JCDSA: a joint covariate detection tool for survival analysis on tumor expression profiles

Survival analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is crucial how to select features which closely correspond to survival time. Furthermore, it is important how to select features which best discriminate between low-risk a...

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Bibliographic Details
Published in:BMC bioinformatics 2018-05, Vol.19 (1), p.187-187, Article 187
Main Authors: Wu, Yiming, Liu, Yanan, Wang, Yueming, Shi, Yan, Zhao, Xudong
Format: Article
Language:English
Subjects:
Analysis
Brain cancer
Brain Neoplasms - genetics
Brain Neoplasms - pathology
Cancer
Cancer research
Colleges & universities
Covariance analysis
Expression profiles
Feature selection
Gene expression
Glioblastoma
Glioblastoma - genetics
Glioblastoma - pathology
Health risk assessment
Humans
MicroRNAs - genetics
miRNA
Patients
Personal computers
Prognosis
Regression analysis
Risk factors
Risk groups
Software
Survival
Survival Analysis
Transcriptome - genetics
Tumors
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Summary:Survival analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is crucial how to select features which closely correspond to survival time. Furthermore, it is important how to select features which best discriminate between low-risk and high-risk group of patients. Common features derived from the two aspects may provide variable candidates for prognosis of cancer. Based on the provided two-step feature selection strategy, we develop a joint covariate detection tool for survival analysis on tumor expression profiles. Significant features, which are not only consistent with survival time but also associated with the categories of patients with different survival risks, are chosen. Using the miRNA expression data (Level 3) of 548 patients with glioblastoma multiforme (GBM) as an example, miRNA candidates for prognosis of cancer are selected. The reliability of selected miRNAs using this tool is demonstrated by 100 simulations. Furthermore, It is discovered that significant covariates are not directly composed of individually significant variables. Joint covariate detection provides a viewpoint for selecting variables which are not individually but jointly significant. Besides, it helps to select features which are not only consistent with survival time but also associated with prognosis risk. The software is available at http://bio-nefu.com/resource/jcdsa .
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2213-3