Alsaedi Sakhaa, Mineta Katsuhiko, Tamura Naoto, Gao Xin, Gojobori Takashi, Ogasawara Michihiro
Computer Science, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
PLoS One. 2025 Aug 21;20(8):e0329101. doi: 10.1371/journal.pone.0329101. eCollection 2025.
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.
We developed a computational pipeline to construct a knowledge graph that combines genetic risk factor molecular networks with multi-omics enrichment analysis to estimate potential therapeutic target for RA. Genetic risk factors were extracted from the literature, curated, and annotated. Molecular interaction networks were constructed based on these genetic risk factors and their neighboring proteins. Enrichment analysis was performed to identify significantly impacted biological processes and pathways. Multi-omics knowledge graph was used to prioritize candidates potential therapeutic target for RA.
Our analysis identified 35 significant genes associated with RA as potential therapeutic targets and biomarkers, categorized into three pathways: Cytokine Regulation and Production, Hematopoietic or Lymphoid Organ Development, and Myeloid Cell Differentiation. Among these, 25 genes were classified as risk genes, while 10 were neighboring genes. We identified nine novel risk proteins (RELA, ETS1, NFATC1, BATF, LCK, PIK3R1, PRKCB, RASGRP1,and FYN) as potential therapeutic targets for RA and they significantly contribute in the disease pathogenesis.
This study provides a comprehensive integrative molecular network and knowledge graph analysis of genetic risk factors in RA, offering a solid framework for integrating multi-omics data in RA research. These findings may contribute to more accurate clinical decision-making and the development of targeted treatment regimens. Additionally, this study highlights the importance of inferring hidden relationships across networks based on disease associations and functional similarities, further enhancing our understanding of RA pathogenesis.
了解类风湿关节炎（RA）中遗传风险因素与分子途径之间的相互作用对于开发有效的治疗方法至关重要。本研究旨在利用RA的遗传风险因素，并通过综合多组学方法确定其关键途径和潜在治疗靶点。
我们开发了一个计算流程来构建一个知识图谱，该图谱将遗传风险因素分子网络与多组学富集分析相结合，以估计RA的潜在治疗靶点。从文献中提取、整理和注释遗传风险因素。基于这些遗传风险因素及其邻近蛋白构建分子相互作用网络。进行富集分析以识别受显著影响的生物过程和途径。多组学知识图谱用于对RA的潜在治疗靶点候选物进行优先级排序。
我们的分析确定了35个与RA相关的重要基因作为潜在治疗靶点和生物标志物，分为三个途径：细胞因子调节与产生、造血或淋巴器官发育以及髓样细胞分化。其中，25个基因被归类为风险基因，10个为邻近基因。我们确定了9种新的风险蛋白（RELA、ETS1、NFATC1、BATF、LCK、PIK3R1、PRKCB、RASGRP1和FYN）作为RA的潜在治疗靶点，它们在疾病发病机制中发挥了重要作用。
本研究提供了对RA遗传风险因素的全面综合分子网络和知识图谱分析，为RA研究中整合多组学数据提供了坚实的框架。这些发现可能有助于更准确的临床决策和靶向治疗方案的开发。此外，本研究强调了基于疾病关联和功能相似性推断网络间隐藏关系的重要性，进一步加深了我们对RA发病机制的理解。
