Evidence map›Paper›PMID 42877285›Full record

ArticleClinics in orthopedic surgery2026

Identification of Mitochondrial Dynamics-Related Genes as Diagnostic Biomarkers for Osteoarthritis Using Weighted Gene Co-expression Network Analysis and Machine Learning.

Hui Wang, Peng Zhang, Xinqiang Ren, Kaitao Tan, Yongfeng Qiu, Linqing Li

Abstract read
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Article in Clinics in orthopedic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hui WangDepartment of Orthopedics I, Xi'an Qinhuang Hospital, Xi'an, China.ORCID https://orcid.org/0009-0003-3712-1439
Peng ZhangDepartment of Orthopedics I, Xi'an Qinhuang Hospital, Xi'an, China.ORCID https://orcid.org/0009-0007-8734-4007
Xinqiang RenDepartment of Orthopedics I, Xi'an Qinhuang Hospital, Xi'an, China.ORCID https://orcid.org/0009-0005-0151-5107
Kaitao TanDepartment of Orthopedics I, Xi'an Qinhuang Hospital, Xi'an, China.ORCID https://orcid.org/0009-0005-6784-6590
Yongfeng QiuDepartment of Orthopedic Trauma, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China.ORCID https://orcid.org/0009-0005-1791-468X
Linqing LiDepartment of Orthopedics II, Yan'an University Xianyang Hospital, Xianyang, China.ORCID https://orcid.org/0009-0005-3195-5672

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Osteoarthritis (OA) is a chronic, painful joint disease whose early diagnosis is critical for effective treatment. Since mitochondrial dynamics have been implicated in the onset and progression of OA, this study aimed to explore the diagnostic value of mitochondrial dynamics-related genes (MDRGs) in OA. Methods: Weighted gene co-expression network analysis and 3 machine learning algorithms, including least absolute shrinkage and selection operator regression, support vector machine-recursive feature elimination, and Boruta, were used to screen signature genes. The intersection of genes identified by all 3 algorithms was taken as the candidate set. Expression levels of these genes were compared between OA and normal tissues, and receiver operating characteristic curves were used to validate their diagnostic performance, yielding final hub genes. An Results: Two hub genes, CDH11 and MSX2, were identified. Both genes were up-regulated in OA and demonstrated promising diagnostic efficacy. qRT-PCR and Western blot validation revealed that mRNA and protein levels of CDH11 and MSX2 were significantly higher in OA cell models than in normal cells. Based on transcriptomic data, the single-sample gene set enrichment analysis algorithm inferred that significantly higher levels of B cells, macrophages, mast cells, neutrophils, T helper cells, and tumor-infiltrating lymphocytes were found in OA samples than in controls. Twenty functional analogs of the 2 hub genes were predicted, mainly enriched in skeletal system development and bone development functional pathways. Additionally, 34 miRNAs (e.g., hsa-miR-568, hsa-miR-548d-5p, hsa-miR-144) and 17 TFs (e.g., CEBPA, NFATC1, SP1, EGR1, FOXO1) were predicted based on CDH11 and MSX2. Entinostat, estradiol, cobaltous chloride, and valproic acid were identified as potential OA therapeutics. Conclusions: CDH11 and MSX2 may serve as candidate diagnostic biomarkers for OA, identified from a predefined MDRG set, but requiring further mechanistic and clinical validation.

Indexed as

CadherinsHomeodomain ProteinsMachine LearningMitochondrial DynamicsOsteoarthritisBiomarkersGene Regulatory NetworksHumansBiomarkersCadherinsHomeodomain ProteinsBiomarkersMachine learningMitochondrial dynamicsOsteoarthritisWeighted gene co-expression network analysis

Identifiers

PMID42877285
PMCPMC13662255

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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.