Pan Bailin, Yao Peixiu, Ma Jinjin, Lin Xuanhao, Zhou Laixi, Lin Canzhen, Zhang Yufeng, Lin Bendan, Lin Chuangxin
Department of Orthopedics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Department of Orthopedic Surgery, Shantou Central Hospital, Shantou, Guangdong, China.
Front Immunol. 2024 Nov 21;15:1482361. doi: 10.3389/fimmu.2024.1482361. eCollection 2024.
Osteoarthritis (OA) is a prevalent joint disease that severely impacts patients' quality of life. Due to its unclear pathogenesis and lack of effective therapeutic targets, discovering new biomarkers for OA is essential. Recently, the role of chondrocyte subpopulations in OA progression has gained significant attention, offering potential insights into the disease. This study aimed to explore the role of fibrocartilage chondrocytes (FC) in the progression of OA and identify key biomarkers related to FC.
We analyzed single-cell ribonucleic acid sequencing (scRNA-seq) data from samples of OA and normal cartilage, focusing on FC. Microarray data were integrated to identify differentially expressed genes (DEGs). We conducted functional-enrichment analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), and used weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm to select biomarkers. A novel risk model for OA was constructed using these biomarkers. We then built a transcription factor (TF)-gene interaction network and performed immunohistochemistry (IHC) to validate protein expression levels of these biomarkers in cartilage samples.
The study identified 545 marker genes associated with FC in OA. GO and KEGG analyses revealed their biological functions; microarray analysis identified 243 DEGs on which functional-enrichment analysis were conducted. Using WGCNA and LASSO, we identified six hub genes, on the basis of which we constructed a risk model for OA. In addition, correlation analysis revealed a close association between Forkhead Box (FoxO)-mediated transcription and these these biomarkers. IHC showed significantly lower protein levels of ABCA5, ABCA6 and SLC7A8 in OA samples than in normal samples.
This study used a multi-omics approach to identify six FC-related OA biomarkers (BCL6, ABCA5, ABCA6, CITED2, NR1D1, and SLC7A8) and developed an exploratory risk model. Functional enrichment analysis revealed that the FoxO pathway may be linked to these markers, particularly implicating ABCA5 and ABCA6 in cholesterol homeostasis within chondrocytes. These findings highlight ABCA family members as novel contributors to OA pathogenesis and suggest new therapeutic targets.
骨关节炎(OA)是一种常见的关节疾病,严重影响患者的生活质量。由于其发病机制尚不清楚且缺乏有效的治疗靶点,发现OA的新生物标志物至关重要。最近,软骨细胞亚群在OA进展中的作用受到了广泛关注,为该疾病提供了潜在的见解。本研究旨在探讨纤维软骨细胞(FC)在OA进展中的作用,并确定与FC相关的关键生物标志物。
我们分析了OA和正常软骨样本的单细胞核糖核酸测序(scRNA-seq)数据,重点关注FC。整合微阵列数据以识别差异表达基因(DEG)。我们进行了功能富集分析,包括京都基因与基因组百科全书(KEGG)和基因本体论(GO),并使用加权基因共表达网络分析(WGCNA)和最小绝对收缩和选择算子(LASSO)算法来选择生物标志物。使用这些生物标志物构建了一种新的OA风险模型。然后,我们构建了一个转录因子(TF)-基因相互作用网络,并进行免疫组织化学(IHC)以验证这些生物标志物在软骨样本中的蛋白表达水平。
该研究确定了545个与OA中FC相关的标记基因。GO和KEGG分析揭示了它们的生物学功能;微阵列分析确定了243个DEG,并对其进行了功能富集分析。使用WGCNA和LASSO,我们确定了六个枢纽基因,并在此基础上构建了OA风险模型。此外,相关性分析揭示了叉头框(FoxO)介导的转录与这些生物标志物之间的密切关联。IHC显示,OA样本中ABCA5、ABCA6和SLC7A8的蛋白水平明显低于正常样本。
本研究采用多组学方法确定了六个与FC相关的OA生物标志物(BCL6、ABCA5、ABCA6、CITED2、NR1D1和SLC7A8),并开发了一种探索性风险模型。功能富集分析表明,FoxO途径可能与这些标志物有关,特别是ABCA5和ABCA6参与软骨细胞内的胆固醇稳态。这些发现突出了ABCA家族成员是OA发病机制的新贡献者,并提出了新的治疗靶点。