Identification of Hub Genes for Colorectal Cancer with Liver Metastasis Using miRNA-mRNA Network
Background. Liver metastasis is an important cause of death in patients with colorectal cancer (CRC). Increasing evidence indicates that microRNAs (miRNAs) are involved in the pathogenesis of colorectal cancer liver metastasis (CRLM). This study is aimed at exploring the potential miRNA-mRNA regulat...
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description | Background. Liver metastasis is an important cause of death in patients with colorectal cancer (CRC). Increasing evidence indicates that microRNAs (miRNAs) are involved in the pathogenesis of colorectal cancer liver metastasis (CRLM). This study is aimed at exploring the potential miRNA-mRNA regulatory network. Methods. From the GEO database, we downloaded the microarray datasets GSE56350 and GSE73178. GEO2R was used to conduct differentially expressed miRNAs (DEMs) between CRC and CRLM using the GEO2R tool. Then, GO and KEGG pathway analysis for differentially expressed genes (DEGs) performed via DAVID. A protein-protein interaction (PPI) network was constructed by the STRING and identified by Cytoscape. Hub genes were identified by miRNA-mRNA network. Finally, the expression of the hub gene expression was assessed in the GSE81558. Results. The four DEMs (hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p) were identified as common DEMs in GSE56350 and GSE73178 datasets. The SP1 was likely to adjust the upregulated DEMs; however, the YY1 could regulate both the upregulated and downregulated DEMs. A total of 3925 genes (3447 upregulated DEM genes and 478 downregulated DEM genes) were screened. These predicted genes were mainly linked to Platinum drug resistance, Cellular senescence, and ErbB signaling pathway. Through the gene network construction, most of the hub genes were found to be modulated by hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p. Among the top 20 hub genes, the expression of CREB1, RHOA, and EGFR was significantly different in the GSE81558 dataset. Conclusion. In this study, miRNA-mRNA networks in CRLM were screened between CRC patients and CRLM patients to provide a new method to predict for the pathogenesis and development of CRC. |
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Liver metastasis is an important cause of death in patients with colorectal cancer (CRC). Increasing evidence indicates that microRNAs (miRNAs) are involved in the pathogenesis of colorectal cancer liver metastasis (CRLM). This study is aimed at exploring the potential miRNA-mRNA regulatory network. Methods. From the GEO database, we downloaded the microarray datasets GSE56350 and GSE73178. GEO2R was used to conduct differentially expressed miRNAs (DEMs) between CRC and CRLM using the GEO2R tool. Then, GO and KEGG pathway analysis for differentially expressed genes (DEGs) performed via DAVID. A protein-protein interaction (PPI) network was constructed by the STRING and identified by Cytoscape. Hub genes were identified by miRNA-mRNA network. Finally, the expression of the hub gene expression was assessed in the GSE81558. Results. The four DEMs (hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p) were identified as common DEMs in GSE56350 and GSE73178 datasets. The SP1 was likely to adjust the upregulated DEMs; however, the YY1 could regulate both the upregulated and downregulated DEMs. A total of 3925 genes (3447 upregulated DEM genes and 478 downregulated DEM genes) were screened. These predicted genes were mainly linked to Platinum drug resistance, Cellular senescence, and ErbB signaling pathway. Through the gene network construction, most of the hub genes were found to be modulated by hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p. Among the top 20 hub genes, the expression of CREB1, RHOA, and EGFR was significantly different in the GSE81558 dataset. Conclusion. In this study, miRNA-mRNA networks in CRLM were screened between CRC patients and CRLM patients to provide a new method to predict for the pathogenesis and development of CRC.</description><identifier>ISSN: 0278-0240</identifier><identifier>EISSN: 1875-8630</identifier><identifier>DOI: 10.1155/2023/2295788</identifier><identifier>PMID: 36798788</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Bioinformatics ; Cancer ; Cell growth ; Colorectal cancer ; Colorectal carcinoma ; Colorectal Neoplasms - genetics ; Datasets ; DNA microarrays ; Drug resistance ; ErbB protein ; Gene expression ; Gene Expression Profiling - methods ; Gene Regulatory Networks ; Genes ; Humans ; Kinases ; Liver ; Liver cancer ; Liver Neoplasms - genetics ; Medical diagnosis ; Medical prognosis ; Metastases ; Metastasis ; MicroRNAs ; MicroRNAs - genetics ; MicroRNAs - metabolism ; miRNA ; Pancreatic cancer ; Pathogenesis ; Platinum ; Protein interaction ; Proteins ; RhoA protein ; Ribonucleic acid ; RNA ; RNA, Messenger - genetics ; RNA, Messenger - metabolism ; Senescence ; Signal transduction</subject><ispartof>Disease markers, 2023, Vol.2023, p.2295788-14</ispartof><rights>Copyright © 2023 Si-ming Zhang et al.</rights><rights>Copyright © 2023 Si-ming Zhang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2023 Si-ming Zhang et al. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3638-5ed79a62e832450bf81c5db138eca1442be8e1322b549477363c02ff49258b4d3</citedby><cites>FETCH-LOGICAL-c3638-5ed79a62e832450bf81c5db138eca1442be8e1322b549477363c02ff49258b4d3</cites><orcidid>0000-0003-0994-6719 ; 0000-0002-2058-5427 ; 0000-0002-8840-2666</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928517/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928517/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,4010,27904,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36798788$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Zhong, Wei long</contributor><contributor>Wei long Zhong</contributor><creatorcontrib>Zhang, Si-ming</creatorcontrib><creatorcontrib>Shen, Cheng</creatorcontrib><creatorcontrib>Li, Jing</creatorcontrib><creatorcontrib>Wang, Xing-dan</creatorcontrib><creatorcontrib>Zhai, Si-qiu</creatorcontrib><creatorcontrib>Shi, Ling-ling</creatorcontrib><creatorcontrib>Lu, Dan-li</creatorcontrib><creatorcontrib>Jiang, Xiao-hui</creatorcontrib><creatorcontrib>Qiu, Junlan</creatorcontrib><title>Identification of Hub Genes for Colorectal Cancer with Liver Metastasis Using miRNA-mRNA Network</title><title>Disease markers</title><addtitle>Dis Markers</addtitle><description>Background. Liver metastasis is an important cause of death in patients with colorectal cancer (CRC). Increasing evidence indicates that microRNAs (miRNAs) are involved in the pathogenesis of colorectal cancer liver metastasis (CRLM). This study is aimed at exploring the potential miRNA-mRNA regulatory network. Methods. From the GEO database, we downloaded the microarray datasets GSE56350 and GSE73178. GEO2R was used to conduct differentially expressed miRNAs (DEMs) between CRC and CRLM using the GEO2R tool. Then, GO and KEGG pathway analysis for differentially expressed genes (DEGs) performed via DAVID. A protein-protein interaction (PPI) network was constructed by the STRING and identified by Cytoscape. Hub genes were identified by miRNA-mRNA network. Finally, the expression of the hub gene expression was assessed in the GSE81558. Results. The four DEMs (hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p) were identified as common DEMs in GSE56350 and GSE73178 datasets. The SP1 was likely to adjust the upregulated DEMs; however, the YY1 could regulate both the upregulated and downregulated DEMs. A total of 3925 genes (3447 upregulated DEM genes and 478 downregulated DEM genes) were screened. These predicted genes were mainly linked to Platinum drug resistance, Cellular senescence, and ErbB signaling pathway. Through the gene network construction, most of the hub genes were found to be modulated by hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p. Among the top 20 hub genes, the expression of CREB1, RHOA, and EGFR was significantly different in the GSE81558 dataset. Conclusion. In this study, miRNA-mRNA networks in CRLM were screened between CRC patients and CRLM patients to provide a new method to predict for the pathogenesis and development of CRC.