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

周巧; 刘健; 朱艳; 汪元; 王桂珍; 齐亚军; 胡月迪

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

摘要:
目的本研究旨在识别骨关节炎（osteoarthritis, OA）中炎性衰老生物标志物。
方法 GEO（Gene Expression Omnibus）数据库获得年轻OA和老年OA微阵列基因谱，人类衰老基因组资源数据库（Human Aging Genome Resource, HAGR）获得衰老相关基因（aging-related genes, ARGs）。筛选获得年轻OA与老年OA的差异基因，再与ARGs取交集得到OA衰老相关基因。富集分析揭示OA衰老相关标志物的潜在机制。3种机器学习方法识别OA核心衰老标志物，受试者工作特征（receiver operating characteristic curve, ROC）曲线评估其诊断OA炎性衰老的能力。收集临床OA患者外周血单核细胞验证衰老相关分泌表型（senescence-associated secretory phenotype, SASP）因子和衰老标志物的表达。
结果总共获得45个衰老相关标志物，主要参与对细胞衰老、细胞周期、炎症反应等的调控。3种机器方法筛选得出5个核心衰老标志物（FOXO3、MCL1、SIRT3、STAG1和S100A13）。纳入20例正常组，40例OA患者，包括年轻组和老年组各20例。与年轻组相比，老年组OA中C反应蛋白（C-reactive protein, CRP）、白细胞介素（interleukin, IL）-6、IL-1β上升，IL-4水平下降（P<0.01）；FOXO3、MCL1、SIRT3 mRNA表达下降，STAG1和S100A13 mRNA表达上升（P<0.01）。Pearson相关性分析表明选定的标志物与红细胞沉降率（erythrocyte sedimentation rate, ESR）、IL-1β、IL-4、CRP、IL-6指标相关。5个核心衰老基因ROC曲线下面积均大于0.8，列线图预测模型中校正曲线的C-index为0.755，模型校准能力较好。
结论 FOXO3、MCL1、SIRT3、STAG1和S100A13可作为OA炎性衰老的新型诊断分子标志物和潜在治疗靶点。

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.

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