Architecture and topologies of gene regulatory networks associated with breast cancer, adjacent normal, and normal tissues

Abstract

AbstractBreast cancer is the most commonly diagnosed cancer and a leading cause of death in women worldwide. It is heterogeneous in nature, comprising multiple subtypes with varying clinical features, treatment responses, and insufficient treatment regimens. The present study aims to identify early-stage candidate genes for efficiently managing this disease. For this, first, we identified differentially expressed genes (DEGs) which followed the construction and analysis of seven gene regulatory networks (GRNs), including five subtypes (viz. Basal, Her2, LuminalA, LuminalB, and Normal-Like), one adjacent normal (ANT), and one normal tissue using the gene expression and regulatory information. Further, the DEGs of each subtype and ANT were analyzed using differential correlation, disease annotation, and functional enrichment analysis. The topologies of these GRNs enabled us to identify tumor-specific features of different subtypes and ANT networks. Further, GRN analysis using escape velocity centrality (EVC+) identified 24 common functionally significant genes, including well-known genes such asE2F1, FOXA1, JUN, BRCA1, GATA3, ERBB2, andERBB3across subtypes and ANT responsible for the cancer development and progression. Similarly, the EVC+ also helped us to identify subtype-specific key genes (Basal: 18, Her2: 6, LuminalA: 5, LuminalB: 5, Normal-Like: 2, and ANT: 7). Moreover, the differential correlation and functional enrichment analysis also highlighted the cancer-associated role of these genes. Thus, it can be concluded that these common and ANT-specific genes have therapeutic potential, which can be explored further usingin vitroandin vivoexperiments to develop more accurate and effective early-stage diagnosis and treatment strategies for better disease management.