</description><subject>Bioinformatics</subject><subject>Cancer</subject><subject>Cell growth</subject><subject>Colorectal cancer</subject><subject>Colorectal carcinoma</subject><subject>Colorectal Neoplasms - genetics</subject><subject>Datasets</subject><subject>DNA microarrays</subject><subject>Drug resistance</subject><subject>ErbB protein</subject><subject>Gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Regulatory Networks</subject><subject>Genes</subject><subject>Humans</subject><subject>Kinases</subject><subject>Liver</subject><subject>Liver cancer</subject><subject>Liver Neoplasms - genetics</subject><subject>Medical diagnosis</subject><subject>Medical prognosis</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>MicroRNAs</subject><subject>MicroRNAs - genetics</subject><subject>MicroRNAs - metabolism</subject><subject>miRNA</subject><subject>Pancreatic cancer</subject><subject>Pathogenesis</subject><subject>Platinum</subject><subject>Protein interaction</subject><subject>Proteins</subject><subject>RhoA protein</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA, Messenger - genetics</subject><subject>RNA, Messenger - metabolism</subject><subject>Senescence</subject><subject>Signal transduction</subject><issn>0278-0240</issn><issn>1875-8630</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kd1rFDEUxYModlt981kCvgh2bD4nyYtQFm0LawWxzzGTudNNnZm0yUyX_vdm2W1RH4Rwc-H-7uEeDkJvKPlIqZQnjDB-wpiRSutnaEG1kpWuOXmOFoQpXREmyAE6zPmGEMqMMC_RAa-V0YVfoJ8XLYxT6IJ3U4gjjh0-nxt8BiNk3MWEl7GPCfzkerx0o4eEN2Fa41W4L-1XmFwuL2R8lcN4jYfw_fK0GkrBlzBtYvr1Cr3oXJ_h9f4_QldfPv9Ynlerb2cXy9NV5XnNdSWhVcbVDDRnQpKm09TLtqFcg3dUCNaABsoZa6QwQqmy5AnrOmGY1I1o-RH6tNO9nZsBWl9cJdfb2xQGlx5sdMH-PRnD2l7He2sM05KqIvB-L5Di3Qx5skPIHvrejRDnbJlSuiZUUVrQd_-gN3FOY7G3pZRhTNG6UMc7yqeYc4Lu6RhK7DY6u43O7qMr-Ns_DTzBj1kV4MMOWIexdZvwf7nfp5qf4Q</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Zhang, Si-ming</creator><creator>Shen, Cheng</creator><creator>Li, Jing</creator><creator>Wang, Xing-dan</creator><creator>Zhai, Si-qiu</creator><creator>Shi, Ling-ling</creator><creator>Lu, Dan-li</creator><creator>Jiang, Xiao-hui</creator><creator>Qiu, Junlan</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-0994-6719</orcidid><orcidid>https://orcid.org/0000-0002-2058-5427</orcidid><orcidid>https://orcid.org/0000-0002-8840-2666</orcidid></search><sort><creationdate>2023</creationdate><title>Identification of Hub Genes for Colorectal Cancer with Liver Metastasis Using miRNA-mRNA Network</title><author>Zhang, Si-ming ; Shen, Cheng ; Li, Jing ; Wang, Xing-dan ; Zhai, Si-qiu ; Shi, Ling-ling ; Lu, Dan-li ; Jiang, Xiao-hui ; Qiu, Junlan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3638-5ed79a62e832450bf81c5db138eca1442be8e1322b549477363c02ff49258b4d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Bioinformatics</topic><topic>Cancer</topic><topic>Cell growth</topic><topic>Colorectal cancer</topic><topic>Colorectal carcinoma</topic><topic>Colorectal Neoplasms - genetics</topic><topic>Datasets</topic><topic>DNA microarrays</topic><topic>Drug resistance</topic><topic>ErbB protein</topic><topic>Gene expression</topic><topic>Gene Expression Profiling - methods</topic><topic>Gene Regulatory Networks</topic><topic>Genes</topic><topic>Humans</topic><topic>Kinases</topic><topic>Liver</topic><topic>Liver cancer</topic><topic>Liver Neoplasms - genetics</topic><topic>Medical diagnosis</topic><topic>Medical prognosis</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>MicroRNAs</topic><topic>MicroRNAs - genetics</topic><topic>MicroRNAs - metabolism</topic><topic>miRNA</topic><topic>Pancreatic cancer</topic><topic>Pathogenesis</topic><topic>Platinum</topic><topic>Protein interaction</topic><topic>Proteins</topic><topic>RhoA protein</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>RNA, Messenger - genetics</topic><topic>RNA, Messenger - metabolism</topic><topic>Senescence</topic><topic>Signal