Evidence map›Paper›PMID 42653491›Full record

ArticleInternational journal of molecular sciences2026

Identification of Cellular Senescence-Related Hub Genes in Rheumatoid Arthritis from Bioinformatics Analysis Through Machine Learning up to Verifications in Mouse Macrophages and Tests in Patients.

Dandan Wang, Linkun Tian, Qingshan Ma, Zhengdong Zhang, Yi Wang, Junhao Fang, Hairong Xu, Qi Chen, Hongdian Chen, Fangyuan Wang and 3 more

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

13 authors.

Dandan WangSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Linkun TianSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Qingshan MaSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Zhengdong ZhangSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Yi WangSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Junhao FangSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Hairong XuSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Qi ChenSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Hongdian ChenSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Fangyuan WangSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Qiaoyan ZhangSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Quanlong ZhangSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.ORCID 0000-0002-1680-4869
Luping QinSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China.ORCID 0000-0002-5056-6497

Funding

the Key Project of the National Natural Science Foundation Joint Fund U2202213Zhejiang Provincial Natural Science Foundation LHDMZ23H280001
6 · The paper itself

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and joint destruction. Cellular senescence contributes to chronic inflammation, yet key senescence-associated regulators in RA remain unclear. This study aimed to identify RA senescence-related signatures and their roles. Using GSE89408 as the training cohort, we screened hub genes by intersecting differentially expressed and senescence-related genes via WGCNA and three machine learning algorithms, with GSE55457 for external validation. Immune infiltration, regulatory network, subtyping and drug prediction were analyzed. Clinical and in vitro assays validated RIPK2 expression and function in the macrophage senescence-like phenotype, with preliminary signaling exploration. Three senescence-related hub genes (TNFAIP6, SLC2A3, RIPK2) were identified. The derived nomogram showed robust diagnostic performance (AUC = 0.988). Hub genes correlated strongly with myeloid cells, especially macrophages. Two immunologically distinct RA subtypes were identified. RIPK2 was upregulated in clinical samples; its inhibition attenuated LPS-induced macrophage senescence-like changes and inflammation. Preliminary data suggested RIPK2 may act via the NF-κB pathway. This study identifies RA senescence-associated signatures, revealing RIPK2 linking innate immunity to macrophage senescence-like changes, offering novel insights into pathogenesis and supporting it as a candidate biomarker and therapeutic target.

Indexed as

Arthritis, RheumatoidCellular SenescenceComputational BiologyMachine LearningMacrophagesReceptor-Interacting Protein Serine-Threonine Kinase 2AnimalsGene Expression ProfilingGene Regulatory NetworksHumansMiceNF-kappa BSignal TransductionTumor Necrosis Factor alpha-Induced Protein 3NF-kappa BReceptor-Interacting Protein Serine-Threonine Kinase 2Ripk2 protein, mouseTnfaip3 protein, mouseTumor Necrosis Factor alpha-Induced Protein 3cellular senescencemachine learningmacrophagerheumatoid arthritisRIPK2

Identifiers

PMID42653491
PMCPMC13513530

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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.