A novel metabolic gene signature-based nomogram to predict overall survival in breast cancer.

ABSTRACT:

Background

Breast cancer risk prediction is often based on clinicopathological characteristics despite the high heterogeneity derived from gene expression. Metabolic alteration is a hallmark of cancer, and thus, the integration of a metabolic signature with clinical parameters is necessary to predict disease outcomes in breast cancers.

Methods

Metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Genes with statistical significance in the univariate analysis were applied in the least absolute shrinkage and selection operator (LASSO) analysis to build a gene signature in the GSE20685 dataset. Clinicopathological characteristics and risk scores with prognostic significance were incorporated into the nomogram to predict the overall survival (OS)

SUBMITTER: Sun X 

PROVIDER: S-EPMC8033348 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature