Kääriäinen A, Pesola V, Dittmann A, Kontio J, Koivunen J, Pihlajaniemi T, Izzi V. Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer.
Int J Mol Sci 2020;
21:E8837. [PMID:
33266472 PMCID:
PMC7700160 DOI:
10.3390/ijms21228837]
[Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/20/2020] [Accepted: 11/20/2020] [Indexed: 12/12/2022] Open
Abstract
The expression and regulation of matrisome genes-the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors-is of paramount importance for many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding of the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of "landmark" matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.
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