Can specific transcriptional regulators assemble a universal cancer signature?

Janine Roy, Zerrin Isik, Christian Pilarsky, Michael Schroeder

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Recently, there is a lot of interest in using biomarker signatures derived from gene expression data to predict cancer progression. We assembled signatures of 25 published datasets covering 13 types of cancers. How do these signatures compare with each other? On one hand signatures answering the same biological question should overlap, whereas signatures predicting different cancer types should differ. On the other hand, there could also be a Universal Cancer Signature that is predictive independently of the cancer type. Initially, we generate signatures for all datasets using classical approaches such as t-test and fold change and then, we explore signatures resulting from a network-based method, that applies the random surfer model of Google's PageRank algorithm. We show that the signatures as published by the authors and the signatures generated with classical methods do not overlap - not even for the same cancer type - whereas the network-based signatures strongly overlap. Selecting 10 out of 37 universal cancer genes gives the optimal prediction for all cancers thus taking a first step towards a Universal Cancer Signature. We furthermore analyze and discuss the involved genes in terms of the Hallmarks of cancer and in particular single out SP1, JUN/FOS and NFKB1 and examine their specific role in cancer progression. © 2013 AIP Publishing LLC.
Original languageEnglish
Title of host publication2013 International Symposium on Computational Models for Life Sciences
Pages135-144
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 International Symposium on Computational Models for Life Sciences, CMLS 2013 - , Australia
Duration: 27 Nov 201329 Nov 2013

Publication series

NameAIP Conference Proceedings
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2013 International Symposium on Computational Models for Life Sciences, CMLS 2013
Country/TerritoryAustralia
Period27/11/1329/11/13

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