Zhou Qiao, Liu Jian, Zhu Yan, Wang Yuan, Wang Guizhen, Qi Yajun, Hu Yuedi
（ 230061） Department of Geriatrics, The Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei 230061, China.
（ 230012） First School of Clinical Medicine, Anhui University of Chinese Medicine, Hefei 230012, China.
Sichuan Da Xue Xue Bao Yi Xue Ban. 2024 Mar 20;55(2):279-289. doi: 10.12182/20240360106.
To identify inflamm-aging related biomarkers in osteoarthritis (OA).
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.
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 3, 1, 3, 1, and 10013, 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 (<0.01); 3, 1, and 3 mRNA expression decreased and 1 and 10013 mRNA expression increased (<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.
3, 1, 3, 1, and 10013 may serve as novel diagnostic biomolecular markers and potential therapeutic targets for OA inflamm-aging.
鉴定骨关节炎（OA）中与炎症衰老相关的生物标志物。
从基因表达综合数据库（GEO）获取年轻和老年OA患者的基因芯片图谱，从人类衰老基因组资源（HAGR）数据库获取衰老相关基因（ARG）。筛选年轻OA患者和老年OA患者的差异表达基因，然后与ARG进行交集分析以获得OA的衰老相关基因。进行富集分析以揭示OA中衰老相关标志物的潜在机制。使用三种机器学习方法鉴定OA的核心衰老标志物，并使用受试者工作特征（ROC）曲线评估其诊断性能。从临床OA患者中收集外周血单核细胞，以验证衰老相关分泌表型（SASP）因子和衰老标志物的表达。
共获得了总共45个衰老相关标志物，主要参与细胞衰老、细胞周期、炎症反应等的调节。通过三种机器学习方法筛选，获得了5个核心衰老生物标志物，包括3、1、3、1和10013。共纳入20例正常对照和40例OA患者，其中年轻患者组20例，老年患者组20例。与年轻患者组相比，老年OA患者组C反应蛋白（CRP）、白细胞介素（IL）-6和IL-1β水平升高，IL-4水平降低（<0.01）；3、1和3的mRNA表达降低，1和10013的mRNA表达升高（<（0.01）。Pearson相关性分析表明，所选标志物与一些指标相关，包括红细胞沉降率（ESR）、IL-1β、IL-4、CRP和IL-6。5个核心衰老基因的ROC曲线下面积始终大于0.8，列线图预测模型中校准曲线的C指数为0.755，表明该模型具有良好的校准能力。
3、1、3、1和1'0013可能作为OA炎症衰老的新型诊断生物分子标志物和潜在治疗靶点。
