A novel autophagy‐related lncRNA survival model for lung adenocarcinoma
Long non‐coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic val...
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| Veröffentlicht in: | Journal of cellular and molecular medicine 2021-06, Vol.25 (12), p.5681-5690 |
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| description | Long non‐coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy‐related lncRNA in lung adenocarcinoma. In this study, autophagy‐related mRNAs‐lncRNAs were screened from lung adenocarcinoma and a co‐expression network of autophagy‐related mRNAs‐lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy‐related lncRNAs and finally obtained a survival model composed of 11 autophagy‐related lncRNAs. Through Kaplan‐Meier analysis, univariate and multivariate Cox regression analysis and time‐dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low‐risk and high‐risk groups. These 11 lncRNAs were GAS6‐AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA‐DQB1‐AS1, LINC01116, LINC01806, FAM83A‐AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196‐1.320) (P |
| doi_str_mv | 10.1111/jcmm.16582 |
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LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy‐related lncRNA in lung adenocarcinoma. In this study, autophagy‐related mRNAs‐lncRNAs were screened from lung adenocarcinoma and a co‐expression network of autophagy‐related mRNAs‐lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy‐related lncRNAs and finally obtained a survival model composed of 11 autophagy‐related lncRNAs. Through Kaplan‐Meier analysis, univariate and multivariate Cox regression analysis and time‐dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low‐risk and high‐risk groups. These 11 lncRNAs were GAS6‐AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA‐DQB1‐AS1, LINC01116, LINC01806, FAM83A‐AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196‐1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149‐1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy‐related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy‐related therapeutic targets.</description><identifier>ISSN: 1582-1838</identifier><identifier>EISSN: 1582-4934</identifier><identifier>DOI: 10.1111/jcmm.16582</identifier><identifier>PMID: 33987935</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>Adenocarcinoma ; Autophagy ; Calibration ; Gene expression ; Gene set enrichment analysis ; Genomes ; long non‐coding RNA (lncRNA) ; lung adenocarcinoma (LUAD) ; Lung cancer ; Lungs ; Medical prognosis ; Mortality ; Non-coding RNA ; Original ; Patients ; Phagocytosis ; Physiology ; Prognosis ; Regression analysis ; Risk groups ; Software ; Survival ; Survival analysis ; The Cancer Genome Atlas (TCGA) ; Therapeutic targets</subject><ispartof>Journal of cellular and molecular medicine, 2021-06, Vol.25 (12), p.5681-5690</ispartof><rights>2021 The Authors. published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.</rights><rights>2021 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4482-5983f9199a118f925aebc63ef2c227408c28da12640a1822e3cc6543d07f98943</citedby><cites>FETCH-LOGICAL-c4482-5983f9199a118f925aebc63ef2c227408c28da12640a1822e3cc6543d07f98943</cites><orcidid>0000-0002-3150-2260</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/PMC8184679/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184679/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,1417,11562,27924,27925,45574,45575,46052,46476,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33987935$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Liwei</creatorcontrib><creatorcontrib>Wen, Zilu</creatorcontrib><creatorcontrib>Song, Yanzheng</creatorcontrib><creatorcontrib>Wang, Lin</creatorcontrib><title>A novel autophagy‐related lncRNA survival model for lung adenocarcinoma</title><title>Journal of cellular and molecular medicine</title><addtitle>J Cell Mol Med</addtitle><description>Long non‐coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy‐related lncRNA in lung adenocarcinoma. In this study, autophagy‐related mRNAs‐lncRNAs were screened from lung adenocarcinoma and a co‐expression network of autophagy‐related mRNAs‐lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy‐related lncRNAs and finally obtained a survival model composed of 11 autophagy‐related lncRNAs. Through Kaplan‐Meier analysis, univariate and multivariate Cox regression analysis and time‐dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low‐risk and high‐risk groups. These 11 lncRNAs were GAS6‐AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA‐DQB1‐AS1, LINC01116, LINC01806, FAM83A‐AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196‐1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149‐1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy‐related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy‐related therapeutic targets.</description><subject>Adenocarcinoma</subject><subject>Autophagy</subject><subject>Calibration</subject><subject>Gene expression</subject><subject>Gene set enrichment analysis</subject><subject>Genomes</subject><subject>long non‐coding RNA (lncRNA)</subject><subject>lung adenocarcinoma (LUAD)</subject><subject>Lung cancer</subject><subject>Lungs</subject><subject>Medical prognosis</subject><subject>Mortality</subject><subject>Non-coding RNA</subject><subject>Original</subject><subject>Patients</subject><subject>Phagocytosis</subject><subject>Physiology</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Risk groups</subject><subject>Software</subject><subject>Survival</subject><subject>Survival analysis</subject><subject>The Cancer Genome Atlas (TCGA)</subject><subject>Therapeutic targets</subject><issn>1582-1838</issn><issn>1582-4934</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kc1KAzEUhYMotlY3PoAMuBGhdfIzM8lGKMWfSqsgug5pJtNOySQ16VS68xF8Rp_E1NaiLrybe-F-nHsuB4BjGHdgqIuprKoOTBOKdkAThtYmDJPdzQwppg1w4P00jnEKMdsHDYwZzRhOmqDfjYxdKB2Jem5nEzFefry9O6XFXOWRNvLxvhv52i3KhdBRZfNAFtZFujbjSOTKWCmcLI2txCHYK4T26mjTW-D5-uqpd9sePNz0e91BWxIS7CSM4oJBxgSEtGAoEWokU6wKJBHKSEwlormAKCWxgBQhhaVME4LzOCsYZQS3wOVad1aPKpVLZeZOaD5zZSXckltR8t8bU0742C44hZSk4esWONsIOPtSKz_nVeml0loYZWvPUYIozFKEV7dO_6BTWzsT3gsUzmjCUoYCdb6mpLPeO1VszcCYrxLiq4T4V0IBPvlpf4t-RxIAuAZeS62W_0jxu95wuBb9BNBKnDI</recordid><startdate>202106</startdate><enddate>202106</enddate><creator>Wu, Liwei</creator><creator>Wen, Zilu</creator><creator>Song, Yanzheng</creator><creator>Wang, Lin</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3150-2260</orcidid></search><sort><creationdate>202106</creationdate><title>A novel autophagy‐related lncRNA survival model for lung adenocarcinoma</title><author>Wu, Liwei ; Wen, Zilu ; Song, Yanzheng ; Wang, Lin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4482-5983f9199a118f925aebc63ef2c227408c28da12640a1822e3cc6543d07f98943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adenocarcinoma</topic><topic>Autophagy</topic><topic>Calibration</topic><topic>Gene expression</topic><topic>Gene set enrichment analysis</topic><topic>Genomes</topic><topic>long non‐coding RNA (lncRNA)</topic><topic>lung adenocarcinoma (LUAD)</topic><topic>Lung cancer</topic><topic>Lungs</topic><topic>Medical prognosis</topic><topic>Mortality</topic><topic>Non-coding RNA</topic><topic>Original</topic><topic>Patients</topic><topic>Phagocytosis</topic><topic>Physiology</topic><topic>Prognosis</topic><topic>Regression analysis</topic><topic>Risk groups</topic><topic>Software</topic><topic>Survival</topic><topic>Survival analysis</topic><topic>The Cancer Genome Atlas (TCGA)</topic><topic>Therapeutic targets</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Liwei</creatorcontrib><creatorcontrib>Wen, Zilu</creatorcontrib><creatorcontrib>Song, Yanzheng</creatorcontrib><creatorcontrib>Wang, Lin</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Free Content</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of cellular and molecular medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Liwei</au><au>Wen, Zilu</au><au>Song, Yanzheng</au><au>Wang, Lin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel autophagy‐related lncRNA survival model for lung adenocarcinoma</atitle><jtitle>Journal of cellular and molecular medicine</jtitle><addtitle>J Cell Mol Med</addtitle><date>2021-06</date><risdate>2021</risdate><volume>25</volume><issue>12</issue><spage>5681</spage><epage>5690</epage><pages>5681-5690</pages><issn>1582-1838</issn><eissn>1582-4934</eissn><abstract>Long non‐coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy‐related lncRNA in lung adenocarcinoma. In this study, autophagy‐related mRNAs‐lncRNAs were screened from lung adenocarcinoma and a co‐expression network of autophagy‐related mRNAs‐lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy‐related lncRNAs and finally obtained a survival model composed of 11 autophagy‐related lncRNAs. Through Kaplan‐Meier analysis, univariate and multivariate Cox regression analysis and time‐dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low‐risk and high‐risk groups. These 11 lncRNAs were GAS6‐AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA‐DQB1‐AS1, LINC01116, LINC01806, FAM83A‐AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196‐1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149‐1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy‐related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy‐related therapeutic targets.</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>33987935</pmid><doi>10.1111/jcmm.16582</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-3150-2260</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adenocarcinoma Autophagy Calibration Gene expression Gene set enrichment analysis Genomes long non‐coding RNA (lncRNA) lung adenocarcinoma (LUAD) Lung cancer Lungs Medical prognosis Mortality Non-coding RNA Original Patients Phagocytosis Physiology Prognosis Regression analysis Risk groups Software Survival Survival analysis The Cancer Genome Atlas (TCGA) Therapeutic targets |
| title | A novel autophagy‐related lncRNA survival model for lung adenocarcinoma |
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