Gene expression profiles and molecular classification to predict distant metastasis and tamoxifen-resistant breast cancer

PNME - A gene-gene parallel network module extraction method

In the domain of gene-gene network analysis, construction of co-expression networks and extraction of network modules have opened up enormous possibilities for exploring the role of genes in biological processes. Through such analysis, one can extract interesting behaviour of genes and would help in the discovery of genes participating in a common biological process. However, such network analysis methods in sequential processing mode often have been found time-consuming even for a moderately sized dataset.

It is observed that most existing network construction techniques are capable of handling only positive correlations in gene-expression data whereas biologically-significant genes exhibit both positive and negative correlations. To address these problems, we propose a faster method for construction and analysis of gene-gene network and extraction of modules using a similarity measure which can identify both negatively and positively correlated co-expressed patterns. Our method utilizes General-purpose computing on graphics processing units (GPGPU) to provide fast, efficient and parallel extraction of biologically relevant network modules to support biomarker identification for breast cancer. The modules extracted are validated using p-value and q-value for both metastasis and non-metastasis stages of breast cancer. PNME has been found capable of identifying interesting biomarkers for this critical disease. We identified six genes with the interesting behaviours which have been found to cause breast cancer in homo-sapiens.

A review of the biological and clinical characteristics of luminal-like oestrogen receptor-positive breast cancer

Global gene expression profiling (GEP) studies of breast cancer have identified distinct biological classes with different clinical and therapeutic implications. Oestrogen receptor (ER) has been found to be a central marker of the molecular signature. GEP studies have consistently recognized a molecularly distinct class of tumours that is characterized by high-level expression of ER and other biomarkers recognized to be characteristic of normal luminal cells of the breast. This class is the largest of the GEP-defined molecular subclasses, comprising 60-70% of breast cancer cases. Moreover, it has been proposed that this group of tumours is composed of at least two subclasses distinguished by differing GEP profiles. At present, there is no consensus on the definition of the luminal subclasses and, in clinical practice, luminal-like tumours and ER-positive tumours are frequently considered to be the same. A better understanding of the biological features of luminal tumours could lead to their improved characterization and consistent identification. In this review, we explore the concept and definitions of the luminal-like class of breast carcinoma and their contribution to our understanding of their molecular features, clinical significance and therapeutic implications.