期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 21, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/ijms21228837
关键词
extracellular matrix; matrisome; cancer; regulatory networks; bioinformatics; big data
资金
- ACADEMY OF FINLAND [329742]
- FINNISH CANCER INSTITUTE, K. Albin Johansson Cancer Research Fellowship
- UNIVERSITY OF OULU, Profi-5 tenure track fund
- Academy of Finland (AKA) [329742, 329742] Funding Source: Academy of Finland (AKA)
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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