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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessArticle
Microarrays 2016, 5(4), 24; doi:10.3390/microarrays5040024

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

1
Department of Medical and Surgical Science, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
2
Department of Experimental Medicine and Clinic, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
3
ISN-CNR, Roccelletta di Borgia, Catanzaro 88100, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Massimo Negrini
Received: 13 July 2016 / Revised: 27 August 2016 / Accepted: 19 September 2016 / Published: 23 September 2016
(This article belongs to the Special Issue Next Generation Microarray Bioinformatics)
View Full-Text   |   Download PDF [3857 KB, uploaded 23 September 2016]   |  

Abstract

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. View Full-Text
Keywords: genotyping microarrays; ADME genes; pharmacogenomics; overall survival; progression-free survival genotyping microarrays; ADME genes; pharmacogenomics; overall survival; progression-free survival
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MDPI and ACS Style

Agapito, G.; Botta, C.; Guzzi, P.H.; Arbitrio, M.; Di Martino, M.T.; Tassone, P.; Tagliaferri, P.; Cannataro, M. OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes. Microarrays 2016, 5, 24.

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