Gene-gene interaction network analysis of hepatocellular carcinoma using bioinformatic software
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...
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Veröffentlicht in: | Oncology letters 2018-06, Vol.15 (6), p.8371-8377 |
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description | 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. |
doi_str_mv | 10.3892/ol.2018.8408 |
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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.</description><identifier>ISSN: 1792-1074</identifier><identifier>EISSN: 1792-1082</identifier><identifier>DOI: 10.3892/ol.2018.8408</identifier><identifier>PMID: 29805571</identifier><language>eng</language><publisher>Greece: Spandidos Publications</publisher><subject>Apoptosis ; Binomial distribution ; Carcinogenesis ; Cell growth ; Datasets ; Development and progression ; Epistasis ; Gene expression ; Genetic aspects ; Genomes ; Health aspects ; Hepatocellular carcinoma ; Liver cancer ; Metabolism ; MicroRNAs ; Oncology ; Pathogenesis ; Proteins ; RNA ; Software ; Studies ; Tumorigenesis ; Visualization</subject><ispartof>Oncology letters, 2018-06, Vol.15 (6), p.8371-8377</ispartof><rights>COPYRIGHT 2018 Spandidos Publications</rights><rights>Copyright Spandidos Publications UK Ltd. 2018</rights><rights>Copyright: © He et al. 2018</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c510t-cd48972e69d062a23441f90d5a34e4492413ca1f3f213b9ac6a5b4d533d49e1f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5950030/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5950030/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29805571$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>He, Jin-Hua</creatorcontrib><creatorcontrib>Han, Ze-Ping</creatorcontrib><creatorcontrib>Wu, Pu-Zhao</creatorcontrib><creatorcontrib>Zou, Mao-Xian</creatorcontrib><creatorcontrib>Wang, Li</creatorcontrib><creatorcontrib>Lv, Yu-Bing</creatorcontrib><creatorcontrib>Zhou, Jia-Bin</creatorcontrib><creatorcontrib>Cao, Ming-Rong</creatorcontrib><creatorcontrib>Li, Yu-Guang</creatorcontrib><title>Gene-gene interaction network analysis of hepatocellular carcinoma using bioinformatic software</title><title>Oncology letters</title><addtitle>Oncol Lett</addtitle><description>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.</description><subject>Apoptosis</subject><subject>Binomial distribution</subject><subject>Carcinogenesis</subject><subject>Cell growth</subject><subject>Datasets</subject><subject>Development and progression</subject><subject>Epistasis</subject><subject>Gene expression</subject><subject>Genetic aspects</subject><subject>Genomes</subject><subject>Health aspects</subject><subject>Hepatocellular carcinoma</subject><subject>Liver cancer</subject><subject>Metabolism</subject><subject>MicroRNAs</subject><subject>Oncology</subject><subject>Pathogenesis</subject><subject>Proteins</subject><subject>RNA</subject><subject>Software</subject><subject>Studies</subject><subject>Tumorigenesis</subject><subject>Visualization</subject><issn>1792-1074</issn><issn>1792-1082</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNptkt1rFDEUxQdRbKl981kGBPHBWfM5m7wIpWgVCr7oc7ibubObmknWJGPpf2-G1rUrJpCE5HdPyMlpmpeUrLjS7H30K0aoWilB1JPmlK416yhR7OlhvRYnzXnON6Q22VOl-ufNCdOKSLmmp425woDdtg6tCwUT2OJiaAOW25h-tBDA32WX2zi2O9xDiRa9nz2k1kKyLsQJ2jm7sG03LrowxjRBcbbNcSy3kPBF82wEn_H8YT5rvn_6-O3yc3f99erL5cV1ZyUlpbODUHrNsNcD6RkwLgQdNRkkcIFCaCYot0BHPjLKNxpsD3IjBsn5IDTW_bPmw73uft5MOFgMJYE3--QmSHcmgjPHJ8HtzDb-MlJLQjipAm8fBFL8OWMuZnJ5eSwEjHM2jIieUKo4rejrf9CbOKfq1EJpKTjpSf-X2oJHs1hT77WLqLmQUkhOaK8rtfoPVfuAk7Mx4Ojq_lHBm0cFOwRfdjn6efm2fAy-uwdtijknHA9mUGKW8JjozRIes4Sn4q8eG3iA_0SF_wbWkb6K</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>He, Jin-Hua</creator><creator>Han, Ze-Ping</creator><creator>Wu, Pu-Zhao</creator><creator>Zou, Mao-Xian</creator><creator>Wang, Li</creator><creator>Lv, Yu-Bing</creator><creator>Zhou, Jia-Bin</creator><creator>Cao, Ming-Rong</creator><creator>Li, Yu-Guang</creator><general>Spandidos Publications</general><general>Spandidos Publications UK Ltd</general><general>D.A. 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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.</abstract><cop>Greece</cop><pub>Spandidos Publications</pub><pmid>29805571</pmid><doi>10.3892/ol.2018.8408</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Apoptosis Binomial distribution Carcinogenesis Cell growth Datasets Development and progression Epistasis Gene expression Genetic aspects Genomes Health aspects Hepatocellular carcinoma Liver cancer Metabolism MicroRNAs Oncology Pathogenesis Proteins RNA Software Studies Tumorigenesis Visualization |
title | Gene-gene interaction network analysis of hepatocellular carcinoma using bioinformatic software |
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