Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer

Understanding cancer cell signal transduction is a promising lead for uncovering therapeutic targets and building treatment-specific markers for epithelial ovarian cancer. To brodaly assay the many known transmembrane receptor systems, previous studies have employed gene expression data measured on...

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Main Authors: Eng, Kevin H., Ruggeri, Christina
Formato: Artigo
Idioma:Inglês
Publicado em: Public Library of Science 2014
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Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC4171104/
https://ncbi.nlm.nih.gov/pubmed/25244152
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0107193
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spelling pubmed-41711042014-09-25 Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer Eng, Kevin H. Ruggeri, Christina PLoS One Research Article Understanding cancer cell signal transduction is a promising lead for uncovering therapeutic targets and building treatment-specific markers for epithelial ovarian cancer. To brodaly assay the many known transmembrane receptor systems, previous studies have employed gene expression data measured on high-throughput microarrays. Starting with the knowledge of validated ligand-receptor pairs (LRPs), these studies postulate that correlation of the two genes implies functional autocrine signaling. It is our goal to consider the additional weight of evidence that prognosis (progression-free survival) can bring to prioritize ovarian cancer specific signaling mechanism. We survey three large studies of epithelial ovarian cancers, with gene expression measurements and clinical information, by modeling survival times both categorically (long/short survival) and continuously. We use differential correlation and proportional hazards regression to identify sets of LRPs that are both prognostic and correlated. Of 475 candidate LRPs, 77 show reproducible evidence of correlation; 55 show differential correlation. Survival models identify 16 LRPs with reproduced, significant interactions. Only two pairs show both interactions and correlation (PDGFA[Image: see text]PDGFRA and COL1A1[Image: see text]CD44) suggesting that the majority of prognostically useful LRPs act without positive feedback. We further assess the connectivity of receptors using a Gaussian graphical model finding one large graph and a number of smaller disconnected networks. These LRPs can be organized into mutually exclusive signaling clusters suggesting different mechanisms apply to different patients. We conclude that a mix of autocrine and endocrine LRPs influence prognosis in ovarian cancer, there exists a heterogenous mix of signaling themes across patients, and we point to a number of novel applications of existing targeted therapies which may benefit ovarian cancer. Public Library of Science 2014-09-22 /pmc/articles/PMC4171104/ /pubmed/25244152 http://dx.doi.org/10.1371/journal.pone.0107193 Text en © 2014 Eng, Ruggeri http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
institution US NLM
collection PubMed Central
language Inglês
format Artigo
topic Research Article
spellingShingle Research Article
Eng, Kevin H.
Ruggeri, Christina
Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
description Understanding cancer cell signal transduction is a promising lead for uncovering therapeutic targets and building treatment-specific markers for epithelial ovarian cancer. To brodaly assay the many known transmembrane receptor systems, previous studies have employed gene expression data measured on high-throughput microarrays. Starting with the knowledge of validated ligand-receptor pairs (LRPs), these studies postulate that correlation of the two genes implies functional autocrine signaling. It is our goal to consider the additional weight of evidence that prognosis (progression-free survival) can bring to prioritize ovarian cancer specific signaling mechanism. We survey three large studies of epithelial ovarian cancers, with gene expression measurements and clinical information, by modeling survival times both categorically (long/short survival) and continuously. We use differential correlation and proportional hazards regression to identify sets of LRPs that are both prognostic and correlated. Of 475 candidate LRPs, 77 show reproducible evidence of correlation; 55 show differential correlation. Survival models identify 16 LRPs with reproduced, significant interactions. Only two pairs show both interactions and correlation (PDGFA[Image: see text]PDGFRA and COL1A1[Image: see text]CD44) suggesting that the majority of prognostically useful LRPs act without positive feedback. We further assess the connectivity of receptors using a Gaussian graphical model finding one large graph and a number of smaller disconnected networks. These LRPs can be organized into mutually exclusive signaling clusters suggesting different mechanisms apply to different patients. We conclude that a mix of autocrine and endocrine LRPs influence prognosis in ovarian cancer, there exists a heterogenous mix of signaling themes across patients, and we point to a number of novel applications of existing targeted therapies which may benefit ovarian cancer.
author Eng, Kevin H.
Ruggeri, Christina
author_facet Eng, Kevin H.
Ruggeri, Christina
author_sort Eng, Kevin H.
title Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_short Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_full Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_fullStr Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_full_unstemmed Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_sort connecting prognostic ligand receptor signaling loops in advanced ovarian cancer
publisher Public Library of Science
publishDate 2014
url https://ncbi.nlm.nih.gov/pmc/articles/PMC4171104/
https://ncbi.nlm.nih.gov/pubmed/25244152
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0107193
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