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OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes

Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribu...

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Autores principales: Agapito, Giuseppe, Botta, Cirino, Guzzi, Pietro Hiram, Arbitrio, Mariamena, Di Martino, Maria Teresa, Tassone, Pierfrancesco, Tagliaferri, Pierosandro, Cannataro, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5197943/
https://www.ncbi.nlm.nih.gov/pubmed/27669316
http://dx.doi.org/10.3390/microarrays5040024
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author Agapito, Giuseppe
Botta, Cirino
Guzzi, Pietro Hiram
Arbitrio, Mariamena
Di Martino, Maria Teresa
Tassone, Pierfrancesco
Tagliaferri, Pierosandro
Cannataro, Mario
author_facet Agapito, Giuseppe
Botta, Cirino
Guzzi, Pietro Hiram
Arbitrio, Mariamena
Di Martino, Maria Teresa
Tassone, Pierfrancesco
Tagliaferri, Pierosandro
Cannataro, Mario
author_sort Agapito, Giuseppe
collection PubMed
description Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients.
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spelling pubmed-51979432017-01-04 OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes Agapito, Giuseppe Botta, Cirino Guzzi, Pietro Hiram Arbitrio, Mariamena Di Martino, Maria Teresa Tassone, Pierfrancesco Tagliaferri, Pierosandro Cannataro, Mario Microarrays (Basel) Article Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients. MDPI 2016-09-23 /pmc/articles/PMC5197943/ /pubmed/27669316 http://dx.doi.org/10.3390/microarrays5040024 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Agapito, Giuseppe
Botta, Cirino
Guzzi, Pietro Hiram
Arbitrio, Mariamena
Di Martino, Maria Teresa
Tassone, Pierfrancesco
Tagliaferri, Pierosandro
Cannataro, Mario
OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
title OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
title_full OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
title_fullStr OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
title_full_unstemmed OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
title_short OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
title_sort osanalyzer: a bioinformatics tool for the analysis of gene polymorphisms enriched with clinical outcomes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5197943/
https://www.ncbi.nlm.nih.gov/pubmed/27669316
http://dx.doi.org/10.3390/microarrays5040024
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