Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China.
Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, United States.
Front Immunol. 2024 Apr 12;15:1334479. doi: 10.3389/fimmu.2024.1334479. eCollection 2024.
The immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data.
The discovery dataset integrated the GSE89408 and GSE143514 datasets to identify biomarkers that were significantly associated with the OA immune microenvironment through multiple machine learning methods and weighted gene co-expression network analysis (WGCNA). The identified signature genes were confirmed using two independent validation datasets. We also performed a two-sample mendelian randomization (MR) study to generate causal relationships between biomarkers and OA using OA genome-wide association study (GWAS) summary data (cases n = 24,955, controls n = 378,169). Inverse-variance weighting (IVW) method was used as the main method of causal estimates. Sensitivity analyses were performed to assess the robustness and reliability of the IVW results.
Three signature genes (FCER1G, HLA-DMB, and HHLA-DPA1) associated with the OA immune microenvironment were identified as having good diagnostic performances, which can be used as biomarkers. MR results showed increased levels of FCER1G (OR = 1.118, 95% CI 1.031-1.212, P = 0.041), HLA-DMB (OR = 1.057, 95% CI 1.045 -1.069, P = 1.11E-21) and HLA-DPA1 (OR = 1.030, 95% CI 1.005-1.056, P = 0.017) were causally and positively associated with the risk of developing OA.
The present study identified the 3 potential immune-related biomarkers for OA, providing new perspectives for the prevention and treatment of OA. The MR study provides genetic support for the causal effects of the 3 biomarkers with OA and may provide new insights into the molecular mechanisms leading to the development of OA.
免疫微环境在骨关节炎（OA）的发病机制中起着重要作用。然而，目前用于 OA 诊断和治疗的生物标志物并不令人满意。我们的研究旨在使用多组学数据识别新的 OA 免疫相关生物标志物，以指导 OA 的预防和治疗。
发现数据集整合了 GSE89408 和 GSE143514 数据集，通过多种机器学习方法和加权基因共表达网络分析（WGCNA），确定与 OA 免疫微环境显著相关的生物标志物。使用两个独立的验证数据集来验证鉴定的特征基因。我们还使用 OA 全基因组关联研究（GWAS）汇总数据（病例 n=24955，对照 n=378169）进行了两样本孟德尔随机化（MR）研究，以生成生物标志物与 OA 之间的因果关系。使用逆方差加权（IVW）方法作为因果估计的主要方法。进行敏感性分析以评估 IVW 结果的稳健性和可靠性。
确定了三个与 OA 免疫微环境相关的特征基因（FCER1G、HLA-DMB 和 HHLA-DPA1），这些基因具有良好的诊断性能，可作为生物标志物。MR 结果表明，FCER1G（OR=1.118，95%CI 1.031-1.212，P=0.041）、HLA-DMB（OR=1.057，95%CI 1.045-1.069，P=1.11E-21）和 HLA-DPA1（OR=1.030，95%CI 1.005-1.056，P=0.017）的水平升高与 OA 发病风险呈因果正相关。
本研究确定了 3 个潜在的 OA 免疫相关生物标志物，为 OA 的预防和治疗提供了新的视角。MR 研究为这 3 个与 OA 相关的生物标志物的因果关系提供了遗传支持，并可能为 OA 发展的分子机制提供新的见解。
