Network Community Detection and Novelty Scoring Reveal Underexplored Hub Genes in Rheumatoid Arthritis
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| Formato: | Preprint |
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2025
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| _version_ | 1866912562987139072 |
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| author | Amirirad, Neda Sayama, Hiroki |
| author_facet | Amirirad, Neda Sayama, Hiroki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Network Community Detection and Novelty Scoring Reveal Underexplored Hub Genes in Rheumatoid Arthritis Amirirad, Neda Sayama, Hiroki Molecular Networks Social and Information Networks Genomics J.3; G.2.2 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. |
| title | Network Community Detection and Novelty Scoring Reveal Underexplored Hub Genes in Rheumatoid Arthritis |
| topic | Molecular Networks Social and Information Networks Genomics J.3; G.2.2 |
| url | https://arxiv.org/abs/2509.00897 |