In silico identification of gene expression meta-signature that predicts breast cancer prognosis

LACUNZA E; BUTTI M; ABBA MC

Quilmes, Bernal, Argentina

Congreso; I Congreso Argentino de Bioinformática y Biología Computacional (CAB2C); 2010

Asociación Argentina de Bioinformática y Biología Computacional

Background Gobal gene expression in breast cancer has been profiled extensively over the last decade, providing novel information of biological and clinical relevance for the classification of breast cancers according to specific phenotypes. In this study, we provide a systematic analysis of the gene expression signature derived from 42 breast cancer gene expression studies in an effort to identify the most relevant breast cancer biomarkers using a novel gene list meta-analysis method. Materials and methods The study approach underwent three phases: (a) detection of overlapping genes among the different signatures, (b) examination of the relationship between gene expression signatures by a two-way unsupervised analysis, followed by (c) identification of the molecular pathways that are mainly affected by the gene expression meta-signature. Results Analysis of gene expression signatures metadata 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 the indicated core of transcripts and protein-protein interactions of this commonly modulated genes indicate three functional modules significantly affected among signatures, one module related to the estrogen receptor alpha and, and two modules related to the cell cycle signaling pathway, in addition to the targeting of various breast cancer-causing genes. Unsupervised statistical analysis of gene expression meta-signature derived from publicly available data (n=295) identified two main clusters of breast carcinomas which differed in their lymph node status (p