Evidence map›Paper›PMID 40125081›Full record

ArticleJournal of inflammation research2025

Identification of Disulfidptosis-Related Genes and Molecular Subgroups in Rheumatoid Arthritis for Diagnostic Model and Patient Stratification.

Xinyi Liu, Siyao Wang, Xinru Du, Yulu Wang, Lingfei Mo, Hanchao Li, Zechao Qu, Xiaohao Wang, Jian Sun, Yuanyuan Li and 1 more

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

11 authors.

Xinyi LiuDepartment of Rheumatology and Immunology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.ORCID 0000-0003-3609-8479
Siyao WangDepartment of Gastroenterology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Xinru DuDepartment of Rheumatology and Immunology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Yulu WangXi'an Jiaotong University College of Medicine, Xi'an, People's Republic of China.
Lingfei MoDepartment of Rheumatology and Immunology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Hanchao LiDepartment of Rheumatology and Immunology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Zechao QuDepartment of Spine Surgery, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an, People's Republic of China.
Xiaohao WangDepartment of Spine Surgery, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an, People's Republic of China.
Jian SunInstitute of Endemic Diseases, School of Public Health & Key Laboratory of Trace Elements and Endemic Diseases, Xi'an Jiaotong University, Xi'an, People's Republic of China.
Yuanyuan LiDepartment of Rheumatology and Immunology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Jing WangDepartment of Rheumatology and Immunology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.ORCID 0000-0001-6351-6758

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cell death contributes to the pathogenesis of rheumatoid arthritis (RA) through various pathways. Disulfidptosis is a recently discovered novel form of cell death characterized by the abnormal accumulation of intracellular disulfide bonds. It remains unclear for the association between RA and disulfidptosis. Methods: A comprehensive analysis of three GEO datasets was presented in this study. First, the analysis involved the use of weighted gene co-expression network analysis (WGCNA) and differential analysis and were employed to identify the key module genes related to RA and disulfidptosis-related genes. The machine learning algorithms were used to identify the hub genes. Second, a diagnostic model was constructed for RA based on the hub genes. The nomogram and receiver operating characteristic (ROC) curves were utilized to evaluate the diagnostic value of the model. Third, two RA subtypes were identified based on hub genes by using consensus clustering analysis. Then, the disease activity scores, clinical markers, and immune cells were compared between the two RA subgroups. Finally, the differential expression of hub genes was validated between healthy controls and RA patients by qPCR. Results: Four hub genes (KLHL2, POLK, CLEC4D, NXT2) were identified. The expression of the four hub genes was verified to be significantly higher in RA patients compared with healthy controls. The superior diagnostic value of the model was validated, which demonstrated that the model outperforms each hub gene individually. Two subtypes of RA were determined. Patients in cluster A exhibited relatively lower levels of DAS28-CRP, DAS28-ESR, CDAI, SDAI, RF, CRP, and MMP3. In contrast, patients in cluster B had significantly higher levels of the above markers. Conclusion: Four hub genes were identified to provide unique insights into the role of disulfidptosis in RA. Additionally, a promising diagnosis model and patient stratification were established based on the hub genes to assess the risk of RA onset and RA disease activity.

Indexed as

bioinformaticsdiagnostic modeldisulfidptosismachine learningrheumatoid arthritis

Identifiers

PMID40125081
PMCPMC11930242

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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.