The First Affiliated Hospital of Harbin Medical University, Harbin, China.
BMC Musculoskelet Disord. 2024 Nov 25;25(1):954. doi: 10.1186/s12891-024-08015-7.
Osteoarthritis is recognized as a common geriatric condition characterized by irregular chronic pain. Its prevalence is steadily increasing, posing significant challenges to global public health, while some studies indicate a trend towards younger individuals being affected. This condition severely impacts patients' quality of life.
Using the Gene Expression Omnibus (GEO) database, we downloaded datasets GSE114007, GSE169077, and GSE206848. We utilized R software to screen and confirm differentially expressed genes (DEGs) related to the development of osteoarthritis. A cross-analysis of the three datasets was conducted, with the least overlapping dataset, GSE206848, selected as the validation set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the DEGs from GSE114007 and GSE169077. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify modules closely associated with osteoarthritis, and genes from these intersecting modules were entered into the STRING database to construct Protein-Protein Interaction Networks. The top ten genes by connectivity were identified and validated using GSE206848. Key genes were identified and preliminarily validated using Quantitative Real-Time PCR (QPCR). Subsequent validation of related genes was carried out through Western Blot (WB) analysis.
Differentially expressed genes were identified from the GSE114007 and GSE169077 datasets and validated in the GSE206848 dataset, with ANGPTL4 selected as the key gene. QPCR results indicated a significant difference in ANGPTL4 expression levels between normal and osteoarthritic chondrocytes. Western Blot analysis confirmed a significant difference in ANGPTL4 protein expression between normal and osteoarthritic chondrocytes.
Based on the experimental findings, ANGPTL4 appears to be a potential key gene in osteoarthritis.
骨关节炎是一种常见的老年疾病，其特征为不规则的慢性疼痛。其患病率呈稳步上升趋势，对全球公共健康构成重大挑战，同时一些研究表明发病趋势呈年轻化。这种疾病严重影响患者的生活质量。
我们使用基因表达综合数据库（GEO）下载了数据集 GSE114007、GSE169077 和 GSE206848。我们使用 R 软件筛选并确认与骨关节炎发展相关的差异表达基因（DEGs）。对这三个数据集进行交叉分析，选择重叠最少的数据集 GSE206848 作为验证集。对 GSE114007 和 GSE169077 中的 DEGs 进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析。采用加权基因共表达网络分析（WGCNA）识别与骨关节炎密切相关的模块，并将这些相交模块中的基因输入 STRING 数据库构建蛋白质-蛋白质相互作用网络。根据连接性识别并验证前十个基因，使用 GSE206848 进行验证。使用定量实时 PCR（QPCR）识别并初步验证关键基因。通过 Western Blot（WB）分析对相关基因进行后续验证。
从 GSE114007 和 GSE169077 数据集中鉴定出差异表达基因，并在 GSE206848 数据集中进行验证，选择 ANGPTL4 作为关键基因。QPCR 结果表明，正常和骨关节炎软骨细胞中 ANGPTL4 的表达水平存在显著差异。Western Blot 分析证实，正常和骨关节炎软骨细胞中 ANGPTL4 蛋白表达存在显著差异。
基于实验结果，ANGPTL4 似乎是骨关节炎的一个潜在关键基因。
