Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer
By Balázs Győrffy, P. Surowiak, Jan Budczies, A. Lánczky. PLoS ONE, 2013
1,628 citations recorded by the source corpus.
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.
Subfields: Molecular Biology, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/9eff81fbfd6aed745ef1552a9c8f10b61c8cf144
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Citations in the corpus
- OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs (2016, background): “Multiple resources exist to help researchers download or explore TCGA data (Cerami et al. 2012; Gyorffy et al. 2013; Koch et al. 2015).”
- METTL14 suppresses the metastatic potential of hepatocellular carcinoma by modulating N6‐methyladenosine‐dependent primary MicroRNA processing (2017, methodology): “Using the online bioinformatics tool Kaplan-Meier plotter,((22)) we found that patients with reduced METTL14 expression had worse recurrence-free survival (RFS) and overall survival (OS) (Fig.”
- Single cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species (2019, methodology): “—Kaplan-Meier plots and univariate cox regression pvalues were generated using the database and tools described in (Gyorffy et al., 2013), using the associated online tool kmplot.”
- Cross-validation of survival associated biomarkers in gastric cancer using transcriptomic data of 1,065 patients (2016, methodology): “Quality control for gene chips and control for duplicate samples were performed as described previously [39].”