Publication Date: 2010.
In this review we provide a systematic analysis of transcriptomic signatures derived from 42 breast cancer gene expression studies, in an effort to identify the most relevant breast cancer biomarkers using a meta-analysis method. Meta-data revealed a set of 117 genes that were the most commonly affected ranging from 12% to 36% of overlap among breast cancer gene expression studies. Data mining analysis of transcripts and protein-protein interactions of these commonly modulated genes indicate three functional modules signifcantly affected among signatures, one module related with the response to steroid hormone stimulus, and two modules related to the cell cycle. Analysis of a publicly available gene expression data showed that the obtained meta-signature is capable of predicting overall survival (P < 0.0001) and relapse-free survival (P < 0.0001) in patients with early-stage breast carcinomas. In addition, the identifed meta-signature improves breast cancer patient stratifcation independently of traditional prognostic factors in a multivariate Cox proportional-hazards analysis.
Facultad de Ciencias Médicas
Keywords: Ciencias Médicas; APC protein; aurora A kinase; Bub1 related protein; cyclin; estrogen receptor alpha; forkhead transcription factor; hepatocyte nuclear factor 3alpha; manganese superoxide dismutase; TTK protein kinase; ubiquitin conjugating enzyme; microarray analysis; mitosis spindle; biomarkers; breast cancer; gene expression signatures.
Repository: SEDICI (UNLP). Universidad Nacional de La Plata