Dataset Information

Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer.

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.

SUBMITTER: Kaariainen A

PROVIDER: S-EPMC7700160 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer.

Kääriäinen Anni A Pesola Vilma V Dittmann Annalena A Kontio Juho J Koivunen Jarkko J Pihlajaniemi Taina T Izzi Valerio V

International journal of molecular sciences 20201122 22

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 req ...[more]

PMID: 33266472

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Dataset Information

Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer.

Publications

Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer.

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