A novel autophagy‐related lncRNA prognostic risk model for breast cancer
Long non‐coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy‐related lncRNAs with prognostic valu...
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| Veröffentlicht in: | Journal of cellular and molecular medicine 2021-01, Vol.25 (1), p.4-14 |
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| description | Long non‐coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy‐related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy‐related lncRNAs in breast cancer by constructing a co‐expression network of autophagy‐related mRNAs‐lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy‐related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy‐related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2‐DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan‐Meier analysis, univariate and multivariate Cox regression analyses and time‐dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low‐risk and high‐risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy‐related lncRNAs has significant prognostic value for breast cancer and might be autophagy‐related therapeutic targets in clinical practice. |
| doi_str_mv | 10.1111/jcmm.15980 |
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LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy‐related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy‐related lncRNAs in breast cancer by constructing a co‐expression network of autophagy‐related mRNAs‐lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy‐related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy‐related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2‐DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan‐Meier analysis, univariate and multivariate Cox regression analyses and time‐dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low‐risk and high‐risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy‐related lncRNAs has significant prognostic value for breast cancer and might be autophagy‐related therapeutic targets in clinical practice.</description><identifier>ISSN: 1582-1838</identifier><identifier>EISSN: 1582-4934</identifier><identifier>DOI: 10.1111/jcmm.15980</identifier><identifier>PMID: 33216456</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>Autophagy ; Biomarkers ; Breast cancer ; Classification ; Clinical medicine ; Gene expression ; Gene set enrichment analysis ; Genomes ; long non‐coding RNAs (lncRNAs) ; Medical prognosis ; Metastasis ; Mortality ; Non-coding RNA ; Patients ; Phagocytosis ; Principal components analysis ; prognosis ; Review ; Reviews ; Risk groups ; risk model ; Survival analysis</subject><ispartof>Journal of cellular and molecular medicine, 2021-01, Vol.25 (1), p.4-14</ispartof><rights>2020 The Authors. published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.</rights><rights>2020 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.</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-c4480-a7f5879232e71abcbac08b600a8fd271a5b11468a9e3304f91c651feecd8586f3</citedby><cites>FETCH-LOGICAL-c4480-a7f5879232e71abcbac08b600a8fd271a5b11468a9e3304f91c651feecd8586f3</cites><orcidid>0000-0002-4569-5649</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/PMC7810925/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810925/$$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/33216456$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Xiaoying</creatorcontrib><creatorcontrib>Jin, Feng</creatorcontrib><creatorcontrib>Li, Yang</creatorcontrib><title>A novel autophagy‐related lncRNA prognostic risk model for breast cancer</title><title>Journal of cellular and molecular medicine</title><addtitle>J Cell Mol Med</addtitle><description>Long non‐coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy‐related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy‐related lncRNAs in breast cancer by constructing a co‐expression network of autophagy‐related mRNAs‐lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy‐related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy‐related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2‐DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan‐Meier analysis, univariate and multivariate Cox regression analyses and time‐dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low‐risk and high‐risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy‐related lncRNAs has significant prognostic value for breast cancer and might be autophagy‐related therapeutic targets in clinical practice.</description><subject>Autophagy</subject><subject>Biomarkers</subject><subject>Breast cancer</subject><subject>Classification</subject><subject>Clinical medicine</subject><subject>Gene expression</subject><subject>Gene set enrichment analysis</subject><subject>Genomes</subject><subject>long non‐coding RNAs (lncRNAs)</subject><subject>Medical prognosis</subject><subject>Metastasis</subject><subject>Mortality</subject><subject>Non-coding RNA</subject><subject>Patients</subject><subject>Phagocytosis</subject><subject>Principal components analysis</subject><subject>prognosis</subject><subject>Review</subject><subject>Reviews</subject><subject>Risk groups</subject><subject>risk model</subject><subject>Survival analysis</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>eNp9kd9KwzAUh4Mobk5vfAApeCNCZ9K0XXIjjOG_MRVEr0Oanm6dbTOTdrI7H8Fn9EnM3Bzqhbk5IefLlxN-CB0S3CVunU1VWXZJxBneQm0SscAPOQ2313vCKGuhPWunGNOYUL6LWpQGJA6juI2Gfa_Scyg82dR6NpHjxcfbu4FC1pB6RaUe7vrezOhxpW2dK8_k9tkrdeouZNp4iQFpa0_JSoHZRzuZLCwcrGsHPV1ePA6u_dH91c2gP_JVGDLsy14WsR4PaAA9IhOVSIVZEmMsWZYG7ihKCAljJjlQisOMExVHJANQKYtYnNEOOl95Z01SQqqgqo0sxMzkpTQLoWUufneqfCLGei56jGAeRE5wshYY_dKArUWZWwVFISvQjRVBGFPMmXvcocd_0KluTOW-5yjnY4yzJXW6opTR1hrINsMQLJYRiWVE4isiBx_9HH-DfmfiALICXvMCFv-oxHBwe7uSfgJQfp1D</recordid><startdate>202101</startdate><enddate>202101</enddate><creator>Li, Xiaoying</creator><creator>Jin, Feng</creator><creator>Li, Yang</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/0000-0002-4569-5649</orcidid></search><sort><creationdate>202101</creationdate><title>A novel autophagy‐related lncRNA prognostic risk model for breast cancer</title><author>Li, Xiaoying ; Jin, Feng ; Li, Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4480-a7f5879232e71abcbac08b600a8fd271a5b11468a9e3304f91c651feecd8586f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Autophagy</topic><topic>Biomarkers</topic><topic>Breast cancer</topic><topic>Classification</topic><topic>Clinical medicine</topic><topic>Gene expression</topic><topic>Gene set enrichment analysis</topic><topic>Genomes</topic><topic>long non‐coding RNAs (lncRNAs)</topic><topic>Medical prognosis</topic><topic>Metastasis</topic><topic>Mortality</topic><topic>Non-coding RNA</topic><topic>Patients</topic><topic>Phagocytosis</topic><topic>Principal components analysis</topic><topic>prognosis</topic><topic>Review</topic><topic>Reviews</topic><topic>Risk groups</topic><topic>risk model</topic><topic>Survival analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xiaoying</creatorcontrib><creatorcontrib>Jin, Feng</creatorcontrib><creatorcontrib>Li, Yang</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Journals</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</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>PML(ProQuest Medical Library)</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</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>Li, Xiaoying</au><au>Jin, Feng</au><au>Li, Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel autophagy‐related lncRNA prognostic risk model for breast cancer</atitle><jtitle>Journal of cellular and molecular medicine</jtitle><addtitle>J Cell Mol Med</addtitle><date>2021-01</date><risdate>2021</risdate><volume>25</volume><issue>1</issue><spage>4</spage><epage>14</epage><pages>4-14</pages><issn>1582-1838</issn><eissn>1582-4934</eissn><abstract>Long non‐coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy‐related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy‐related lncRNAs in breast cancer by constructing a co‐expression network of autophagy‐related mRNAs‐lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy‐related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy‐related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2‐DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan‐Meier analysis, univariate and multivariate Cox regression analyses and time‐dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low‐risk and high‐risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy‐related lncRNAs has significant prognostic value for breast cancer and might be autophagy‐related therapeutic targets in clinical practice.</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>33216456</pmid><doi>10.1111/jcmm.15980</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-4569-5649</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Wiley-Blackwell Journals; Wiley Online Library Open Access; DOAJ Directory of Open Access Journals; PubMed Central; EZB Electronic Journals Library |
| subjects | Autophagy Biomarkers Breast cancer Classification Clinical medicine Gene expression Gene set enrichment analysis Genomes long non‐coding RNAs (lncRNAs) Medical prognosis Metastasis Mortality Non-coding RNA Patients Phagocytosis Principal components analysis prognosis Review Reviews Risk groups risk model Survival analysis |
| title | A novel autophagy‐related lncRNA prognostic risk model for breast cancer |
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