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通过微阵列分析探索与骨质疏松症相关的转录组机制。

Investigation of transcriptome mechanism associated with osteoporosis explored by microarray analysis.

作者信息

Liu Yan, Li Ying, Liu Xing, Wang Chun-Sheng

机构信息

Department of Spine Surgery, First Hospital, Jilin University, Changchun, Jilin 130021, P.R. China.

Beijing Splinger Medical Research Institute, Beijing 100054, P.R. China.

出版信息

Exp Ther Med. 2019 May;17(5):3459-3464. doi: 10.3892/etm.2019.7349. Epub 2019 Mar 6.

Abstract

Microarray data of osteoporosis (OP) were analyzed based on prediction of transcription factors (TFs) or their targets as well as influences of TFs or TF network to uncover key TFs in OP. The microarray data E-GEOD-35956 was downloaded from the GPL570 platform. Differentially expressed genes (DEGs) with logarithm of fold change (|logFC|) >2 and P-value <0.05 were identified between OP samples and normal controls. TF genes were screened from the DEGs based on ITFP, Marbach 2016, TRRUST databases. TF targets were enriched from DEGs using Fisher's exact test. TF targets were selected based on their impact factors. TF targets were chosen from TF network analysis. Finally, key TFs were identified by based on TFs coverage. A total of 300 DEGs were obtained. There were no TF genes screened from the DEGs. In total 165, 87 and 178 TF targets were screened from DEGs respectively based on Fisher's exact test, influence of TFs or TF network analysis. According to the optimal TF set with TFs having maximum coverage of DEGs, 178 TF targets was the most. Thus, the optimal sets of TFs were , and . Altogether, these results suggested identified crucial TFs in OP might play a significant role in OP development, showing these key TFs probably would aid in unveiling the underlying molecular mechanisms and may be therapeutic targets, diagnostic or prognostic biomarkers for OP.

摘要

基于转录因子(TFs)或其靶标的预测以及TFs或TF网络的影响,对骨质疏松症(OP)的微阵列数据进行分析,以揭示OP中的关键TFs。微阵列数据E-GEOD-35956从GPL570平台下载。在OP样本和正常对照之间鉴定出对数变化倍数(|logFC|)>2且P值<0.05的差异表达基因(DEGs)。基于ITFP、Marbach 2016、TRRUST数据库从DEGs中筛选TF基因。使用Fisher精确检验从DEGs中富集TF靶标。根据其影响因子选择TF靶标。从TF网络分析中选择TF靶标。最后,基于TF覆盖度鉴定关键TFs。共获得300个DEGs。未从DEGs中筛选出TF基因。基于Fisher精确检验、TFs的影响或TF网络分析,分别从DEGs中筛选出总共165、87和178个TF靶标。根据具有最大DEGs覆盖度的TF的最佳TF集,178个TF靶标最多。因此,最佳TF集为 , 和 。总之,这些结果表明,在OP中鉴定出的关键TFs可能在OP发展中起重要作用,表明这些关键TFs可能有助于揭示潜在的分子机制,并且可能是OP的治疗靶点、诊断或预后生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8d/6468394/24076f7c9028/etm-17-05-3459-g01.jpg

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