Main genes in breast cancer primary tumor and first metastasis in lymph nodes revealed by information-theory-based genetic networks pattern analysis

Abstract

In this paper, we use pattern analysis in genetic networks to identify differentially expressed genes in primary breast cancer tumors and their first metastasis in lymph nodes, using human biopsies from the GEO and GDCDP databases. By applying Information-Theory-based algorithms to process gene expression profile matrices, we obtained the genetic networks of the following tissues: (1) breast cancer-free, (2) primary breast cancer tumors, and (3) first metastasis of breast cancer in lymph nodes. Topological analysis of the genetic networks delves for identifying patterns of pairs of genes with higher mutual information than a threshold; then, among these genes, the ones with highest degree are elected. We propose the plausible hypothesis that the elected genes, having principal roles in each network, could be relevant as biomarkers regarding the genetic information. A subsequent gene ontology-based analysis of the molecular and functional characteristics of these genes reveals specific signaling pathways signatures in cancer-free tissue and in the tumor microenvironment associated with primary and metastatic requirements. Furthermore, a state-of-the-art review of the functional roles of genes reveals tumor suppressor genes in cancer-free tissue and proliferation- and migration-associated genes in cancer.