Evidence map›Paper›PMID 41652638›Full record

ArticleBMC rheumatology2026

NRAS and SREBF1 identified as mitophagy-associated risk genes for rheumatoid arthritis through interpretable machine learning and experimental validation studies.

Hongman Li, Peng Liu, Xiao Han, Shengyong Si, ZhiJing Li, Chun Zhao, Xue Zhao, Xue Zhou, Mengdie Liu

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Article in BMC rheumatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Hongman LiDepartment of Hypertension, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Peng LiuDepartment of Hepatic-Biliary-Pancreatic Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China. lhmgyfy@163.com.
Xiao HanDepartment of Hypertension, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Shengyong SiDepartment of Hypertension, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
ZhiJing LiDepartment of Hypertension, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Chun ZhaoMedical Examination Center, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Xue ZhaoGuizhou Medical University, Guiyang, Guizhou, China.
Xue ZhouDepartment of Hypertension, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Mengdie LiuGuiyang Public Health Treatment Center, Guiyang, Guizhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMitophagy has been implicated in the pathogenesis of rheumatoid arthritis (RA), particularly in fibroblast-like synoviocytes (FLSs), yet its regulatory landscape and diagnostic relevance remain incompletely understood. This study aimed to identify mitophagy-associated genes (MRGs) in RA and to explore their potential diagnostic value using integrative bioinformatics and interpretable machine learning approaches.

methodsTranscriptomic datasets of RA patients and healthy controls were retrieved from the Gene Expression Omnibus (GEO). Mitophagy-related differentially expressed genes were identified using limma-based differential analysis and functional enrichment. Multiple supervised machine learning algorithms were applied within a unified modeling framework to prioritize feature genes and evaluate diagnostic performance. NRAS expression was further examined in TNF-α–stimulated MH7A cells and a collagen-induced arthritis (CIA) rat model. In addition, exploratory drug–gene interaction analysis and molecular docking were performed to identify potential compounds targeting NRAS-related pathways.

resultsEight mitophagy-related differentially expressed genes were identified, among which NRAS and SREBF1 were consistently selected as key features across multiple machine learning models, showing robust discriminative performance (AUC > 0.90). A logistic regression model based on these genes demonstrated stable diagnostic performance in internal and external validation cohorts. NRAS was significantly upregulated in RA and was experimentally confirmed in vitro and in vivo. Unsupervised clustering further revealed distinct mitophagy-associated RA subtypes with differential immune infiltration patterns and functional pathway enrichment. Drug–gene interaction analysis and molecular docking suggested Binimetinib and Sotorasib as candidate compounds with potential relevance to NRAS signaling.

conclusionNRAS and SREBF1 were identified as mitophagy-associated genes with diagnostic relevance in RA. NRAS, in particular, showed consistent expression changes and experimental validation, supporting its potential involvement in mitophagy-related regulatory networks. These findings provide integrative and hypothesis-generating insights into mitophagy-associated mechanisms in RA and may inform future diagnostic and therapeutic studies.

Indexed as

BiomarkersDiagnostic modelMachine learningMitophagyMolecular dockingRA

Identifiers

PMID41652638
PMCPMC12977777

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