四川大学学报（医学版）2024，Vol.55Issue(2)：279-289,11.DOI:10.12182/20240360106
生物信息学和机器学习策略识别骨关节炎炎性衰老生物标志物与临床验证
Identification of Osteoarthritis Inflamm-Aging Biomarkers by Integrating Bioinformatic Analysis and Machine Learning Strategies and the Clinical Validation
摘要
Abstract
Objective To identify inflamm-aging related biomarkers in osteoarthritis(OA).Methods Microarray gene profiles of young and aging OA patients were obtained from the Gene Expression Omnibus(GEO)database and aging-related genes(ARGs)were obtained from the Human Aging Genome Resource(HAGR)database.The differentially expressed genes of young OA and older OA patients were screened and then intersected with ARGs to obtain the aging-related genes of OA.Enrichment analysis was performed to reveal the potential mechanisms of aging-related markers in OA.Three machine learning methods were used to identify core senescence markers of OA and the receiver operating characteristic(ROC)curve was used to assess their diagnostic performance.Peripheral blood mononuclear cells were collected from clinical OA patients to verify the expression of senescence-associated secretory phenotype(SASP)factors and senescence markers.Results A total of 45 senescence-related markers were obtained,which were mainly involved in the regulation of cellular senescence,the cell cycle,inflammatory response,etc.Through the screening with the three machine learning methods,5 core senescence biomarkers,including FOXO3,MCL1,SIRT3,STAG1,and S100A13,were obtained.A total of 20 cases of normal controls and 40 cases of OA patients,including 20 cases in the young patient group and 20 in the elderly patient group,were enrolled.Compared with those of the young patient group,C-reactive protein(CRP),interleukin(IL)-6,and IL-1β levels increased and IL-4 levels decreased in the elderly OA patient group(P＜0.01);FOXO3,MCL1,and SIRT3 mRNA expression decreased and STAG1 and S100A13 mRNA expression increased(P＜0.01).Pearson correlation analysis demonstrated that the selected markers were associated with some indicators,including erythrocyte sedimentation rate(ESR),IL-1β,IL-4,CRP,and IL-6.The area under the ROC curve of the 5 core aging genes was always greater than 0.8 and the C-index of the calibration curve in the nomogram prediction model was 0.755,which suggested the good calibration ability of the model.Conclusion FOXO3,MCL1,SIRT3,STAG1,and S100A13 may serve as novel diagnostic biomolecular markers and potential therapeutic targets for OA inflamm-aging.关键词
骨关节炎/炎性衰老/衰老相关分泌表型/机器学习/生物标志物Key words
Osteoarthritis/Inflamm-aging/Senescence-associated secretory phenotype/Machine learning/Biomarkers引用本文复制引用
周巧,刘健,朱艳,汪元,王桂珍,齐亚军,胡月迪..生物信息学和机器学习策略识别骨关节炎炎性衰老生物标志物与临床验证[J].四川大学学报（医学版）,2024,55(2):279-289,11.基金项目
国家自然基金面上项目(No.82274310)、安徽省高等学校科学研究项目(自然科学类)重点项目(No.2022AH050449)、安徽省中医药领军人才项目(中医药发展秘[2018]23号)、高水平中医药重点学科建设项目——中医痹病学(No.zyyzdxk-2023100)、青年人才培养项目"杏林青秀培育计划"(No.0500-48-65)、安徽省高校2023年自然科学重大项目(No.2023AH040112)和安徽现代中医内科应用基础与开发研究省级实验室(No.2016080503B041)资助 （No.82274310）