transduction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Si-ming</creatorcontrib><creatorcontrib>Shen, Cheng</creatorcontrib><creatorcontrib>Li, Jing</creatorcontrib><creatorcontrib>Wang, Xing-dan</creatorcontrib><creatorcontrib>Zhai, Si-qiu</creatorcontrib><creatorcontrib>Shi, Ling-ling</creatorcontrib><creatorcontrib>Lu, Dan-li</creatorcontrib><creatorcontrib>Jiang, Xiao-hui</creatorcontrib><creatorcontrib>Qiu, Junlan</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Disease markers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Si-ming</au><au>Shen, Cheng</au><au>Li, Jing</au><au>Wang, Xing-dan</au><au>Zhai, Si-qiu</au><au>Shi, Ling-ling</au><au>Lu, Dan-li</au><au>Jiang, Xiao-hui</au><au>Qiu, Junlan</au><au>Zhong, Wei long</au><au>Wei long Zhong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of Hub Genes for Colorectal Cancer with Liver Metastasis Using miRNA-mRNA Network</atitle><jtitle>Disease markers</jtitle><addtitle>Dis Markers</addtitle><date>2023</date><risdate>2023</risdate><volume>2023</volume><spage>2295788</spage><epage>14</epage><pages>2295788-14</pages><issn>0278-0240</issn><eissn>1875-8630</eissn><abstract>Background. Liver metastasis is an important cause of death in patients with colorectal cancer (CRC). Increasing evidence indicates that microRNAs (miRNAs) are involved in the pathogenesis of colorectal cancer liver metastasis (CRLM). This study is aimed at exploring the potential miRNA-mRNA regulatory network. Methods. From the GEO database, we downloaded the microarray datasets GSE56350 and GSE73178. GEO2R was used to conduct differentially expressed miRNAs (DEMs) between CRC and CRLM using the GEO2R tool. Then, GO and KEGG pathway analysis for differentially expressed genes (DEGs) performed via DAVID. A protein-protein interaction (PPI) network was constructed by the STRING and identified by Cytoscape. Hub genes were identified by miRNA-mRNA network. Finally, the expression of the hub gene expression was assessed in the GSE81558. Results. The four DEMs (hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p) were identified as common DEMs in GSE56350 and GSE73178 datasets. The SP1 was likely to adjust the upregulated DEMs; however, the YY1 could regulate both the upregulated and downregulated DEMs. A total of 3925 genes (3447 upregulated DEM genes and 478 downregulated DEM genes) were screened. These predicted genes were mainly linked to Platinum drug resistance, Cellular senescence, and ErbB signaling pathway. Through the gene network construction, most of the hub genes were found to be modulated by hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p. Among the top 20 hub genes, the expression of CREB1, RHOA, and EGFR was significantly different in the GSE81558 dataset. Conclusion. In this study, miRNA-mRNA networks in CRLM were screened between CRC patients and CRLM patients to provide a new method to predict for the pathogenesis and development of CRC.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>36798788</pmid><doi>10.1155/2023/2295788</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-0994-6719</orcidid><orcidid>https://orcid.org/0000-0002-2058-5427</orcidid><orcidid>https://orcid.org/0000-0002-8840-2666</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Bioinformatics Cancer Cell growth Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - genetics Datasets DNA microarrays Drug resistance ErbB protein Gene expression Gene Expression Profiling - methods Gene Regulatory Networks Genes Humans Kinases Liver Liver cancer Liver Neoplasms - genetics Medical diagnosis Medical prognosis Metastases Metastasis MicroRNAs MicroRNAs - genetics MicroRNAs - metabolism miRNA Pancreatic cancer Pathogenesis Platinum Protein interaction Proteins RhoA protein Ribonucleic acid RNA RNA, Messenger - genetics RNA, Messenger - metabolism Senescence Signal transduction |
title | Identification of Hub Genes for Colorectal Cancer with Liver Metastasis Using miRNA-mRNA Network |
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