation density (MCD).
• * Identification of differential modules using a gene set enrichment analysis-analysis of variance model within the attract method.

Main Results:

• * DON and HON comprised 5,301 nodes and 28,176 interactions.
• * 20 and 21 modules were detected in AS and healthy groups, respectively.
• * Six seed modules were identified across both groups (Jaccard score ≥0.5).
• * One differential module was identified between AS and healthy groups using the attract method, with Seed module 1 showing the highest differential MCD (ΔC = 0.077) and Jaccard score (1.000).

Conclusions:

• * The study successfully identified a single differential module associated with AS.
• * This module represents a potential biomarker for AS targeted therapy.
• * The findings provide valuable insights for future research into ankylosing spondylitis.