Integrative molecular network analysis of genetic risk factors to infer biomarkers and therapeutic targets for rheumatoid arthritis
Sakhaa Alsaedi, Katsuhiko Mineta, Naoto Tamura, Xin Gao, Takashi Gojobori, Michihiro Ogasawara
Abstract
Background
Understanding the interplay between genetic risk factors and molecular pathways in rheumatoid arthritis (RA) is essential for developing effective treatments. This study aims to utilize genetic risk factors of RA and identify their key pathways and potential therapeutic targets through an integrated multi-omics approach.
Introduction
Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease characterized by inflammation of the synovial joints, leading to progressive joint destruction, pain, and disability [1, 2]. It is also associated with conditions such as metabolic diseases [3, 4], neurological disorders [5–7], and infectious diseases [8], indicating a complex interplay between the immune system and overall health [9].
Materials and method
We developed a computational pipeline to analyze genetic risk factors across multiple omics levels and construct a multi-omics knowledge graph to estimate potential therapeutic target scores for RA using integrative molecular network analysis. This pipeline integrates various database APIs and bioinformatics packages across five key stages: (1) data curation of RA genetic risk factors, (2) comprehensive enrichment analysis based on molecular function, biological process similarity, tissue and cell-type specificity, molecular system similarity, and disease association, (3) construction of risk factor knowledge graphs, (4) protein prioritization and therapeutic target scoring, and (5) in silico validation of predicted therapeutic targets for RA (Fig 1).
Results
3.1 Overview of genetic risk factors in RA
The retrieved list of genetic risk factors for RA includes 279 SNPs associated with the condition. However, only 60% of the reviewed articles provided odds ratios for the reported significant risk variants. These SNPs were annotated and integrated with public biomedical and genetic databases to create a dataset comprising 196 genes, 158 of which are protein-coding.
Discussion
This study presents a comprehensive integrative analysis of genetic risk factors in RA using a multi-omics molecular network and knowledge graph approach. We developed a genetic risk factor–based knowledge graph integrating genomics, transcriptomics, and protein–protein interactions to elucidate RA molecular mechanisms. This approach enables the identification of key pathways, therapeutic targets, and drug repurposing opportunities based on functional connectivity, disease associations, and shared pathway signatures.
Acknowledgments
We thank Mr. Mohammed Alarawi for the discussion on the molecular functions of protein networks. We would such as to express our special thanks to Dr. Aseel Alsaedi for the medical discussion on the impact of the genetic risk factors for RA on the digestive system and the influence of existing treatments on patients with RA complications, Rt. Bushra Hameed for the discussion on diagnostic radiology in patients with RA , and Dr. Malak Alsaedi for the medical discussion on the impact of risk variants on the development of severe outcomes related to metabolic and immune diseases.
Citation: Alsaedi S, Mineta K, Tamura N, Gao X, Gojobori T, Ogasawara M (2025) Integrative molecular network analysis of genetic risk factors to infer biomarkers and therapeutic targets for rheumatoid arthritis. PLoS One 20(8): e0329101. https://doi.org/10.1371/journal.pone.0329101
Editor: Cheorl-Ho Kim, Sungkyunkwan University - Suwon Campus: Sungkyunkwan University - Natural Sciences Campus, KOREA, REPUBLIC OF
Received: September 21, 2024; Accepted: July 11, 2025; Published: August 21, 2025
Copyright: © 2025 Alsaedi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the results presented in the study are available from in the Supporting information.
Funding: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award Nos. REI/1/5234-01-01, REI/1/5414-01-01, REI/1/5289-01-01, REI/1/5404-01-01, REI/1/5992-01-01, URF/1/4663-01-01; the Center of Excellence for Smart Health (KCSH) under Award No. 5932; and the Center of Excellence on Generative AI under Award No. 5940 awarded to Professor Xin Gao; and the Office of Sponsored Research (OSR) under Award No. BAS/1/1059-01-01 awarded to Professor Takashi Gojobori. Funder website: https://www.kaust.edu.sa. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
