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Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types

Microarray technology has been widely applied to the analysis of many malignancies, however, integrative analyses across multiple studies are rarely investigated. In this study we performed a meta-analysis on the expression profiles of four published studies analyzing organ donor, benign tissues adj...

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Autores principales: Tseng, George C., Cheng, Chunrong, Yu, Yan Ping, Nelson, Joel, Michalopoulos, George, Luo, Jian-Hua
Formato: Texto
Lenguaje:English
Publicado: Libertas Academica 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2716681/
https://www.ncbi.nlm.nih.gov/pubmed/19652763
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author Tseng, George C.
Cheng, Chunrong
Yu, Yan Ping
Nelson, Joel
Michalopoulos, George
Luo, Jian-Hua
author_facet Tseng, George C.
Cheng, Chunrong
Yu, Yan Ping
Nelson, Joel
Michalopoulos, George
Luo, Jian-Hua
author_sort Tseng, George C.
collection PubMed
description Microarray technology has been widely applied to the analysis of many malignancies, however, integrative analyses across multiple studies are rarely investigated. In this study we performed a meta-analysis on the expression profiles of four published studies analyzing organ donor, benign tissues adjacent to tumor and tumor tissues from liver, prostate, lung and bladder samples. We identified 99 distinct multi-cancer biomarkers in the comparison of all three tissues in liver and prostate and 44 in the comparison of normal versus tumor in liver, prostate and lung. The bladder samples appeared to have a different list of biomarkers from the other three cancer types. The identified multi-cancer biomarkers achieved high accuracy similar to using whole genome in the within-cancer-type prediction. They also performed superior than the one using whole genome in inter-cancer-type prediction. To test the validity of the multi-cancer biomarkers, 23 independent prostate cancer samples were evaluated and 96% accuracy was achieved in inter-study prediction from the original prostate, liver and lung cancer data sets respectively. The result suggests that the compact lists of multi-cancer biomarkers are important in cancer development and represent the common signatures of malignancies of multiple cancer types. Pathway analysis revealed important tumorogenesis functional categories.
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spelling pubmed-27166812009-08-03 Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types Tseng, George C. Cheng, Chunrong Yu, Yan Ping Nelson, Joel Michalopoulos, George Luo, Jian-Hua Biomark Insights Original Research Microarray technology has been widely applied to the analysis of many malignancies, however, integrative analyses across multiple studies are rarely investigated. In this study we performed a meta-analysis on the expression profiles of four published studies analyzing organ donor, benign tissues adjacent to tumor and tumor tissues from liver, prostate, lung and bladder samples. We identified 99 distinct multi-cancer biomarkers in the comparison of all three tissues in liver and prostate and 44 in the comparison of normal versus tumor in liver, prostate and lung. The bladder samples appeared to have a different list of biomarkers from the other three cancer types. The identified multi-cancer biomarkers achieved high accuracy similar to using whole genome in the within-cancer-type prediction. They also performed superior than the one using whole genome in inter-cancer-type prediction. To test the validity of the multi-cancer biomarkers, 23 independent prostate cancer samples were evaluated and 96% accuracy was achieved in inter-study prediction from the original prostate, liver and lung cancer data sets respectively. The result suggests that the compact lists of multi-cancer biomarkers are important in cancer development and represent the common signatures of malignancies of multiple cancer types. Pathway analysis revealed important tumorogenesis functional categories. Libertas Academica 2009-05-01 /pmc/articles/PMC2716681/ /pubmed/19652763 Text en © 2009 by the authors http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Original Research
Tseng, George C.
Cheng, Chunrong
Yu, Yan Ping
Nelson, Joel
Michalopoulos, George
Luo, Jian-Hua
Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types
title Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types
title_full Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types
title_fullStr Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types
title_full_unstemmed Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types
title_short Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types
title_sort investigating multi-cancer biomarkers and their cross-predictability in the expression profiles of multiple cancer types
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2716681/
https://www.ncbi.nlm.nih.gov/pubmed/19652763
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