120017
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
通过生物信息学和实验方法探索骨关节炎中中心体扩增相关生物标志物
Authors Nie S, Zhao P, Chen Z, Wu S, Li M , Lian C, Zhang H
Received 11 November 2025
Accepted for publication 26 January 2026
Published 4 February 2026 Volume 2026:19 576712
DOI https://doi.org/10.2147/JIR.S576712
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Ujjwol Risal
Shixin Nie,1,* Pei Zhao,1,* Zhi Chen,1 Siyi Wu,2 Mingxuan Li,2 Chengjie Lian,1 Hua Zhang1
1Department of Orthopedics, Sports Injury Division, Fujian Medical University Union Hospital, Fuzhou, Fujian, People’s Republic of China; 2Fujian Medical University, Fuzhou, Fujian, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hua Zhang; Chengjie Lian, Email zhanghua@fjmu.edu.cn; lianchj@hospital.cqmu.edu.cn
Purpose: This study aimed to identify centrosome amplification-related genes (CA-RGs) as potential biomarkers in osteoarthritis (OA).
Methods: Publicly available databases provided the transcriptome datasets for OA, while a well-established list of CA-related genes was obtained from published resources. A comprehensive computational biology strategy was implemented, integrating differential gene expression profiling, weighted gene co-expression network analysis (WGCNA), Mendelian randomization (MR) analysis, and ROC curve evaluation to screen for potential diagnostic markers. Following this, a predictive nomogram was developed, accompanied by gene set enrichment analysis (GSEA) and immune cell infiltration characterization utilizing the identified biomarkers. Additionally, single-cell RNA sequencing (scRNA-seq) datasets were examined to determine critical cell subpopulations and track biomarker expression patterns across different cell types. Ultimately, clinical specimen analysis through RT-qPCR was conducted to experimentally confirm the expression profiles of these biomarkers.
Results: PLK2 and SUN2 were identified as biomarkers. A nomogram model based on them demonstrated high predictive accuracy for OA. GSEA indicated both biomarkers were strongly correlated with lysosomal pathways. Immune infiltration analysis revealed significant differences in 16 immune cell types between OA and control groups. scRNA-seq analysis pinpointed two key cell clusters: preinflammatory chondrocytes (preInfC) and hypertrophic chondrocytes (HTC). Pseudotime trajectory analysis further uncovered dynamic expression patterns: PLK2 showed a sustained increase in HTC, while SUN2 displayed a dip then rise. In preInfC, PLK2 expression fluctuated (increase-decrease-increase), whereas SUN2 rose initially then declined.
Conclusion: This study identified 2 biomarkers associated with CA and explored their underlying mechanisms of action, providing new insights into potential therapeutic strategies for OA.
Keywords: osteoarthritis, centrosome amplification, Mendelian randomization analysis, biomarker, single-cell RNA analysis
1Department of Orthopedics, Sports Injury Division, Fujian Medical University Union Hospital, Fuzhou, Fujian, People’s Republic of China; 2Fujian Medical University, Fuzhou, Fujian, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hua Zhang; Chengjie Lian, Email zhanghua@fjmu.edu.cn; lianchj@hospital.cqmu.edu.cn
Purpose: This study aimed to identify centrosome amplification-related genes (CA-RGs) as potential biomarkers in osteoarthritis (OA).
Methods: Publicly available databases provided the transcriptome datasets for OA, while a well-established list of CA-related genes was obtained from published resources. A comprehensive computational biology strategy was implemented, integrating differential gene expression profiling, weighted gene co-expression network analysis (WGCNA), Mendelian randomization (MR) analysis, and ROC curve evaluation to screen for potential diagnostic markers. Following this, a predictive nomogram was developed, accompanied by gene set enrichment analysis (GSEA) and immune cell infiltration characterization utilizing the identified biomarkers. Additionally, single-cell RNA sequencing (scRNA-seq) datasets were examined to determine critical cell subpopulations and track biomarker expression patterns across different cell types. Ultimately, clinical specimen analysis through RT-qPCR was conducted to experimentally confirm the expression profiles of these biomarkers.
Results: PLK2 and SUN2 were identified as biomarkers. A nomogram model based on them demonstrated high predictive accuracy for OA. GSEA indicated both biomarkers were strongly correlated with lysosomal pathways. Immune infiltration analysis revealed significant differences in 16 immune cell types between OA and control groups. scRNA-seq analysis pinpointed two key cell clusters: preinflammatory chondrocytes (preInfC) and hypertrophic chondrocytes (HTC). Pseudotime trajectory analysis further uncovered dynamic expression patterns: PLK2 showed a sustained increase in HTC, while SUN2 displayed a dip then rise. In preInfC, PLK2 expression fluctuated (increase-decrease-increase), whereas SUN2 rose initially then declined.
Conclusion: This study identified 2 biomarkers associated with CA and explored their underlying mechanisms of action, providing new insights into potential therapeutic strategies for OA.
Keywords: osteoarthritis, centrosome amplification, Mendelian randomization analysis, biomarker, single-cell RNA analysis