Evidence map›Paper›PMID 42421966›Full record

ArticleFrontiers in immunology2026

Integrating genetic, epigenetic, and clinical signatures via machine learning for robust prediction of leflunomide response in rheumatoid arthritis: a multi-center validation study.

Meng Chen, Haina Liu, Lei Jin, Xin Feng, Bingbing Dai, Fang Wang, Qiao Wang, Yulan Chen, Man Yi, Bowen Jia and 10 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Meng ChenDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Haina LiuDepartment of Rheumatology, The First Hospital of China Medical University, Shenyang, China.
Lei JinDepartment of Rheumatology, ShengJing Hospital of China Medical University, Shenyang, China.
Xin FengDepartment of Rheumatology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.
Bingbing DaiDepartment of Rheumatology and Immunology, Central Hospital of Dalian University of Technology, Dalian, China.
Fang WangDepartment of Rheumatology, The First Hospital of China Medical University, Shenyang, China.
Qiao WangDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Yulan ChenDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Man YiDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Bowen JiaDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Kangyi DongDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Jintao ZhangDepartment of Rheumatology and Immunology, Central Hospital of Dalian University of Technology, Dalian, China.
Zhijun FanDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Jiahui LiDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Feng ZhaoDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Yuanyuan JiaDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Jianpeng WangDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Miao LiuDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Jiayi XuDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.
Lingyu FuDepartment of Clinical Epidemiology and Evidence-Based Medicine, the First Hospital of China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To develop and validate a machine learning(ML)-based integrated predictive model combining genetic, epigenetic, and clinical factors for predicting leflunomide (LEF) treatment response in rheumatoid arthritis (RA) patients. Methods: A total of 357 RA patients (231 in the model development cohort [MDC], 126 in the external validation cohort [EVC]) were recruited from multiple centers in China. Whole-exome sequencing(WES), genome-wide DNA methylation profiling, and comprehensive clinical data were integrated for model development. Feature selection was performed via univariate analysis, Least Absolute Shrinkage and Selection Operator(LASSO) regression, and clinical feasibility filtering. Ten ML algorithms were tested, with SHapley Additive exPlanations (SHAP) for interpretability, and external validation to assess generalizability. Results: The final integrated model included 3 single nucleotide polymorphisms (SNPs: Conclusion: The integrated clinical-genetic/epigenetic RF model enables reliable prediction of LEF response in RA. Multi-omics integration showed superior performance in the MDC, while maintaining robust and non-inferior performance in EVC. The methylation-dependent interaction between cg07694252-

Indexed as

Antirheumatic AgentsArthritis, RheumatoidEpigenesis, GeneticLeflunomideMachine LearningAdultDNA MethylationFemaleHumansMaleMiddle AgedMultidrug Resistance-Associated Protein 2Polymorphism, Single NucleotidePredictive Learning ModelsTreatment OutcomeABCC2 protein, humanAntirheumatic AgentsLeflunomideMultidrug Resistance-Associated Protein 2DNA methylationleflunomidemachine learningrheumatoid arthritissingle nucleotide polymorphism

Identifiers

PMID42421966
PMCPMC13342399

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.