Prognostic Biomarkers in Breast Cancer via Multi-Omics Clustering Analysis.

ABSTRACT: Breast cancer (BC) is a highly heterogeneous disease with diverse molecular subtypes, which complicates prognosis and treatment. In this study, we performed a multi-omics clustering analysis using the Cancer Integration via MultIkernel LeaRning (CIMLR) method on a large BC dataset from The Cancer Genome Atlas (TCGA) to identify key prognostic biomarkers. We identified three genes-LMO1, PRAME, and RSPO2-that were significantly associated with poor prognosis in both the TCGA dataset and an additional dataset comprising 146 metastatic BC patients. Patients' stratification based on the expression of these three genes revealed distinct subtypes with markedly different overall survival (OS) outcomes. Further validation using almost 2000 BC patients' data from the METABRIC da

SUBMITTER: Malighetti F 

PROVIDER: S-EPMC11900291 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature