基于基因共表达权重网络分析骨质疏松相关的关键LncRNA
Analysis of key lncRNA related to osteoporosis based on gene co-expression weight network
  
DOI:10.3969/j.issn.1006-7108.2021.04.015
中文关键词:  基因共表达  WGCNA  LncRNA  骨质疏松  靶基因
英文关键词:gene co-expression weights  WGCNA  lncRNA  osteoporosis  target genes
基金项目:广东省自然科学基金项目 (2018A030313369)
作者单位
黎永华* 谭仁霆 黄世福 邱金龙 海南省儋州市中医医院骨伤科海南 儋州571700 
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中文摘要:
      目的 通过基因共表达权重网络分析 (WGCNA) 探讨LncRNA与骨质疏松的关系。方法 收集GEO数据库中GSE56815芯片的80个样本,通过重注释获得了127 LncRNA,用WGCNA对表达数据进行样本聚类分析,并与骨质疏松进行相关性分析。寻找相关性最高的模块的靶基因并绘制网络关系图,随后进行GO和KEGG富集分析。结果 共检测到9个LncRNA模块,关键模块为turquoise模块包含29个靶基因,并获得204个靶向基因,绘制网络关系图后获得枢纽LncRNA为ACVR2B-AS1、LINC01278、SND1-IT1、C22orf24、ZNF213-AS1、WT1-AS、PLAC4、RHPN1-AS1和LINC01140。GO功能主要集中在细胞间信息传递上;细胞功能集中在细胞核、细胞膜和线粒体中,涉及成骨细胞分化的调控。KEGG主要富集于GnRH、Notch、神经营养和钙离子信号通路上。结论 利用WGCNA方法,获得与骨质疏松性状相关模块,鉴定有生物学意义的基因模块,获得了核心LncRNA,并通过寻找潜在的靶基因,进行了富集分析。这些结果为LncRNA在骨质疏松症病理生理机制的研究中提供新的见解。
英文摘要:
      Objective To explore the relationship between LncRNA and osteoporosis through gene co-expression weight network analysis (WGCNA). Methods Collected 80 samples of GSE56815 chip in GEO database, obtained 127 LncRNA by re-annotation, used WGCNA to analyze the clustering analysis of the expression data, and analyzed the correlation with osteoporosis. Find the target genes of the most relevant modules and draw a network relationship diagram, and then perform GO and KEGG enrichment analysis. Results A total of 9 LncRNA modules were detected. The key module was the turquoise module containing 29 target genes, and 204 target genes were obtained. After drawing the network diagram, the hub LncRNA was ACVR2B-AS1, LINC01278, SND1-IT1, C22orf24, ZNF213 -AS1, WT1-AS, PLAC4, RHPN1-AS1 and LINC01140. The GO function was mainly focused on the transmission of information between cells; the cell function was concentrated in the nucleus, cell membrane and mitochondria, and was involved in the regulation of osteoblast differentiation. KEGG was mainly enriched in GnRH, Notch, neurotrophic and calcium ion signaling pathways. Conclusion The WGCNA method was used to obtain modules related to osteoporosis traits, to identify biologically meaningful gene modules, to obtain core LncRNA, and to perform enrichment analysis by searching for potential target genes. These results provide new insights into the study of LncRNA in the pathophysiology of osteoporosis.
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