111732
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
利用机器学习探究强直性脊柱炎潜在生物标志物：关于线粒体和衰老通路的研究
Authors Yang L , Chang C, Tam W, Liang Y, Chen M, Zhang J, Li K, Li Y , Gong Q
Received 26 June 2025
Accepted for publication 11 October 2025
Published 4 November 2025 Volume 2025:18 Pages 15479—15500
DOI https://doi.org/10.2147/JIR.S549917
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Ujjwol Risal
Lu Yang,1,2,* Chitat Chang,1,* WengFai Tam,3 Yeqi Liang,2 Meiqi Chen,2 Jun Zhang,1 Ke Li,2 Yunhao Li,4 Qiming Gong5,6
1Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, People’s Republic of China; 2Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 3The First Affiliated Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 4Second Department of Traditional Chinese Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, People’s Republic of China; 5Department of Nephrology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, People’s Republic of China; 6Key Laboratory of Medical Research Basic Guarantee for Immune-Related Diseases Research of Guangxi, Baise, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qiming Gong, Department of Nephrology, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2nd Road, Youjiang District, Baise, Guangxi, 533000, People’s Republic of China, Email 15610398015@163.com Yunhao Li, Second Department of Traditional Chinese Medicine, South China Hospital, Medical School, Shenzhen University, No. 1 Fuxin Street, Pinghu Sub-district, Longgang District, Shenzhen, Guangdong, 518116, People’s Republic of China, Email 2246811138@qq.com
Objective: Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disorder characterized by inflammation and pathological bone formation. Growing evidence suggests that mitochondrial dysfunction and cellular senescence are key drivers of disease progression. This study aimed to identify novel biomarkers linking these processes to AS.
Methods: Transcriptomic datasets of AS patients and controls were analyzed to identify differentially expressed genes related to mitochondrial function and cellular senescence. Bioinformatics pipelines and multiple machine learning algorithms were used to screen candidate biomarkers, which were further validated in an independent dataset and in a collagen antibody-induced arthritis (CAIA) mouse model. Clinical diagnostic value was assessed using receiver operating characteristic analysis.
Results: We identified 25 mitochondrial- and 8 senescence-related genes differentially expressed in AS. Consensus machine learning analysis highlighted COX17 and MATK as robust candidates with significant diagnostic performance. Immune infiltration analysis suggested strong correlations between these genes and altered immune cell subsets. In vivo validation confirmed upregulation of COX17 and downregulation of MATK in the AS mouse model, accompanied by enhanced osteogenic activity.
Conclusion: COX17 and MATK are promising biomarkers linking mitochondrial dysfunction and cellular senescence to AS. Their diagnostic potential highlights new avenues for improving early disease detection and personalized therapeutic strategies.
Keywords: ankylosing spondylitis, mitochondrial dysfunction, cellular senescence, machine learning, transcriptomic analysis
1Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, People’s Republic of China; 2Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 3The First Affiliated Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 4Second Department of Traditional Chinese Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, People’s Republic of China; 5Department of Nephrology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, People’s Republic of China; 6Key Laboratory of Medical Research Basic Guarantee for Immune-Related Diseases Research of Guangxi, Baise, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qiming Gong, Department of Nephrology, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2nd Road, Youjiang District, Baise, Guangxi, 533000, People’s Republic of China, Email 15610398015@163.com Yunhao Li, Second Department of Traditional Chinese Medicine, South China Hospital, Medical School, Shenzhen University, No. 1 Fuxin Street, Pinghu Sub-district, Longgang District, Shenzhen, Guangdong, 518116, People’s Republic of China, Email 2246811138@qq.com
Objective: Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disorder characterized by inflammation and pathological bone formation. Growing evidence suggests that mitochondrial dysfunction and cellular senescence are key drivers of disease progression. This study aimed to identify novel biomarkers linking these processes to AS.
Methods: Transcriptomic datasets of AS patients and controls were analyzed to identify differentially expressed genes related to mitochondrial function and cellular senescence. Bioinformatics pipelines and multiple machine learning algorithms were used to screen candidate biomarkers, which were further validated in an independent dataset and in a collagen antibody-induced arthritis (CAIA) mouse model. Clinical diagnostic value was assessed using receiver operating characteristic analysis.
Results: We identified 25 mitochondrial- and 8 senescence-related genes differentially expressed in AS. Consensus machine learning analysis highlighted COX17 and MATK as robust candidates with significant diagnostic performance. Immune infiltration analysis suggested strong correlations between these genes and altered immune cell subsets. In vivo validation confirmed upregulation of COX17 and downregulation of MATK in the AS mouse model, accompanied by enhanced osteogenic activity.
Conclusion: COX17 and MATK are promising biomarkers linking mitochondrial dysfunction and cellular senescence to AS. Their diagnostic potential highlights new avenues for improving early disease detection and personalized therapeutic strategies.
Keywords: ankylosing spondylitis, mitochondrial dysfunction, cellular senescence, machine learning, transcriptomic analysis