Identification of a six-gene signature associated with tumor mutation burden for predicting prognosis in patients with invasive breast carcinoma.

ABSTRACT:

Background

Breast cancer (BC) is one of the most common cancers with high mortality worldwide. In the present study, through bioinformatics analysis, we aimed to identify new biomarkers to predict the survival rate of BC patients.

Methods

Differentially expressed genes (DEGs) between low- and high-tumor mutation burden (TMB) groups were identified by using The Cancer Genome Atlas (TCGA) dataset and integrated analysis. Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and the protein-protein interaction (PPI) network, were applied to predict the function of these above DEGs. Then, the Cox proportional hazard model was developed to screen DEGs. Based on the prognostic signature, survival analysis was used on The Cancer Genome Atlas Breast

SUBMITTER: Wang F 

PROVIDER: S-EPMC7210212 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature