Evidence map›Paper›PMID 42079609›Full record

ArticleFrontiers in immunology2026

Immune cells with senescence-related transcriptional signatures orchestrate the inflammatory continuum in osteoarthritis synovium: a single-cell and machine learning study.

Chunrong He, Xianghan Wang, Peiguan Huang, Jianxin Jiang, Xiaoxu Wang

Abstract read
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. Cited by 1 paper.

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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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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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

5 authors.

Chunrong HeDepartment of Trauma Medical Center, Daping Hospital, State Key Laboratory of Trauma and Chemical Poisoning, Army Medical University, Chongqing, China.
Xianghan WangDepartment of Orthopedic Surgery, The Second Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Peiguan HuangDepartment of Orthopedic Surgery, The Second Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Jianxin JiangDepartment of Trauma Medical Center, Daping Hospital, State Key Laboratory of Trauma and Chemical Poisoning, Army Medical University, Chongqing, China.
Xiaoxu WangDepartment of Orthopedic Surgery, The Second Affiliated Hospital, University of South China, Hengyang, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Synovitis is a key driver of osteoarthritis (OA), characterized by a chronic inflammatory microenvironment. However, the contribution of immune-cell senescence to synovial pathology and its therapeutic potential remains poorly understood. This study aims to map the senescent immune landscape and identify actionable targets for OA therapy. Methods: Single-cell RNA dataset (GSE152805) of OA synovium was used to identify immune subsets with senescence-related transcriptional signatures and subset-specific gene signatures. These gene signatures served as machine-learning features to train and validate diagnostic models in a combine dataset of 4 OA transcriptomic datasets (GSE55235, GSE55457, GSE82107, GSE169077) using Random Forest, Support Vector Machine, Gradient Boosting Machine, and eXtreme Gradient Boosting. Immunofluorescence staining and Western blotting of human synovial tissues validated macrophage-associated markers. Pseudotime trajectory and cell-cell communication analyses defined temporal dynamics and intercellular communication. Drug-gene interaction analysis and molecular docking were performed to assess therapeutic potential. Results: Macrophages, mast cells, and dendritic cells were identified as the immune populations with senescence-related transcriptional signatures. Pseudotime and communication analyses revealed a coordinated inflammatory continuum: macrophages initiated and sustained synovial inflammation, mast cells amplified intermediate inflammatory responses, and dendritic cells contributed to late-stage matrix remodeling. Machine-learning models identified hub biomarkers, with RHOB and PDK4 emerging as the macrophage-associated biomarkers with therapeutic potential. RHOB and PDK4 were markedly upregulated and colocalized with CD68 Conclusion: This study characterizes the transcriptomic landscape of senescence-related features in the OA synovial microenvironment. We identify RHOB and PDK4 as key macrophage-associated therapeutic targets, providing novel insights for immunomodulatory strategies in OA treatment.

Indexed as

Cellular SenescenceMachine LearningOsteoarthritisSynovial MembraneSynovitisTranscriptomeDendritic CellsGene Expression ProfilingHumansInflammationMacrophagesMast CellsSingle-Cell AnalysisSingle-Cell Gene Expression Analysisimmune microenvironmentimmunosenescencemachine learningosteoarthritisRHOBsynovitis

Identifiers

PMID42079609
PMCPMC13132789

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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.