DataSheet_1_Mendelian randomization and transcriptome analysis identified immune-related biomarkers for osteoarthritis.pdf

BackgroundThe 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.
MethodsThe 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.
ResultsThree 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.
ConclusionThe 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.

研究背景：免疫微环境在骨关节炎（Osteoarthritis, OA）的发病机制中发挥重要作用。然而当前用于骨关节炎诊断与治疗的生物标志物仍不尽如人意。本研究旨在借助多组学数据，筛选新型骨关节炎免疫相关生物标志物，以指导骨关节炎的防治工作。
研究方法：本研究的发现数据集整合了GSE89408与GSE143514数据集，通过多种机器学习方法及加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA），筛选出与骨关节炎免疫微环境显著相关的生物标志物。随后采用两个独立的验证数据集对筛选得到的特征基因进行验证。本研究同时开展了双样本孟德尔随机化（Mendelian Randomization, MR）分析，借助骨关节炎全基因组关联研究（Genome-wide Association Study, GWAS）汇总数据（病例组n=24955，对照组n=378169），明确生物标志物与骨关节炎之间的因果关联。本研究以逆方差加权（Inverse-variance Weighting, IVW）法作为因果效应估计的主要方法，并通过敏感性分析评估逆方差加权结果的稳健性与可靠性。
研究结果：本研究共筛选出3个与骨关节炎免疫微环境相关的特征基因（FCER1G、HLA-DMB及HLA-DPA1），上述基因均具有良好的诊断效能，可作为骨关节炎的生物标志物。孟德尔随机化分析结果显示，FCER1G（比值比[OR]=1.118，95%置信区间[CI]=1.031~1.212，P=0.041）、HLA-DMB（OR=1.057，95%CI=1.045~1.069，P=1.11×10^-21）及HLA-DPA1（OR=1.030，95%CI=1.005~1.056，P=0.017）的表达水平升高与骨关节炎的发病风险呈因果性正相关。
研究结论：本研究筛选出3个骨关节炎潜在免疫相关生物标志物，为骨关节炎的防治提供了新的研究视角。本孟德尔随机化研究为3个生物标志物与骨关节炎的因果关联提供了遗传学支持，同时可为阐明骨关节炎发生发展的分子机制提供新的思路。