Survival analysis tools in genomics research.

Xiaochen Sun,Yujin Hoshida,Xintong Chen

doi:10.1186/s40246-014-0021-z

Xiaochen Sun, Yujin Hoshida + Show 1 more

Open Access

https://doi.org/10.1186/s40246-014-0021-z

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Journal: Human genomics	Publication Date: Nov 25, 2014
Citations: 17	License type: CC BY 4.0

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract

There is an increasing demand to determine the clinical implication of experimental findings in molecular biomedical research. Survival (or failure time) analysis methodologies have been adapted to the analysis of genomics data to link molecular information with clinical outcomes of interest. Genome-wide molecular profiles have served as sources for discovery of predictive/prognostic biomarkers as well as therapeutic targets in the past decade. In this review, we overview currently available software, web applications, and databases specifically developed for survival analysis in genomics research and discuss issues in assessing clinical utility of molecular features derived from genomic profiling.

Highlights

There is an increasing demand to determine the clinical implication of experimental findings in molecular biomedical research
The major interest is the association of genomic features with clinical outcomes, including response to certain treatment and prognosis of the patients under specific clinical scenarios
Clinical outcome especially prognosis is often presented as the time period between the start and end of the clinical observation in combination with a binary status information, indicating whether or not each patient had a clinical event of interest, e.g., death, cancer recurrence, and drug response

Summary

Introduction

There is an increasing demand to determine the clinical implication of experimental findings in molecular biomedical research. Expanding clinical contexts covered by the studies/datasets has enabled exploration of clinically more relevant predictive/prognostic biomarkers from genomic data [1]. The accumulated genomic datasets with clinical outcome information have led to a new paradigm of biomarker research, i.e., in silico discovery and/or validation of predictive/prognostic molecular biomarkers.

Results

Conclusion