DataSheet_1_Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning.docx

BackgroundOsteoarthritis (OA) is a degenerative disease closely related to aging. Nevertheless, the role and mechanisms of aging in osteoarthritis remain unclear. This study aims to identify potential aging-related biomarkers in OA and to explore the role and mechanisms of aging-related genes and the immune microenvironment in OA synovial tissue.
MethodsNormal and OA synovial gene expression profile microarrays were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) from the Human Aging Genomic Resources database (HAGR). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), and Gene set variation analysis (GSVA) enrichment analysis were used to uncover the underlying mechanisms. To identify Hub ARDEGs with highly correlated OA features (Hub OA-ARDEGs), Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning methods were used. Furthermore, we created diagnostic nomograms and receiver operating characteristic curves (ROC) to assess Hub OA-ARDEGs’ ability to diagnose OA and predict which miRNAs and TFs they might act on. The Single sample gene set enrichment analysis (ssGSEA) algorithm was applied to look at the immune infiltration characteristics of OA and their relationship with Hub OA-ARDEGs.
ResultsWe discovered 87 ARDEGs in normal and OA synovium samples. According to functional enrichment, ARDEGs are primarily associated with inflammatory regulation, cellular stress response, cell cycle regulation, and transcriptional regulation. Hub OA-ARDEGs with excellent OA diagnostic ability were identified as MCL1, SIK1, JUND, NFKBIA, and JUN. Wilcox test showed that Hub OA-ARDEGs were all significantly downregulated in OA and were validated in the validation set and by qRT-PCR. Using the ssGSEA algorithm, we discovered that 15 types of immune cell infiltration and six types of immune cell activation were significantly increased in OA synovial samples and well correlated with Hub OA-ARDEGs.
ConclusionSynovial aging may promote the progression of OA by inducing immune inflammation. MCL1, SIK1, JUND, NFKBIA, and JUN can be used as novel diagnostic biomolecular markers and potential therapeutic targets for OA.

骨关节炎（Osteoarthritis, OA）是一种与衰老密切相关的退行性疾病。然而，衰老在骨关节炎中的作用及机制仍不明确。本研究旨在识别骨关节炎中潜在的衰老相关生物标志物，并探讨衰老相关基因与免疫微环境在骨关节炎滑膜组织中的作用及机制。
本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）获取正常与骨关节炎滑膜的基因表达谱芯片数据，从人类衰老基因组资源数据库（Human Aging Genomic Resources, HAGR）下载衰老相关基因（aging-related genes, ARGs）。采用基因本体论（Gene Ontology, GO）、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）、疾病本体论（Disease Ontology, DO）及基因集变异分析（Gene set variation analysis, GSVA）进行富集分析，以揭示潜在的发病机制。为筛选与骨关节炎特征高度相关的核心衰老差异表达基因（Hub OA-ARDEGs），本研究采用加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）与机器学习方法。此外，我们构建了诊断列线图与受试者工作特征曲线（receiver operating characteristic curves, ROC），以评估核心OA-ARDEGs的骨关节炎诊断能力，并预测其可能调控的微小RNA（microRNAs, miRNAs）与转录因子（transcription factors, TFs）。采用单样本基因集富集分析（Single sample gene set enrichment analysis, ssGSEA）算法，分析骨关节炎的免疫浸润特征及其与核心OA-ARDEGs的相关性。
本研究在正常与骨关节炎滑膜样本中筛选得到87个衰老相关差异表达基因（ARDEGs）。功能富集分析结果显示，ARDEGs主要与炎症调控、细胞应激反应、细胞周期调控及转录调控相关。最终筛选得到具有优异骨关节炎诊断能力的核心OA-ARDEGs，分别为MCL1、SIK1、JUND、NFKBIA及JUN。Wilcoxon秩和检验结果显示，核心OA-ARDEGs在骨关节炎样本中均显著下调，并在验证集与实时定量聚合酶链反应（quantitative real-time polymerase chain reaction, qRT-PCR）实验中得到验证。通过ssGSEA算法分析发现，骨关节炎滑膜样本中15种免疫细胞浸润与6种免疫细胞激活状态显著升高，且与核心OA-ARDEGs具有良好的相关性。
滑膜衰老可能通过诱导免疫炎症反应促进骨关节炎的进展。MCL1、SIK1、JUND、NFKBIA及JUN可作为骨关节炎新型诊断生物分子标志物与潜在治疗靶点。