Yu Enming, Zhang Mingshu, Xi Chunyang, Yan Jinglong
Department of Orthopedics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Heliyon. 2024 Aug 28;10(17):e37047. doi: 10.1016/j.heliyon.2024.e37047. eCollection 2024 Sep 15.
Osteoarthritis (OA) is a prevalent cause of disability in older adults. Identifying diagnostic markers for OA is essential for elucidating its mechanisms and facilitating early diagnosis.
We analyzed 53 synovial tissue samples (n = 30 for OA, n = 23 for the control group) from two datasets in the Gene Express Omnibus (GEO) database. We identified differentially expressed genes (DEGs) between the groups and applied dimensionality reduction using six machine learning algorithms to pinpoint characteristic genes (key genes). We classified the OA samples into subtypes based on these key genes and explored the differences in biological functions and immune characteristics among subtypes, as well as the roles of the key genes. Additionally, we constructed a protein-protein interaction network to predict small molecules that target these genes. Further, we accessed synovial tissue sample data from the single-cell RNA dataset GSE152805, categorized the cells into various types, and examined variations in gene expression and their correlation with OA progression. Validation of key gene expression was conducted in cellular experiments using the qPCR method.
Four genes , and , were identified as characteristic genes of OA. All can independently predict the occurrence of OA. With these genes, the OA samples can be clustered into two subtypes, which showed significant differences in functional pathways and immune infiltration. Eight cell types were obtained by analyzing the single-cell RNA data, with synovial intimal fibroblasts (SIF) accounting for the highest proportion in each sample. The key genes were found over-expressed in SIF and significantly correlated with OA progression and the content of immune cells (ICs). We validated the relative levels of key genes in OA and normal cartilage tissue cells, which showed an expression trend consistency with the bioinformatics result except for .
Four genes, , and are closely related to the progression of OA, and play as diagnostic and predictive markers in early OA.
骨关节炎（OA）是老年人残疾的常见原因。识别OA的诊断标志物对于阐明其发病机制和促进早期诊断至关重要。
我们分析了来自基因表达综合数据库（GEO）中两个数据集的53个滑膜组织样本（OA组30个，对照组23个）。我们确定了两组之间的差异表达基因（DEG），并使用六种机器学习算法进行降维以确定特征基因（关键基因）。我们根据这些关键基因将OA样本分为不同亚型，探讨各亚型之间生物学功能和免疫特征的差异以及关键基因的作用。此外，我们构建了蛋白质-蛋白质相互作用网络以预测靶向这些基因的小分子。进一步地，我们获取了单细胞RNA数据集GSE152805的滑膜组织样本数据，将细胞分类为不同类型，并检查基因表达的变化及其与OA进展的相关性。使用qPCR方法在细胞实验中对关键基因表达进行验证。
四个基因， 和 ，被确定为OA的特征基因。所有这些基因均可独立预测OA的发生。利用这些基因，OA样本可聚类为两个亚型，它们在功能途径和免疫浸润方面存在显著差异。通过分析单细胞RNA数据获得了八种细胞类型，滑膜内膜成纤维细胞（SIF）在每个样本中占比最高。发现关键基因在SIF中过表达，且与OA进展和免疫细胞（IC）含量显著相关。我们验证了OA和正常软骨组织细胞中关键基因的相对水平，除 外，其表达趋势与生物信息学结果一致。
四个基因， 和 与OA进展密切相关，在早期OA中可作为诊断和预测标志物。
