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

He-Jun Hu, Chao Kuang, Ru-Lin Deng, Zhi-Jun Zheng, Kang-Yan Liu, Xing-Xing Wei
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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.

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通过 WGCNA 鉴定骨关节炎的易感性模块和特征基因。
通过加权基因共表达网络分析(WGCNA)确定了骨关节炎(OA)患者的易感基因模块和特征基因,并分析了免疫细胞在OA相关微环境中的作用。GSE98918 和 GSE117999 数据集来自 GEO 数据库。合并后的新数据集使用 R 语言进行差异分析。基因共表达网络的形成、易感性模块的筛选和核心基因的筛选均通过 WGCNA 进行。对枢纽基因采用了 GO 和 KEGG 富集分析。通过 Lasso 回归筛选获得疾病的特征基因。使用 SSGSEA 估算样本中免疫细胞的丰度,并进行一系列相关性分析。利用 WGCNA 形成了 6 个基因共表达模块。黄绿色模块被确定为 OA 的易感模块。202 个基因被确定为核心基因。最后,RHOT2、FNBP4 和 NARF 被确定为 OA 的特征基因。结果显示,OA的特征基因与浆细胞树突状细胞、NKT细胞和未成熟树突状细胞呈正相关,但与活性B细胞呈负相关。MDSC是软骨中数量最多的免疫细胞。这项研究发现,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)有关。这三个基因在 OA 患者的软骨中下调,可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治疗 OA。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。
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