Analysis of Normal-Tumour Tissue Interaction in Tumours: Prediction of Prostate Cancer Features from the Molecular Profile of Adjacent Normal Cells
Victor Trevino,
Mahlet G Tadesse,
Marina Vannucci,
Fatima Al-Shahrour,
Philipp Antczak,
Sarah Durant,
Andreas Bikfalvi,
Joaquin Dopazo,
Moray J Campbell and
Francesco Falciani
PLOS ONE, 2011, vol. 6, issue 3, 1-13
Abstract:
Statistical modelling, in combination with genome-wide expression profiling techniques, has demonstrated that the molecular state of the tumour is sufficient to infer its pathological state. These studies have been extremely important in diagnostics and have contributed to improving our understanding of tumour biology. However, their importance in in-depth understanding of cancer patho-physiology may be limited since they do not explicitly take into consideration the fundamental role of the tissue microenvironment in specifying tumour physiology. Because of the importance of normal cells in shaping the tissue microenvironment we formulate the hypothesis that molecular components of the profile of normal epithelial cells adjacent the tumour are predictive of tumour physiology. We addressed this hypothesis by developing statistical models that link gene expression profiles representing the molecular state of adjacent normal epithelial cells to tumour features in prostate cancer. Furthermore, network analysis showed that predictive genes are linked to the activity of important secreted factors, which have the potential to influence tumor biology, such as IL1, IGF1, PDGF BB, AGT, and TGFβ.
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0016492 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 16492&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0016492
DOI: 10.1371/journal.pone.0016492
Access Statistics for this article
More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().