Network Community Detection and Novelty Scoring Reveal Underexplored Hub Genes in Rheumatoid Arthritis (2509.00897v1)

Abstract: Understanding the modular structure and central elements of complex biological networks is critical for uncovering system-level mechanisms in disease. Here, we constructed weighted gene co-expression networks from bulk RNA-seq data of rheumatoid arthritis (RA) synovial tissue, using pairwise correlation and a percolation-guided thresholding strategy. Community detection with Louvain and Leiden algorithms revealed robust modules, and node-strength ranking identified the top 50 hub genes globally and within communities. To assess novelty, we integrated genome-wide association studies (GWAS) with literature-based evidence from PubMed, highlighting five high-centrality genes with little to no prior RA-specific association. Functional enrichment confirmed their roles in immune-related processes, including adaptive immune response and lymphocyte regulation. Notably, these hubs showed strong positive correlations with T- and B-cell markers and negative correlations with NK-cell markers, consistent with RA immunopathology. Overall, our framework demonstrates how correlation-based network construction, modularity-driven clustering, and centrality-guided novelty scoring can jointly reveal informative structure in omics-scale data. This generalizable approach offers a scalable path to gene prioritization in RA and other autoimmune conditions.