Refereed journal article or data article (A1)

Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer

List of Authors: Kaariainen A, Pesola V, Dittmann A, Kontio J, Koivunen J, Pihlajaniemi T, Izzi V

Publisher: MDPI

Publication year: 2020

Journal: International Journal of Molecular Sciences


Journal acronym: INT J MOL SCI

Volume number: 21

Number of pages: 12


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.

Last updated on 2022-01-12 at 11:36