A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models

Ovarian carcinomas can be aggressive with a high mortality rate (e.g., high-grade serous ovarian carcinomas, or HGSOCs), or indolent with much better long-term outcomes (e.g., low-malignant-potential, or LMP, serous ovarian carcinomas). By comparing LMP and HGSOC tumors, we can gain insight into the...

Full description

Bibliographic Details
Main Authors:	Yang, Mary Qu, Elnitski, Laura
Format:	Online
Language:	English
Published:	Public Library of Science 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082879/

id	pubmed-5082879
recordtype	oai_dc
spelling	pubmed-50828792016-11-04 A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models Yang, Mary Qu Elnitski, Laura Research Article Ovarian carcinomas can be aggressive with a high mortality rate (e.g., high-grade serous ovarian carcinomas, or HGSOCs), or indolent with much better long-term outcomes (e.g., low-malignant-potential, or LMP, serous ovarian carcinomas). By comparing LMP and HGSOC tumors, we can gain insight into the mechanisms underlying malignant progression in ovarian cancer. However, previous studies of the two subtypes have been focused on gene expression analysis. Here, we applied a systems biology approach, integrating gene expression profiles derived from two independent data sets containing both LMP and HGSOC tumors with protein-protein interaction data. Genes and related networks implicated by both data sets involved both known and novel disease mechanisms and highlighted the different roles of BRCA1 and CREBBP in the two tumor types. In addition, the incorporation of somatic mutation data revealed that amplification of PAK4 is associated with poor survival in patients with HGSOC. Thus, perturbations in protein interaction networks demonstrate differential trafficking of network information between malignant and benign ovarian cancers. The novel network-based molecular signatures identified here may be used to identify new targets for intervention and to improve the treatment of invasive ovarian cancer as well as early diagnosis. Public Library of Science 2016-10-27 /pmc/articles/PMC5082879/ /pubmed/27788148 http://dx.doi.org/10.1371/journal.pone.0163353 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Yang, Mary Qu Elnitski, Laura
spellingShingle	Yang, Mary Qu Elnitski, Laura A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models
author_facet	Yang, Mary Qu Elnitski, Laura
author_sort	Yang, Mary Qu
title	A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models
title_short	A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models
title_full	A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models
title_fullStr	A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models
title_full_unstemmed	A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models
title_sort	systems biology comparison of ovarian cancers implicates putative somatic driver mutations through protein-protein interaction models
description	Ovarian carcinomas can be aggressive with a high mortality rate (e.g., high-grade serous ovarian carcinomas, or HGSOCs), or indolent with much better long-term outcomes (e.g., low-malignant-potential, or LMP, serous ovarian carcinomas). By comparing LMP and HGSOC tumors, we can gain insight into the mechanisms underlying malignant progression in ovarian cancer. However, previous studies of the two subtypes have been focused on gene expression analysis. Here, we applied a systems biology approach, integrating gene expression profiles derived from two independent data sets containing both LMP and HGSOC tumors with protein-protein interaction data. Genes and related networks implicated by both data sets involved both known and novel disease mechanisms and highlighted the different roles of BRCA1 and CREBBP in the two tumor types. In addition, the incorporation of somatic mutation data revealed that amplification of PAK4 is associated with poor survival in patients with HGSOC. Thus, perturbations in protein interaction networks demonstrate differential trafficking of network information between malignant and benign ovarian cancers. The novel network-based molecular signatures identified here may be used to identify new targets for intervention and to improve the treatment of invasive ovarian cancer as well as early diagnosis.
publisher	Public Library of Science
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082879/
_version_	1613700763145469952

A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models

Similar Items