Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape.

ABSTRACT: The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor stat

SUBMITTER: Yoon S 

PROVIDER: S-EPMC7281553 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature