骨关节炎核心基因的生物信息学鉴定

Identification of core genes of osteoarthritis by bioinformatics

背景:目前骨关节炎成为影响老年人生活质量的主要疾病,治疗效果欠佳,临床治疗措施主要集中在阻止疾病的进程,同时骨关节炎的发病机制尚不完全清楚。为了探索骨关节炎的主要发病机制和基因编码调控的相关机制,进行生物信息学分析。目的:通过基因表达谱筛选出在骨关节炎中起主要作用的核心差异基因。方法:从基因表达综合数据库(GEO)下载数据集:GSE114007、GSE117999和GSE129147,利用R软件筛选出GSE114007和GSE117999数据合集的差异基因,将差异基因进行加权基因共表达网络分析,选择和骨关节炎最相关的模块基因并进行蛋白互作分析,利用cytocape软件筛选出候选核心基因,随后将候选核心基因进行最小绝对收缩和选择算子回归(LASSO回归)和COX分析,鉴定出在骨关节炎中起关键作用的核心基因,利用外部数据集GSE129147验证核心基因的准确性。结果与结论:①共鉴定出477个差异基因,加权基因共表达网络分析获得265个和骨关节炎相关的差异基因,共鉴定8个候选核心基因,LASSO回归分析最终获得了一个具有核心价值的差异基因ASPM并进行了外部验证;②提示通过生物信息学筛选出基因ASPM表达异常在骨关节炎中起到关键核心作用。

BACKGROUND:At present,osteoarthritis has become a major disease affecting the quality of life of the elderly,and the therapeutic effect is poor,often focusing on preventing the disease process,and the pathogenesis of osteoarthritis is still not fully understood.Bioinformatics analysis was carried out to explore the main pathogenesis of osteoarthritis and related mechanisms of gene coding regulation.OBJECTIVE:To screen core differential genes with a major role in osteoarthritis by gene expression profiling.METHODS:Datasets were downloaded from the Gene Expression Omnibus(GEO):GSE114007,GSE117999,and GSE129147.Differential genes in the GSE114007 and GSE117999 data collections were screened using R software,performing differential genes to weighted gene co-expression network analysis.The module genes most relevant to osteoarthritis were selected to perform protein interaction analysis.Candidate core genes were selected using the cytocape software.The candidate core genes were subsequently subjected to least absolute shrinkage and selection operator regression and COX analysis to identify the core genes with a key role in osteoarthritis.The accuracy of the core genes was validated using an external dataset,GSE129147.RESULTS AND CONCLUSION:(1)A total of 477 differential genes were identified,265 differential genes associated with osteoarthritis were obtained by weighted gene co-expression network analysis,and 8 candidate core genes were identified.The least absolute shrinkage and selection operator regression analysis finally yielded a differential gene ASPM with core value that was externally validated.(2)It is concluded that abnormal gene ASPM expression screened by bioinformatics plays a key central role in osteoarthritis.