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使用生物信息学软件对肝细胞癌进行基因-基因相互作用网络分析。

Gene-gene interaction network analysis of hepatocellular carcinoma using bioinformatic software.

作者信息

He Jin-Hua, Han Ze-Ping, Wu Pu-Zhao, Zou Mao-Xian, Wang Li, Lv Yu-Bing, Zhou Jia-Bin, Cao Ming-Rong, Li Yu-Guang

机构信息

Department of Laboratory, Central Hospital of Panyu District, Guangzhou, Guangdong 511400, P.R. China.

Department of General Surgery, First Affiliated Hospital, Jinan University, Guangzhou, Guangdong 510630, P.R. China.

出版信息

Oncol Lett. 2018 Jun;15(6):8371-8377. doi: 10.3892/ol.2018.8408. Epub 2018 Apr 2.

DOI:10.3892/ol.2018.8408
PMID:29805571
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5950030/
Abstract

Information processing tools and bioinformatics software have markedly advanced the ability of researchers to process and analyze biological data. Data from the genomes of humans and model organisms aid researchers to identify topics to study, which in turn improves predictive accuracy, facilitates the identification of relevant genes and simplifies the validation of laboratory data. The objective of the present study was to investigate the regulatory network constituted by long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNA in hepatocellular carcinoma (HCC). Microarray data from HCC datasets were downloaded from The Cancer Genome Atlas database, and the Limma package in R was used to identify the differentially expressed genes (DEGs) between HCC and normal samples. Gene ontology enrichment analysis of DEGs was conducted using the Database for Annotation, Visualization, and Integrated Discovery. TargetScan, microcosm, miRanda, miRDB and PicTar were used to predict target genes. lncRNAs associated with HCC were probed using the lncRNASNP database, and a lncRNA-miRNA-mRNA regulatory network was visualized using Cytoscape. The present study identified 114 differentially expressed miRNAs and 2,239 differentially expressed mRNAs; of these, 725 were downregulated genes that were primarily involved in complement and coagulation cascades, fatty acid metabolism and butanoate metabolism, among others. The remaining 1,514 were upregulated genes principally involved in DNA replication, oocyte meiosis and homologous recombination, among others. Through the integrated analysis of associations between different types of RNAs and target gene prediction, the present study identified 203 miRNA-mRNA pairs, including 28 miRNAs and 170 mRNAs, and identified 348 lncRNA-miRNA pairs, containing 28 miRNAs. Therefore, owing to the association between lncRNAs-miRNAs-mRNAs, the present study screened out 2,721 regulatory associations. The data in the present study provide a comprehensive bioinformatic analysis of genes, functions and pathways that may be involved in the pathogenesis of HCC.

摘要

信息处理工具和生物信息学软件显著提升了研究人员处理和分析生物数据的能力。来自人类和模式生物基因组的数据帮助研究人员确定研究主题，进而提高预测准确性、促进相关基因的识别并简化实验室数据的验证。本研究的目的是探究长链非编码RNA（lncRNA）、微小RNA（miRNA）和信使RNA（mRNA）在肝细胞癌（HCC）中构成的调控网络。从癌症基因组图谱数据库下载HCC数据集的微阵列数据，并使用R语言中的Limma软件包来识别HCC与正常样本之间的差异表达基因（DEG）。使用注释、可视化和综合发现数据库对DEG进行基因本体富集分析。利用TargetScan、microcosm、miRanda、miRDB和PicTar来预测靶基因。使用lncRNASNP数据库探测与HCC相关的lncRNA，并使用Cytoscape软件可视化lncRNA-miRNA-mRNA调控网络。本研究鉴定出114个差异表达的miRNA和2239个差异表达的mRNA；其中，725个是下调基因，主要参与补体和凝血级联反应、脂肪酸代谢和丁酸代谢等。其余1514个是上调基因，主要参与DNA复制、卵母细胞减数分裂和同源重组等。通过对不同类型RNA之间的关联进行综合分析和靶基因预测，本研究鉴定出203对miRNA-mRNA，包括28个miRNA和170个mRNA，并鉴定出348对lncRNA-miRNA，包含28个miRNA。因此，由于lncRNA-miRNA-mRNA之间的关联，本研究筛选出2721个调控关联。本研究中的数据提供了对可能参与HCC发病机制的基因、功能和通路的全面生物信息学分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f7b/5950030/444fd64121d1/ol-15-06-8371-g06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f7b/5950030/a311a5a257ee/ol-15-06-8371-g00.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f7b/5950030/a311a5a257ee/ol-15-06-8371-g00.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f7b/5950030/02887d412297/ol-15-06-8371-g01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f7b/5950030/4f2817383109/ol-15-06-8371-g02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f7b/5950030/6aa26ac7199c/ol-15-06-8371-g03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f7b/5950030/53315d07e049/ol-15-06-8371-g04.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f7b/5950030/444fd64121d1/ol-15-06-8371-g06.jpg

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