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, 8837. https://doi.org/10.3390/ijms21228837
Machine learning identifies robust matrisome markers and regulatory mechanisms in cancer
|Author:||Kääriäinen, Anni1; Pesola, Vilma1; Dittmann, Annalena1;|
1Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland
2Faculty of Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland
3Finnish Cancer Institute, 00130 Helsinki, Finland
|Online Access:||PDF Full Text (PDF, 1.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202102023534
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2021-02-02
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.
International journal of molecular sciences
|Pages:||1 - 12|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
This research was funded by: ACADEMY OF FINLAND, grant number 329742; FINNISH CANCER INSTITUTE, K. Albin Johansson Cancer Research Fellowship; UNIVERSITY OF OULU, Profi-5 tenure track fund.
|Academy of Finland Grant Number:||
329742 (Academy of Finland Funding decision)
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).