The Diagnostic Features of Peripheral Blood Biomarkers in Identifying Osteoarthritis Individuals: Machine Learning Strategies and Clinical Evidence.

IF 1.5 4区医学 Q4 CHEMISTRY, MEDICINAL Current computer-aided drug design Pub Date : 2024-01-01 DOI:10.2174/1573409920666230818092427

Qiao Zhou, Jian Liu, Ling Xin, Yuedi Hu, Yajun Qi

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Abstract

Background: People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic features and clinical value of peripheral blood biomarkers for osteoarthritis.

Objective: The goal of this project was to investigate the diagnostic features of peripheral blood and immune cell infiltration in osteoarthritis (OA).

Methods: Two eligible datasets (GSE63359 and GSE48556) were obtained from the GEO database to discern differentially expressed genes (DEGs). The machine learning strategy was employed to filtrate diagnostic biomarkers for OA. Additional verification was implemented by collecting clinical samples of OA. The CIBERSORT website estimated relative subsets of RNA transcripts to evaluate the immune-inflammatory states of OA. The link between specific DEGs and clinical immune-inflammatory markers was found by correlation analysis.

Results: Overall, 67 robust DEGs were identified. The nuclear receptor subfamily 2 group C member 2 (NR2C2), transcription factor 4 (TCF4), stromal antigen 1 (STAG1), and interleukin 18 receptor accessory protein (IL18RAP) were identified as effective diagnostic markers of OA in peripheral blood. All four diagnostic markers showed significant increases in expression in OA. Analysis of immune cell infiltration revealed that macrophages are involved in the occurrence of OA. Candidate diagnostic markers were correlated with clinical immune-inflammatory indicators of OA patients.

Conclusion: We highlight that DEGs associated with immune inflammation (NR2C2, TCF4, STAG1, and IL18RAP) may be potential biomarkers for peripheral blood in OA, which are also associated with clinical immune-inflammatory indicators.

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外周血生物标记物在识别骨关节炎个体中的诊断特征：机器学习策略与临床证据

背景：骨关节炎患者给社会带来巨大负担。早期诊断对预防疾病进展和更有效地选择最佳治疗策略至关重要。本研究旨在探讨骨关节炎外周血生物标志物的诊断特征和临床价值：本项目旨在研究骨关节炎（OA）外周血和免疫细胞浸润的诊断特征：方法：从GEO数据库中获取两个符合条件的数据集（GSE63359和GSE48556），以发现差异表达基因（DEGs）。采用机器学习策略筛选出诊断 OA 的生物标志物。此外，还通过收集 OA 的临床样本进行了验证。CIBERSORT网站估算了RNA转录本的相对子集，以评估OA的免疫炎症状态。通过相关性分析发现了特定 DEGs 与临床免疫炎症标志物之间的联系：结果：总共发现了 67 个稳健的 DEGs。核受体 2 亚家族 C 组 2（NR2C2）、转录因子 4（TCF4）、基质抗原 1（STAG1）和白细胞介素 18 受体附属蛋白（IL18RAP）被确定为外周血中 OA 的有效诊断标志物。这四种诊断标记物在 OA 中的表达量都有显著增加。对免疫细胞浸润的分析表明，巨噬细胞参与了 OA 的发生。候选诊断标志物与 OA 患者的临床免疫炎症指标相关：我们强调，与免疫炎症相关的 DEGs（NR2C2、TCF4、STAG1 和 IL18RAP）可能是 OA 外周血的潜在生物标记物，它们也与临床免疫炎症指标相关。

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来源期刊

Current computer-aided drug design 医学-计算机：跨学科应用

CiteScore

3.70

自引率

5.90%

发文量

审稿时长

>12 weeks

期刊介绍： Aims & Scope Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.