Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model.

ABSTRACT:

Background

Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models.

Methods

A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net wer

SUBMITTER: Tapak L 

PROVIDER: S-EPMC10034277 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature