Yang Liyu, Liu Jiamei, Yu Yuanqi, Liu Shengye
Department of Orthopedics, The Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, PR China.
Department of Pathology, The Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, PR China.
Life Sci. 2023 May 15;321:121599. doi: 10.1016/j.lfs.2023.121599. Epub 2023 Mar 24.
We aimed to provide an autophagy-related signature to seek immunophenotyping biomarkers in osteoarthritis (OA).
Microarray expression profiling of OA subchondral bone samples and screening of an autophagy database for autophagy-related differentially expressed genes (au-DEGs) between OA and normal samples were performed. A weighted gene co-expression network analysis (WGCNA) was constructed using au-DEGs to identify key modules significantly associated with clinical information of OA samples. OA-related autophagy hub genes were identified based on the connectivity with the phenotypes of genes in key modules and the protein-protein interaction (PPI) network in which the genes in the modules are involved, followed by feasibility verification of autophagy hub genes by bioinformatics analysis and biological experiments.
We screened 754 au-DEGs between OA and control samples, and co-expression networks were constructed using au-DEGs. Three OA-related autophagy hub genes (HSPA5, HSP90AA1, and ITPKB) were identified. Based on the hub gene expression profiles, OA samples were divided into two clusters with significantly different expression profiles and distinct immunological features, and the three hub genes were significantly differentially expressed between the clusters. Differences in hub genes between OA and control samples regarding sex, age, and grades of OA were examined using external datasets and experimental validation.
Three autophagy-related markers of OA were identified using bioinformatics methods, and these markers may be useful for the autophagy-related immunophenotyping of OA. The present data may facilitate the diagnosis of OA, as well as the design of immunotherapies and individualized medical treatments.
我们旨在提供一种自噬相关特征,以寻找骨关节炎(OA)中的免疫表型生物标志物。
对OA软骨下骨样本进行微阵列表达谱分析,并筛选自噬数据库以寻找OA与正常样本之间的自噬相关差异表达基因(au-DEGs)。使用au-DEGs构建加权基因共表达网络分析(WGCNA),以识别与OA样本临床信息显著相关的关键模块。基于关键模块中基因的表型连接性以及模块中基因所涉及的蛋白质-蛋白质相互作用(PPI)网络,鉴定OA相关的自噬枢纽基因,随后通过生物信息学分析和生物学实验对自噬枢纽基因进行可行性验证。
我们筛选出OA与对照样本之间的754个au-DEGs,并使用au-DEGs构建共表达网络。鉴定出三个OA相关的自噬枢纽基因(HSPA5、HSP90AA1和ITPKB)。基于枢纽基因表达谱,OA样本被分为两个具有显著不同表达谱和不同免疫特征的簇,并且这三个枢纽基因在簇之间存在显著差异表达。使用外部数据集和实验验证检查了OA与对照样本在性别、年龄和OA分级方面枢纽基因的差异。
使用生物信息学方法鉴定出三个OA的自噬相关标志物,这些标志物可能有助于OA的自噬相关免疫表型分析。本数据可能有助于OA的诊断以及免疫疗法和个性化医疗的设计。