Identification and evaluation of a risk model predicting the prognosis of breast cancer based on characteristic signatures.

ABSTRACT:

Background

Breast cancer (BC) is one of the most common fatal cancers in women. Identifying new biomarkers is thus of great significance for the diagnosis and prognosis of BC.

Methods

In this study, 1,030 BC cases from The Cancer Genome Atlas (TCGA) were obtained for differential expression analysis and Short Time-series Expression Miner (STEM) analysis to identify characteristic BC development genes, which were further divided into upregulated and downregulated genes. Two predictive prognosis models were both defined by Least Absolute Shrinkage and Selection Operator (LASSO). Survival analysis and receiver operating characteristic (ROC) curve analysis were used to determine the diagnostic and prognostic capabilities of the two gene set model scores, respectively.

Results

SUBMITTER: Ying Y 

PROVIDER: S-EPMC10331456 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature