Comparison of pathway and gene-level models for cancer prognosis prediction.

ABSTRACT:

Background

Cancer prognosis prediction is valuable for patients and clinicians because it allows them to appropriately manage care. A promising direction for improving the performance and interpretation of expression-based predictive models involves the aggregation of gene-level data into biological pathways. While many studies have used pathway-level predictors for cancer survival analysis, a comprehensive comparison of pathway-level and gene-level prognostic models has not been performed. To address this gap, we characterized the performance of penalized Cox proportional hazard models built using either pathway- or gene-level predictors for the cancers profiled in The Cancer Genome Atlas (TCGA) and pathways from the Molecular Signatures Database (MSigDB).

Results

When anal

SUBMITTER: Zheng X 

PROVIDER: S-EPMC7048092 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature