A subnetwork-based framework for prioritizing and evaluating prognostic gene modules from cancer transcriptome data.

ABSTRACT: Cancer prognosis prediction is critical to the clinical decision-making process. Currently, the high availability of transcriptome datasets allows us to extract the gene modules with promising prognostic values. However, the biomarker identification is greatly challenged by tumor and patient heterogeneity. In this study, a framework of three subnetwork-based strategies is presented, incorporating hypothesis-driven, data-driven, and literature-based methods with informative visualization to prioritize candidate genes. By applying the proposed approaches to a head and neck squamous cell cancer (HNSCC) transcriptome dataset, we successfully identified multiple HNSCC-specific gene modules with improved prognostic values and mechanism information compared with the standard gene panel selection

SUBMITTER: Cao B 

PROVIDER: S-EPMC9845797 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature