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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Bibliographic Details
Published in:PloS one 2013-12, Vol.8 (12), p.e82241-e82241
Main Authors: Győrffy, Balázs, Surowiak, Pawel, Budczies, Jan, Lánczky, András
Format: Article
Language:English
Subjects:
Arrays
Bioindicators
Biological markers
Biomarkers
Cancer
Carcinoma, Non-Small-Cell Lung - genetics
Cell survival
Datasets
DNA microarrays
ERCC1 protein
Gene expression
Genes
Genomes
Histology
Humans
Internet
Kaplan-Meier Estimate
Laboratories
Lung cancer
Lung cancer, Non-small cell
Lung diseases
Lung Neoplasms - genetics
Medical prognosis
Meta-analysis
Middleware
Multivariate Analysis
Nephrology
Non-small cell lung carcinoma
Pediatrics
Prognosis
Proportional Hazards Models
Regression analysis
Repositories
Small cell lung carcinoma
Software
Squamous cell carcinoma
Studies
Survival
Survival analysis
Transcriptome - genetics
Vascular endothelial growth factor
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Summary: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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0082241