Evidence map›Paper›PMID 38741149›Full record

ArticleArthritis research & therapy2024

Identification and verification of a novel signature that combines cuproptosis-related genes with ferroptosis-related genes in osteoarthritis using bioinformatics analysis and experimental validation.

Baoqiang He, Yehui Liao, Minghao Tian, Chao Tang, Qiang Tang, Fei Ma, Wenyang Zhou, Yebo Leng, Dejun Zhong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Arthritis research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Cuproptosis and its potential role in musculoskeletal disease.Frontiers in cell and developmental biology · 2025
    Review
  4. Review
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.

Baoqiang He *Department of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China.
Yehui Liao *Department of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China.
Minghao TianDepartment of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China.
Chao TangDepartment of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China.
Qiang TangDepartment of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China.
Fei MaDepartment of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China.
Wenyang ZhouDepartment of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China.
Yebo LengDepartment of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China. 906112714@qq.com.
Dejun ZhongDepartment of Orthopedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taping Street, Lu Zhou City, China. zdj_1974@163.com.

Funding

Affiliated Hospital of Southwest Medical University No. 22155Sichuan Medical Association No. S17075, Q22008, Q21005Sichuan Student Innovation and Entrepreneurship Training Program Project No. S202010632174
6 · The paper itself

Abstract

backgroundExploring the pathogenesis of osteoarthritis (OA) is important for its prevention, diagnosis, and treatment. Therefore, we aimed to construct novel signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs) to explore the pathogenesis of OA and aid in its treatment. MATERIALS AND

methodsDifferentially expressed c-FRGs (c-FDEGs) were obtained using R software. Enrichment analysis was performed and a protein-protein interaction (PPI) network was constructed based on these c-FDEGs. Then, seven hub genes were screened. Three machine learning methods and verification experiments were used to identify four signature biomarkers from c-FDEGs, after which gene set enrichment analysis, gene set variation analysis, single-sample gene set enrichment analysis, immune function analysis, drug prediction, and ceRNA network analysis were performed based on these signature biomarkers. Subsequently, a disease model of OA was constructed using these biomarkers and validated on the GSE82107 dataset. Finally, we analyzed the distribution of the expression of these c-FDEGs in various cell populations.

resultsA total of 63 FRGs were found to be closely associated with 11 CRGs, and 40 c-FDEGs were identified. Bioenrichment analysis showed that they were mainly associated with inflammation, external cellular stimulation, and autophagy. CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as OA signature biomarkers, and their corresponding miRNAs and lncRNAs were predicted. Finally, scRNA-seq data analysis showed that the differentially expressed c-FRGs had significantly different expression distributions across the cell populations.

conclusionFour genes, namely CDKN1A, FZD7, GABARAPL2, and SLC39A14, are excellent biomarkers and prospective therapeutic targets for OA.

Indexed as

Computational BiologyFerroptosisOsteoarthritisAnimalsBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningProtein Interaction MapsBiomarkersBioinformaticsCuproptosisFerroptosisMachine learningOsteoarthritis

Identifiers

PMID38741149
PMCPMC11089679

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