Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers.

Autor:	Cook CJ; Department of Bioengineering, Oregon State University, Corvallis, OR 97331, USA., Miller AE; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA., Barker TH; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA., Di Y; Department of Statistics, Oregon State University, Corvallis, OR 97331, USA., Fogg KC; Department of Bioengineering, Oregon State University, Corvallis, OR 97331, USA.; Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, OR 97201, USA.
Jazyk:	angličtina
Zdroj:	Matrix biology plus [Matrix Biol Plus] 2022 Jul 16; Vol. 15, pp. 100117. Date of Electronic Publication: 2022 Jul 16 (Print Publication: 2022).
DOI:	10.1016/j.mbplus.2022.100117
Abstrakt:	Increasingly, the matrisome, a set of proteins that form the core of the extracellular matrix (ECM) or are closely associated with it, has been demonstrated to play a key role in tumor progression. However, in the context of gynecological cancers, the matrisome has not been well characterized. A holistic, yet targeted, exploration of the tumor microenvironment is critical for better understanding the progression of gynecological cancers, identifying key biomarkers for cancer progression, establishing the role of gene expression in patient survival, and for assisting in the development of new targeted therapies. In this work, we explored the matrisome gene expression profiles of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) using publicly available RNA-seq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) portal. We hypothesized that the matrisomal expression patterns of CESC, UCEC, and UCS would be highly distinct with respect to genes which are differentially expressed and hold inferential significance with respect to tumor progression, patient survival, or both. Through a combination of statistical and machine learning analysis techniques, we identified sets of genes and gene networks which characterized each of the gynecological cancer cohorts. Our findings demonstrate that the matrisome is critical for characterizing gynecological cancers and transcriptomic mechanisms of cancer progression and outcome. Furthermore, while the goal of pan-cancer transcriptional analyses is often to highlight the shared attributes of these cancer types, we demonstrate that they are highly distinct diseases which require separate analysis, modeling, and treatment approaches. In future studies, matrisome genes and gene ontology terms that were identified as holding inferential significance for cancer stage and patient survival can be evaluated as potential drug targets and incorporated into in vitro models of disease. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 Published by Elsevier B.V.)
Databáze:	MEDLINE
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