Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer

Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical l...

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Bibliographic Details
Published in:Briefings in bioinformatics 2016-03, Vol.17 (2), p.233-242
Main Authors: Qi, Lishuang, Chen, Libin, Li, Yang, Qin, Yuan, Pan, Rufei, Zhao, Wenyuan, Gu, Yunyan, Wang, Hongwei, Wang, Ruiping, Chen, Xiangqi, Guo, Zheng
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Biomarkers, Tumor - genetics
Cancer
Carcinoma, Non-Small-Cell Lung - genetics
Carcinoma, Non-Small-Cell Lung - mortality
Carcinoma, Non-Small-Cell Lung - surgery
Cells
Gene expression
Gene Expression Profiling - methods
Genes
Genetic Predisposition to Disease - epidemiology
Genetic Predisposition to Disease - genetics
Humans
Lung cancer
Lung Neoplasms - pathology
Lung Neoplasms - surgery
Lungs
Neoplasm Staging
Prevalence
Prognosis
Reproducibility of Results
Risk
Risk Assessment - methods
Sensitivity and Specificity
Signatures
Survival
Survival Analysis
Treatment Outcome
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Summary:Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbv064