Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma
Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this s...
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| description | Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD. |
| doi_str_mv | 10.1155/2021/4093426 |
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However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2021/4093426</identifier><identifier>PMID: 33628780</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Adenocarcinoma ; Apoptosis ; Bioinformatics ; Biomarkers ; Cell adhesion & migration ; Cell cycle ; Cell division ; Cell growth ; Cell survival ; Computational biology ; Development and progression ; Enrichment ; Epidermal growth factor ; Gene expression ; Gene sequencing ; Genetic aspects ; Hypotheses ; Identification and classification ; Kinases ; Leukemia ; Lung cancer ; Lungs ; Medical prognosis ; Metastases ; MicroRNAs ; miRNA ; Molecular modelling ; mRNA ; Mutation ; Pathogenesis ; Patient outcomes ; Physiological aspects ; Proteins ; Regression analysis ; Regression models ; Regulatory mechanisms (biology) ; RNA ; Smad7 protein ; Survival ; Survival analysis ; Tumor markers ; Tumorigenesis ; Tumors</subject><ispartof>BioMed research international, 2021, Vol.2021 (1), p.4093426-4093426</ispartof><rights>Copyright © 2021 Lixian Chen et al.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>Copyright © 2021 Lixian Chen 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 © 2021 Lixian Chen et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c504t-e7271e296ca58e5a665bceb8d5dd7b906c370e786b7afa457afb330f3eb2ed6b3</citedby><cites>FETCH-LOGICAL-c504t-e7271e296ca58e5a665bceb8d5dd7b906c370e786b7afa457afb330f3eb2ed6b3</cites><orcidid>0000-0002-9380-909X ; 0000-0003-0971-8206</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/PMC7895565/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895565/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4023,27922,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33628780$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Kamaraj, Balu</contributor><contributor>Balu Kamaraj</contributor><creatorcontrib>Chen, Lixian</creatorcontrib><creatorcontrib>Ren, Zhonglu</creatorcontrib><creatorcontrib>Cai, Yongming</creatorcontrib><title>Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.</description><subject>Adenocarcinoma</subject><subject>Apoptosis</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Cell adhesion & migration</subject><subject>Cell cycle</subject><subject>Cell division</subject><subject>Cell growth</subject><subject>Cell survival</subject><subject>Computational biology</subject><subject>Development and progression</subject><subject>Enrichment</subject><subject>Epidermal growth factor</subject><subject>Gene expression</subject><subject>Gene sequencing</subject><subject>Genetic aspects</subject><subject>Hypotheses</subject><subject>Identification and classification</subject><subject>Kinases</subject><subject>Leukemia</subject><subject>Lung cancer</subject><subject>Lungs</subject><subject>Medical prognosis</subject><subject>Metastases</subject><subject>MicroRNAs</subject><subject>miRNA</subject><subject>Molecular modelling</subject><subject>mRNA</subject><subject>Mutation</subject><subject>Pathogenesis</subject><subject>Patient outcomes</subject><subject>Physiological aspects</subject><subject>Proteins</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Regulatory mechanisms (biology)</subject><subject>RNA</subject><subject>Smad7 protein</subject><subject>Survival</subject><subject>Survival analysis</subject><subject>Tumor markers</subject><subject>Tumorigenesis</subject><subject>Tumors</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kktr3DAUhU1paUKaXddF0E0hdSNZ1sObghnSpjCk0MdayNL1jFJbmkr2hPz7ajLT6WMRLSTB-TjiHp2ieEnwO0IYu6xwRS5r3NC64k-K04qSuuSkJk-Pd0pPivOUbnFeknDc8OfFCaW8kkLi02K9CD5NcTaTCx5pb1Hr9XCfXEKhR1_nuHVbPZRtSsE4PYFFizBuYHJ-ha68DSvwYU7oy02LbmC6C_EHch4t5yy3NmtGR-N8GPWL4lmvhwTnh_Os-P7h6tviulx-_vhp0S5Lw3A9lSAqQaBquNFMAtOcs85AJy2zVnQN5oYKDELyTuhe1yzvHaW4p9BVYHlHz4r3e9_N3I1gDfgp6kFtoht1vFdBO_Wv4t1arcJWCdkwxlk2eHMwiOHnDGlSo0sGhkF7yKOqqs5pMyyEzOjr_9DbMMec3wNFBMME13-olR5AOd-H_K7ZmaqWN7xhgkv5OCUpo5g1JFNv95SJIaUI_XEwgtWuEWrXCHVoRMZf_R3GEf79_xm42ANr562-c4_b_QKff7zp</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Chen, Lixian</creator><creator>Ren, Zhonglu</creator><creator>Cai, Yongming</creator><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9380-909X</orcidid><orcidid>https://orcid.org/0000-0003-0971-8206</orcidid></search><sort><creationdate>2021</creationdate><title>Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma</title><author>Chen, Lixian ; Ren, Zhonglu ; Cai, Yongming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c504t-e7271e296ca58e5a665bceb8d5dd7b906c370e786b7afa457afb330f3eb2ed6b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adenocarcinoma</topic><topic>Apoptosis</topic><topic>Bioinformatics</topic><topic>Biomarkers</topic><topic>Cell adhesion & migration</topic><topic>Cell cycle</topic><topic>Cell division</topic><topic>Cell growth</topic><topic>Cell survival</topic><topic>Computational biology</topic><topic>Development and progression</topic><topic>Enrichment</topic><topic>Epidermal growth factor</topic><topic>Gene expression</topic><topic>Gene sequencing</topic><topic>Genetic aspects</topic><topic>Hypotheses</topic><topic>Identification and classification</topic><topic>Kinases</topic><topic>Leukemia</topic><topic>Lung cancer</topic><topic>Lungs</topic><topic>Medical prognosis</topic><topic>Metastases</topic><topic>MicroRNAs</topic><topic>miRNA</topic><topic>Molecular modelling</topic><topic>mRNA</topic><topic>Mutation</topic><topic>Pathogenesis</topic><topic>Patient outcomes</topic><topic>Physiological aspects</topic><topic>Proteins</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Regulatory mechanisms (biology)</topic><topic>RNA</topic><topic>Smad7 protein</topic><topic>Survival</topic><topic>Survival analysis</topic><topic>Tumor markers</topic><topic>Tumorigenesis</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Lixian</creatorcontrib><creatorcontrib>Ren, Zhonglu</creatorcontrib><creatorcontrib>Cai, Yongming</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription 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Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Lixian</au><au>Ren, Zhonglu</au><au>Cai, Yongming</au><au>Kamaraj, Balu</au><au>Balu Kamaraj</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><issue>1</issue><spage>4093426</spage><epage>4093426</epage><pages>4093426-4093426</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>33628780</pmid><doi>10.1155/2021/4093426</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-9380-909X</orcidid><orcidid>https://orcid.org/0000-0003-0971-8206</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adenocarcinoma Apoptosis Bioinformatics Biomarkers Cell adhesion & migration Cell cycle Cell division Cell growth Cell survival Computational biology Development and progression Enrichment Epidermal growth factor Gene expression Gene sequencing Genetic aspects Hypotheses Identification and classification Kinases Leukemia Lung cancer Lungs Medical prognosis Metastases MicroRNAs miRNA Molecular modelling mRNA Mutation Pathogenesis Patient outcomes Physiological aspects Proteins Regression analysis Regression models Regulatory mechanisms (biology) RNA Smad7 protein Survival Survival analysis Tumor markers Tumorigenesis Tumors |
| title | Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma |
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