An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis
The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate...
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| Veröffentlicht in: | Journal of cellular and molecular medicine 2020-02, Vol.24 (3), p.2342-2355 |
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| creator | Xiong, Yaoyi Yuan, Lushun Xiong, Jing Xu, Huimin Luo, Yongwen Wang, Gang Ju, Lingao Xiao, Yu Wang, Xinghuan |
| description | The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making. |
| doi_str_mv | 10.1111/jcmm.14918 |
| format | Article |
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Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.</description><identifier>ISSN: 1582-1838</identifier><identifier>EISSN: 1582-4934</identifier><identifier>DOI: 10.1111/jcmm.14918</identifier><identifier>PMID: 31883309</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>Accuracy ; Bioinformatics ; Biomarkers ; Bladder cancer ; Cancer ; Computational Biology - methods ; Datasets ; Decision making ; Disease Progression ; Gene expression ; Gene Expression Omnibus (GEO) ; Gene Expression Profiling - methods ; Gene Expression Regulation, Neoplastic - genetics ; Humans ; LASSO ; Medical prognosis ; Multivariate Analysis ; Nomograms ; Original ; Patients ; Performance evaluation ; Prognosis ; Proportional Hazards Models ; Regression Analysis ; Researchers ; Software ; Survival analysis ; Urinary Bladder Neoplasms - genetics ; Urinary Bladder Neoplasms - pathology ; WGCNA</subject><ispartof>Journal of cellular and molecular medicine, 2020-02, Vol.24 (3), p.2342-2355</ispartof><rights>2019 The Authors. published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.</rights><rights>2019 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.</rights><rights>2020. 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-c4488-7f97935fd4727cf1bc563405bfecc2d224770d2fb08c6d3a12efa080c7ad87143</citedby><cites>FETCH-LOGICAL-c4488-7f97935fd4727cf1bc563405bfecc2d224770d2fb08c6d3a12efa080c7ad87143</cites><orcidid>0000-0003-1377-9685 ; 0000-0003-3497-0024</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/PMC7011142/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011142/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1411,11542,27903,27904,45553,45554,46030,46454,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31883309$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xiong, Yaoyi</creatorcontrib><creatorcontrib>Yuan, Lushun</creatorcontrib><creatorcontrib>Xiong, Jing</creatorcontrib><creatorcontrib>Xu, Huimin</creatorcontrib><creatorcontrib>Luo, Yongwen</creatorcontrib><creatorcontrib>Wang, Gang</creatorcontrib><creatorcontrib>Ju, Lingao</creatorcontrib><creatorcontrib>Xiao, Yu</creatorcontrib><creatorcontrib>Wang, Xinghuan</creatorcontrib><title>An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis</title><title>Journal of cellular and molecular medicine</title><addtitle>J Cell Mol Med</addtitle><description>The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.</description><subject>Accuracy</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Bladder cancer</subject><subject>Cancer</subject><subject>Computational Biology - methods</subject><subject>Datasets</subject><subject>Decision making</subject><subject>Disease Progression</subject><subject>Gene expression</subject><subject>Gene Expression Omnibus (GEO)</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Expression Regulation, Neoplastic - genetics</subject><subject>Humans</subject><subject>LASSO</subject><subject>Medical prognosis</subject><subject>Multivariate Analysis</subject><subject>Nomograms</subject><subject>Original</subject><subject>Patients</subject><subject>Performance evaluation</subject><subject>Prognosis</subject><subject>Proportional Hazards Models</subject><subject>Regression Analysis</subject><subject>Researchers</subject><subject>Software</subject><subject>Survival analysis</subject><subject>Urinary Bladder Neoplasms - 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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>Xiong, Yaoyi</au><au>Yuan, Lushun</au><au>Xiong, Jing</au><au>Xu, Huimin</au><au>Luo, Yongwen</au><au>Wang, Gang</au><au>Ju, Lingao</au><au>Xiao, Yu</au><au>Wang, Xinghuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis</atitle><jtitle>Journal of cellular and molecular medicine</jtitle><addtitle>J Cell Mol Med</addtitle><date>2020-02</date><risdate>2020</risdate><volume>24</volume><issue>3</issue><spage>2342</spage><epage>2355</epage><pages>2342-2355</pages><issn>1582-1838</issn><eissn>1582-4934</eissn><abstract>The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>31883309</pmid><doi>10.1111/jcmm.14918</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-1377-9685</orcidid><orcidid>https://orcid.org/0000-0003-3497-0024</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Bioinformatics Biomarkers Bladder cancer Cancer Computational Biology - methods Datasets Decision making Disease Progression Gene expression Gene Expression Omnibus (GEO) Gene Expression Profiling - methods Gene Expression Regulation, Neoplastic - genetics Humans LASSO Medical prognosis Multivariate Analysis Nomograms Original Patients Performance evaluation Prognosis Proportional Hazards Models Regression Analysis Researchers Software Survival analysis Urinary Bladder Neoplasms - genetics Urinary Bladder Neoplasms - pathology WGCNA |
| title | An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis |
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