title: Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.
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.
authors.affiliation: School of Biosciences and IBR, University of Birmingham, Edgbaston, United Kingdom.
authors.forename: Victor
authors.initials: V
authors.lastname: Trevino
authors.forename: Mahlet G
authors.initials: MG
authors.lastname: Tadesse
authors.forename: Marina
authors.initials: M
authors.lastname: Vannucci
authors.forename: Fatima
authors.initials: F
authors.lastname: Al-Shahrour
authors.forename: Philipp
authors.initials: P
authors.lastname: Antczak
authors.forename: Sarah
authors.initials: S
authors.lastname: Durant
authors.forename: Andreas
authors.initials: A
authors.lastname: Bikfalvi
authors.forename: Joaquin
authors.initials: J
authors.lastname: Dopazo
authors.forename: Moray J
authors.initials: MJ
authors.lastname: Campbell
authors.forename: Francesco
authors.initials: F
authors.lastname: Falciani
mesh_terms: D002450:Cell Communication; D045744:Cell Line, Tumor; D016207:Cytokines / Q000235:genetics / Q000378:metabolism; D018450:Disease Progression; D004847:Epithelial Cells / Q000378:metabolism / Q000473:pathology; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks / Q000235:genetics; D006801:Humans; D036341:Intercellular Signaling Peptides and Proteins / Q000235:genetics / Q000378:metabolism; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D009928:Organ Specificity / Q000235:genetics; D025341:Principal Component Analysis; D011467:Prostate / Q000378:metabolism* / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000473:pathology*; D017978:Receptors, Growth Factor / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics
chemical_list: D016207:Cytokines; D036341:Intercellular Signaling Peptides and Proteins; D017978:Receptors, Growth Factor