基于 WGCNA 鉴定骨关节炎易感性模块和特征基因

Identification of susceptibility modules and characteristic genes to osteoarthritis by WGCNA.

机构信息

Nan Chang Hong Du Hospital Of TCM, Nan Chang city 330038, Jiang Xi province, China.

Jiangxi University of Chinese Medicine, Nanchang, Jiangxi 330004, China.

出版信息

Ann Biol Clin (Paris). 2024 Sep 19;82(4):423-437. doi: 10.1684/abc.2024.1913.

Abstract

The susceptibility modules and characteristic genes of patients with osteoarthritis (OA) were determined by weighted gene co-expression network analysis (WGCNA), and the role of immune cells in OA related microenvironment was analyzed. GSE98918 and GSE117999 data sets are from GEO database. R language was used to conduct difference analysis for the new data set after merging. The formation of gene co-expression network, screening of susceptibility modules and screening of core genes are all through WGCNA. GO and KEGG enrichment analyses were used for Hub genes. The characteristic genes of the disease were obtained by Lasso regression screening. SSGSEA was used to estimate immune cell abundance in sample and a series of correlation analyses were performed. WGCNA was used to form 6 gene co-expression modules. The yellow-green module is identified as the susceptible module of OA. 202 genes were identified as core genes. Finally, RHOT2, FNBP4 and NARF were identified as the characteristic genes of OA. The results showed that the characteristic genes of OA were positively correlated with plasmacytoid dendritic cells, NKT cells and immature dendritic cells, but negatively correlated with active B cells. MDSC were the most abundant immune cells in cartilage. This study identified the Hippo signaling pathway, mTOR signaling pathway, and three characteristic genes (RHOT2, FNBP4, NARF) as being associated with osteoarthritis (OA). These three genes are downregulated in the cartilage of OA patients and may serve as biomarkers for early diagnosis and targeted therapy. Proper regulation of immune cells may aid in the treatment of OA. Future research should focus on developing tools to detect these genes and exploring their therapeutic applications.

摘要

采用加权基因共表达网络分析(WGCNA)确定骨关节炎(OA)患者的易感性模块和特征基因,并分析免疫细胞在 OA 相关微环境中的作用。GEO 数据库中的 GSE98918 和 GSE117999 数据集。使用 R 语言合并新数据集后进行差异分析。通过 WGCNA 进行基因共表达网络的构建、易感性模块的筛选和核心基因的筛选。GO 和 KEGG 富集分析用于 Hub 基因。通过 Lasso 回归筛选获得疾病的特征基因。SSGSEA 用于估计样本中免疫细胞的丰度,并进行了一系列相关性分析。WGCNA 形成 6 个基因共表达模块。黄色-绿色模块被鉴定为 OA 的易感性模块。鉴定出 202 个核心基因。最后,确定 RHOT2、FNBP4 和 NARF 为 OA 的特征基因。结果表明,OA 的特征基因与浆细胞样树突状细胞、NKT 细胞和未成熟树突状细胞呈正相关,与活性 B 细胞呈负相关。MDSC 是软骨中最丰富的免疫细胞。本研究确定 Hippo 信号通路、mTOR 信号通路和三个特征基因(RHOT2、FNBP4、NARF)与骨关节炎(OA)相关。这些基因在 OA 患者的软骨中下调,可能作为早期诊断和靶向治疗的生物标志物。适当调节免疫细胞可能有助于 OA 的治疗。未来的研究应侧重于开发检测这些基因的工具,并探索其治疗应用。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索