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

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, incorpo-rating hypothesis-driven, data-driven, and literature-based methods with informa-tive visualization to prioritize candidate genes. By applying the proposed ap-proaches 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 stan-dard gene panel selection methods. The proposed framework is general and can be applied to any type of omics data. Overall, the study demonstrates and sup-ports the use of the subnetwork-based approach for distilling reliable and biolog-ically meaningful prognostic factors.

Automated summary

This study presents a subnetwork-based framework that combines hypothesis-driven, data-driven, and literature-based methods with informative visualization to prioritize candidate genes for cancer prognosis prediction. By applying this framework to a head and neck squamous cell cancer (HNSCC) transcriptome dataset, multiple HNSCC-specific gene modules with improved prognostic values and mechanism information were successfully identified compared to standard gene panel selection methods. The proposed framework is general and can be applied to any type of omics data.