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Comprehensive analysis of lncRNA‐TF crosstalks and identification of prognostic regulatory feedback loops of glioblastoma using lncRNA/TF‐mediated ceRNA network
Glioblastoma (GBM) has become the most aggressive primary brain tumor in the world. Patients with GBM usually have a poor prognosis. The median survival times of GBM patients retain less than 2 years. Thus, it is urgent to investigate the molecular mechanism of GBM. Recently, studies have demonstrat...
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Published in: | Journal of cellular biochemistry 2020-01, Vol.121 (1), p.755-767 |
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description | Glioblastoma (GBM) has become the most aggressive primary brain tumor in the world. Patients with GBM usually have a poor prognosis. The median survival times of GBM patients retain less than 2 years. Thus, it is urgent to investigate the molecular mechanism of GBM. Recently, studies have demonstrated that transcription factors (TFs) participate in cancer pathology by regulating long noncoding RNAs (lncRNAs). However, the functional and regulatory roles of TF‐lncRNA crosstalks are still unclear. In this study, we constructed a global lncRNA‐TF network (GLTN) based on competing endogenous RNA. As a result, some topological features of GLTN were identified. A known GBM lncRNA MCM3AP‐AS1 showed multiple central topological features in GLTN. Furthermore, we identified hub genes and extracted the hub‐hub pairs from GLTN to form a hub associated lncRNA‐TF network (HALTN). Results showed that a risk model combined with multiple hubs had a significant effect on prognosis. Additionally, we performed module searching and two functional modules from HALTN were identified, which were confirmed as risk factors of GBM. More importantly, we also identified some core lncRNA‐TF crosstalks that might form feedback loops to control the biological processes in GBM. Our results demonstrated that the synergistic, competitive lncRNA‐TF crosstalks played an important role in pathological processes of GBM, and had strong effect on prognosis. All these results can help us to uncover the molecular mechanism and provide a new therapeutic target for GBM.
1.
A global view of long noncoding RNA transcription factor (lncRNA‐TF) crosstalks was constructed for glioblastoma (GBM) investigation.
2.
Integration of competing endogenous RNA (ceRNA) crosstalks and TF‐DNA binding, some core lncRNA‐TF feedback loops with strong prognostic effect were identified. |
doi_str_mv | 10.1002/jcb.29321 |
format | article |
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1.
A global view of long noncoding RNA transcription factor (lncRNA‐TF) crosstalks was constructed for glioblastoma (GBM) investigation.
2.
Integration of competing endogenous RNA (ceRNA) crosstalks and TF‐DNA binding, some core lncRNA‐TF feedback loops with strong prognostic effect were identified.</description><identifier>ISSN: 0730-2312</identifier><identifier>EISSN: 1097-4644</identifier><identifier>DOI: 10.1002/jcb.29321</identifier><identifier>PMID: 31478223</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Biological activity ; Biomarkers, Tumor - genetics ; Biomarkers, Tumor - metabolism ; Brain cancer ; Brain tumors ; ceRNA networks ; Control theory ; core lncRNA‐TF crosstalks ; Feature extraction ; Feedback ; Feedback loops ; Feedback, Physiological ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Glioblastoma ; Glioblastoma - genetics ; Glioblastoma - metabolism ; Glioblastoma - pathology ; Humans ; Medical prognosis ; MicroRNAs - genetics ; MicroRNAs - metabolism ; Modules ; Prognosis ; Ribonucleic acid ; Risk analysis ; Risk factors ; RNA ; RNA, Long Noncoding - genetics ; RNA, Long Noncoding - metabolism ; survival biomarkers ; Survival Rate ; Therapeutic applications ; Topology ; Transcription factors ; Transcription Factors - genetics ; Transcription Factors - metabolism</subject><ispartof>Journal of cellular biochemistry, 2020-01, Vol.121 (1), p.755-767</ispartof><rights>2019 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3881-25215a858e799a4279ef6dc4d7e31f32eed951e9eb502a9708efa7e8f0e2884f3</citedby><cites>FETCH-LOGICAL-c3881-25215a858e799a4279ef6dc4d7e31f32eed951e9eb502a9708efa7e8f0e2884f3</cites><orcidid>0000-0001-8299-5723</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31478223$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ji, Yang</creatorcontrib><creatorcontrib>Gu, Yaqin</creatorcontrib><creatorcontrib>Hong, Shuai</creatorcontrib><creatorcontrib>Yu, Bo</creatorcontrib><creatorcontrib>Zhang, Jian‐Hua</creatorcontrib><creatorcontrib>Liu, Jin‐Na</creatorcontrib><title>Comprehensive analysis of lncRNA‐TF crosstalks and identification of prognostic regulatory feedback loops of glioblastoma using lncRNA/TF‐mediated ceRNA network</title><title>Journal of cellular biochemistry</title><addtitle>J Cell Biochem</addtitle><description>Glioblastoma (GBM) has become the most aggressive primary brain tumor in the world. Patients with GBM usually have a poor prognosis. The median survival times of GBM patients retain less than 2 years. Thus, it is urgent to investigate the molecular mechanism of GBM. Recently, studies have demonstrated that transcription factors (TFs) participate in cancer pathology by regulating long noncoding RNAs (lncRNAs). However, the functional and regulatory roles of TF‐lncRNA crosstalks are still unclear. In this study, we constructed a global lncRNA‐TF network (GLTN) based on competing endogenous RNA. As a result, some topological features of GLTN were identified. A known GBM lncRNA MCM3AP‐AS1 showed multiple central topological features in GLTN. Furthermore, we identified hub genes and extracted the hub‐hub pairs from GLTN to form a hub associated lncRNA‐TF network (HALTN). Results showed that a risk model combined with multiple hubs had a significant effect on prognosis. Additionally, we performed module searching and two functional modules from HALTN were identified, which were confirmed as risk factors of GBM. More importantly, we also identified some core lncRNA‐TF crosstalks that might form feedback loops to control the biological processes in GBM. Our results demonstrated that the synergistic, competitive lncRNA‐TF crosstalks played an important role in pathological processes of GBM, and had strong effect on prognosis. All these results can help us to uncover the molecular mechanism and provide a new therapeutic target for GBM.
1.
A global view of long noncoding RNA transcription factor (lncRNA‐TF) crosstalks was constructed for glioblastoma (GBM) investigation.
2.
Integration of competing endogenous RNA (ceRNA) crosstalks and TF‐DNA binding, some core lncRNA‐TF feedback loops with strong prognostic effect were identified.</description><subject>Biological activity</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Brain cancer</subject><subject>Brain tumors</subject><subject>ceRNA networks</subject><subject>Control theory</subject><subject>core lncRNA‐TF crosstalks</subject><subject>Feature extraction</subject><subject>Feedback</subject><subject>Feedback loops</subject><subject>Feedback, Physiological</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Gene Regulatory Networks</subject><subject>Glioblastoma</subject><subject>Glioblastoma - genetics</subject><subject>Glioblastoma - metabolism</subject><subject>Glioblastoma - pathology</subject><subject>Humans</subject><subject>Medical prognosis</subject><subject>MicroRNAs - genetics</subject><subject>MicroRNAs - metabolism</subject><subject>Modules</subject><subject>Prognosis</subject><subject>Ribonucleic acid</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>RNA</subject><subject>RNA, Long Noncoding - genetics</subject><subject>RNA, Long Noncoding - metabolism</subject><subject>survival biomarkers</subject><subject>Survival Rate</subject><subject>Therapeutic applications</subject><subject>Topology</subject><subject>Transcription factors</subject><subject>Transcription Factors - genetics</subject><subject>Transcription Factors - metabolism</subject><issn>0730-2312</issn><issn>1097-4644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp10cuO0zAUBmALgZgysOAFkCU2sMjUl6S2l0NFuWgEEirryHGOi1snLrbDqDsegYfgyXgSPG1hgcTKkvXpP_b5EXpKyRUlhM23prtiijN6D80oUaKqF3V9H82I4KRinLIL9CilLSFEFfUQXXBaC8kYn6GfyzDsI3yBMblvgPWo_SG5hIPFfjSfPlz_-v5jvcImhpSy9rtUSI9dD2N21hmdXRjv8D6GzRhSdgZH2Exe5xAP2AL0nTY77EPYH0M33oXO65TDoPGU3Lg5z5mvV2XUAL3TGXpsoNzhEfJtiLvH6IHVPsGT83mJPq9er5dvq5uPb94tr28qw6WkFWsYbbRsJAildM2EArvoTd0L4NRyVh6jGgoKuoYwrQSRYLUAaQkwKWvLL9GLU275zdcJUm4Hlwx4r0cIU2oZk1wVKWShz_-h2zDFsr2iOBUNk2rBi3p5Usf9RbDtPrpBx0NLSXtXXVuqa4_VFfvsnDh1ZQ1_5Z-uCpifwK3zcPh_Uvt--eoU-Rs-s6by</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Ji, Yang</creator><creator>Gu, Yaqin</creator><creator>Hong, Shuai</creator><creator>Yu, Bo</creator><creator>Zhang, Jian‐Hua</creator><creator>Liu, Jin‐Na</creator><general>Wiley Subscription Services, Inc</general><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>7QP</scope><scope>7QR</scope><scope>7T7</scope><scope>7TK</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8299-5723</orcidid></search><sort><creationdate>202001</creationdate><title>Comprehensive analysis of lncRNA‐TF crosstalks and identification of prognostic regulatory feedback loops of glioblastoma using lncRNA/TF‐mediated ceRNA network</title><author>Ji, Yang ; Gu, Yaqin ; Hong, Shuai ; Yu, Bo ; Zhang, Jian‐Hua ; Liu, Jin‐Na</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3881-25215a858e799a4279ef6dc4d7e31f32eed951e9eb502a9708efa7e8f0e2884f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biological activity</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Biomarkers, Tumor - metabolism</topic><topic>Brain cancer</topic><topic>Brain tumors</topic><topic>ceRNA networks</topic><topic>Control theory</topic><topic>core lncRNA‐TF crosstalks</topic><topic>Feature extraction</topic><topic>Feedback</topic><topic>Feedback loops</topic><topic>Feedback, Physiological</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Gene Regulatory Networks</topic><topic>Glioblastoma</topic><topic>Glioblastoma - genetics</topic><topic>Glioblastoma - metabolism</topic><topic>Glioblastoma - pathology</topic><topic>Humans</topic><topic>Medical prognosis</topic><topic>MicroRNAs - genetics</topic><topic>MicroRNAs - metabolism</topic><topic>Modules</topic><topic>Prognosis</topic><topic>Ribonucleic acid</topic><topic>Risk analysis</topic><topic>Risk factors</topic><topic>RNA</topic><topic>RNA, Long Noncoding - genetics</topic><topic>RNA, Long Noncoding - metabolism</topic><topic>survival biomarkers</topic><topic>Survival Rate</topic><topic>Therapeutic applications</topic><topic>Topology</topic><topic>Transcription factors</topic><topic>Transcription Factors - genetics</topic><topic>Transcription Factors - metabolism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ji, Yang</creatorcontrib><creatorcontrib>Gu, Yaqin</creatorcontrib><creatorcontrib>Hong, Shuai</creatorcontrib><creatorcontrib>Yu, Bo</creatorcontrib><creatorcontrib>Zhang, Jian‐Hua</creatorcontrib><creatorcontrib>Liu, Jin‐Na</creatorcontrib><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>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of cellular biochemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ji, Yang</au><au>Gu, Yaqin</au><au>Hong, Shuai</au><au>Yu, Bo</au><au>Zhang, Jian‐Hua</au><au>Liu, Jin‐Na</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comprehensive analysis of lncRNA‐TF crosstalks and identification of prognostic regulatory feedback loops of glioblastoma using lncRNA/TF‐mediated ceRNA network</atitle><jtitle>Journal of cellular biochemistry</jtitle><addtitle>J Cell Biochem</addtitle><date>2020-01</date><risdate>2020</risdate><volume>121</volume><issue>1</issue><spage>755</spage><epage>767</epage><pages>755-767</pages><issn>0730-2312</issn><eissn>1097-4644</eissn><abstract>Glioblastoma (GBM) has become the most aggressive primary brain tumor in the world. Patients with GBM usually have a poor prognosis. The median survival times of GBM patients retain less than 2 years. Thus, it is urgent to investigate the molecular mechanism of GBM. Recently, studies have demonstrated that transcription factors (TFs) participate in cancer pathology by regulating long noncoding RNAs (lncRNAs). However, the functional and regulatory roles of TF‐lncRNA crosstalks are still unclear. In this study, we constructed a global lncRNA‐TF network (GLTN) based on competing endogenous RNA. As a result, some topological features of GLTN were identified. A known GBM lncRNA MCM3AP‐AS1 showed multiple central topological features in GLTN. Furthermore, we identified hub genes and extracted the hub‐hub pairs from GLTN to form a hub associated lncRNA‐TF network (HALTN). Results showed that a risk model combined with multiple hubs had a significant effect on prognosis. Additionally, we performed module searching and two functional modules from HALTN were identified, which were confirmed as risk factors of GBM. More importantly, we also identified some core lncRNA‐TF crosstalks that might form feedback loops to control the biological processes in GBM. Our results demonstrated that the synergistic, competitive lncRNA‐TF crosstalks played an important role in pathological processes of GBM, and had strong effect on prognosis. All these results can help us to uncover the molecular mechanism and provide a new therapeutic target for GBM.
1.
A global view of long noncoding RNA transcription factor (lncRNA‐TF) crosstalks was constructed for glioblastoma (GBM) investigation.
2.
Integration of competing endogenous RNA (ceRNA) crosstalks and TF‐DNA binding, some core lncRNA‐TF feedback loops with strong prognostic effect were identified.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>31478223</pmid><doi>10.1002/jcb.29321</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-8299-5723</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Biological activity Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Brain cancer Brain tumors ceRNA networks Control theory core lncRNA‐TF crosstalks Feature extraction Feedback Feedback loops Feedback, Physiological Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Glioblastoma Glioblastoma - genetics Glioblastoma - metabolism Glioblastoma - pathology Humans Medical prognosis MicroRNAs - genetics MicroRNAs - metabolism Modules Prognosis Ribonucleic acid Risk analysis Risk factors RNA RNA, Long Noncoding - genetics RNA, Long Noncoding - metabolism survival biomarkers Survival Rate Therapeutic applications Topology Transcription factors Transcription Factors - genetics Transcription Factors - metabolism |
title | Comprehensive analysis of lncRNA‐TF crosstalks and identification of prognostic regulatory feedback loops of glioblastoma using lncRNA/TF‐mediated ceRNA network |
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