Network Community Detection and Novelty Scoring Reveal Underexplored Hub Genes in Rheumatoid Arthritis

2025-08-31

AI Summary

Rheumatoid arthritis (RA) pathogenesis remains incompletely understood, particularly regarding understudied hub genes in synovial tissue co-expression networks. Method: We constructed a weighted gene co-expression network from RA synovial transcriptomes, integrated Louvain/Leiden module detection, node strength ranking, and functional enrichment, and innovatively developed a novelty scoring framework combining GWAS significance and PubMed literature coverage. Contribution/Results: This approach identified five high-centrality hub genes with weak prior RA association evidence. These genes are significantly enriched in adaptive immune pathways, exhibit positive correlations with T- and B-cell markers and negative correlations with NK-cell markers—aligning with RA’s hallmark immune dysregulation. The framework establishes a scalable, interpretable paradigm for prioritizing candidate genes in autoimmune diseases, bridging network topology, genetic evidence, and literature-based knowledge.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Identifying understudied hub genes in rheumatoid arthritis networks
• Integrating GWAS and literature to score gene novelty
• Revealing immune-related gene functions through network analysis

Innovation

• Weighted gene co-expression network construction
• Community detection with modularity algorithms
• Centrality-guided novelty scoring integration