Evidence map›Paper›PMID 40083558›Full record

ArticleFrontiers in immunology2025

Identify the potential target of efferocytosis in knee osteoarthritis synovial tissue: a bioinformatics and machine learning-based study.

Shangbo Niu, Mengmeng Li, Jinling Wang, Peirui Zhong, Xing Wen, Fujin Huang, Linwei Yin, Yang Liao, Jun Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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

9 authors.

Shangbo NiuRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Mengmeng LiRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Jinling WangRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Peirui ZhongRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Xing WenRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Fujin HuangRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Linwei YinRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Yang LiaoRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
Jun ZhouRehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Knee osteoarthritis (KOA) is a degenerative joint disease characterized by the progressive deterioration of cartilage and synovial inflammation. A critical mechanism in the pathogenesis of KOA is impaired efferocytosis in synovial tissue. The present study aimed to identify and validate key efferocytosis-related genes (EFRGs) in KOA synovial tissue by using comprehensive bioinformatics and machine learning approaches. Methods: We integrated three datasets (GSE55235, GSE55457, and GSE12021) from the Gene Expression Omnibus database to screen differentially expressed genes (DEGs) associated with efferocytosis and performed weighted gene co-expression network analysis. Subsequently, we utilized univariate logistic regression analysis, least absolute shrinkage and selection operator regression, support vector machine, and random forest algorithms to further refine these genes. The results were then inputted into multivariate logistic regression analysis to construct a diagnostic nomogram. Public datasets and quantitative real-time PCR experiments were employed for validation. Additionally, immune infiltration analysis was conducted with CIBERSORT using the combined datasets. Results: Analysis of the intersection between DEGs and EFRGs identified 12 KOA-related efferocytosis DEGs. Further refinement through machine learning algorithms and multivariate logistic regression revealed UCP2, CX3CR1, and CEBPB as hub genes. Immune infiltration analysis demonstrated significant correlations between immune cell components and the expression levels of these hub genes. Validation using independent datasets and experimental approaches confirmed the robustness of these findings. Conclusions: This study successfully identified three hub genes (UCP2, CX3CR1, and CEBPB) with significant expression alterations in KOA, demonstrating high diagnostic potential and close associations with impaired efferocytosis. These targets may modulate synovial efferocytosis-related immune processes, offering novel therapeutic avenues for KOA intervention.

Indexed as

Computational BiologyMachine LearningOsteoarthritis, KneePhagocytosisSynovial MembraneDatabases, GeneticEfferocytosisGene Expression ProfilingGene Regulatory NetworksHumansbioinformatics analysisefferocytosis-related genesimmune infiltrationknee osteoarthritismachine learning

Identifiers

PMID40083558
PMCPMC11903261

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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.