Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers - Martin-Hernandez, Roberto; Espeso-Gil, Sergio; Domingo, Clara; Latorre, Pablo; Hervas, Sergi; Hernandez Mora, Jose Ramon; Kotelnikova, Ekaterina (Frontiers in Molecular Biosciences, 2023)
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 multiomics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC.prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients.Conclusions: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.