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一种用于识别肝细胞癌和正常肝组织转录组和网络差异的计算框架及其在药物重定位中的应用。

A Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs.

机构信息

Xiantao First People's Hospital Affiliated to Yangtze University, Xiantao 433000, China.

出版信息

Biomed Res Int. 2021 Sep 23;2021:9921195. doi: 10.1155/2021/9921195. eCollection 2021.

DOI:10.1155/2021/9921195
PMID:34604388
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8483911/
Abstract

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide. Although there have been extensive studies on the molecular mechanisms of its carcinogenesis, FDA-approved drugs for HCC are rare. Side effects, development time, and cost of these drugs are the major bottlenecks, which can be partially overcome by drug repositioning. In this study, we developed a computational framework to study the mechanisms of HCC carcinogenesis, in which drug perturbation-induced gene expression signatures were utilized for repositioning of potential drugs. Specifically, we first performed differential expression analysis and coexpression network module analysis on the HCC dataset from The Cancer Genome Atlas database. Differential gene expression analysis identified 1,337 differentially expressed genes between HCC and adjacent normal tissues, which were significantly enriched in functions related to various pathways, including -adrenergic receptor activity pathway and epinephrine binding pathway. Weighted gene correlation network analysis (WGCNA) suggested that the number of coexpression modules was higher in HCC tissues than in normal tissues. Finally, by correlating differentially expressed genes with drug perturbation-related signatures, we prioritized a few potential drugs, including nutlin and eribulin, for the treatment of hepatocellular carcinoma. The drugs have been reported by a few experimental studies to be effective in killing cancer cells.

摘要

肝细胞癌(HCC)是全球最常见和最致命的恶性肿瘤之一。尽管已经对其致癌机制进行了广泛的研究,但获得 FDA 批准的 HCC 药物却很少。这些药物的副作用、开发时间和成本是主要的瓶颈,而药物重定位可以部分克服这些问题。在本研究中,我们开发了一种计算框架来研究 HCC 致癌机制,其中利用药物扰动诱导的基因表达特征来重新定位潜在药物。具体来说,我们首先对来自癌症基因组图谱数据库的 HCC 数据集进行了差异表达分析和共表达网络模块分析。差异基因表达分析鉴定出 HCC 与相邻正常组织之间的 1337 个差异表达基因,这些基因显著富集在与各种途径相关的功能中,包括β肾上腺素能受体活性途径和肾上腺素结合途径。加权基因相关网络分析(WGCNA)表明,HCC 组织中的共表达模块数量高于正常组织。最后,通过将差异表达基因与药物扰动相关特征相关联,我们确定了几种潜在药物,包括 nutlin 和 eribulin,可用于治疗肝细胞癌。这些药物已经有一些实验研究报道称它们在杀死癌细胞方面有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b0f/8483911/0622e07ad012/BMRI2021-9921195.006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b0f/8483911/0622e07ad012/BMRI2021-9921195.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b0f/8483911/f142bed4b3ac/BMRI2021-9921195.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b0f/8483911/66b88b480158/BMRI2021-9921195.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b0f/8483911/63189c5d1b3b/BMRI2021-9921195.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b0f/8483911/c0744fdf607b/BMRI2021-9921195.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b0f/8483911/6fcff248f534/BMRI2021-9921195.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b0f/8483911/0622e07ad012/BMRI2021-9921195.006.jpg

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