Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.

ABSTRACT: Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics sign

SUBMITTER: Martin-Hernandez R 

PROVIDER: S-EPMC10658191 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature