Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an onli...

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Autores principales:	Balázs Győrffy, Pawel Surowiak, Jan Budczies, András Lánczky
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fd00024cd6c541ef84464a10f4ac5b54
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spelling	oai:doaj.org-article:fd00024cd6c541ef84464a10f4ac5b542021-11-18T08:41:25ZOnline survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.1932-620310.1371/journal.pone.0082241https://doaj.org/article/fd00024cd6c541ef84464a10f4ac5b542013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24367507/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.Balázs GyőrffyPawel SurowiakJan BudcziesAndrás LánczkyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82241 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Balázs Győrffy Pawel Surowiak Jan Budczies András Lánczky Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.
description	In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.
format	article
author	Balázs Győrffy Pawel Surowiak Jan Budczies András Lánczky
author_facet	Balázs Győrffy Pawel Surowiak Jan Budczies András Lánczky
author_sort	Balázs Győrffy
title	Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.
title_short	Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.
title_full	Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.
title_fullStr	Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.
title_full_unstemmed	Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.
title_sort	online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/fd00024cd6c541ef84464a10f4ac5b54
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Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.

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