The Hierarchical Modular Structure of HER2+ Breast Cancer Network

Abstract

HER2-enriched breast cancer is a complex disease characterized by the overexpression of the ERBB2 amplicon. While the effects of this genomic aberration on the pathology have been studied, genome-wide deregulation patterns in this subtype of cancer are also observed. A novel approach to the study of this malignant neoplasy is the use of transcriptional networks. These networks generally exhibit modular structures, which in turn may be associated to biological processes. This modular regulation of biological functions may also exhibit a hierarchical structure, with deeper levels of modular organization accounting for more specific functional regulation. In this work, we identified the most probable (maximum likelihood) model of the hierarchical modular structure of the HER2-enriched transcriptional network as reconstructed from gene expression data, and analyzed the statistical associations of modules and submodules to biological functions. We found modular structures, independent from direct ERBB2 amplicon regulation, involved in different biological functions such as signaling, immunity, and cellular morphology. Higher resolution submodules were identified in more specific functions, such as micro-RNA regulation and the activation of viral-like immune response. We propose the approach presented here as one that may help to unveil mechanisms involved in the development of the pathology.

Comorbidity Networks in Cardiovascular Diseases

“…Modularity or clustering is a property that allows further analysis of local structural properties of a network that lead to the appearance of subunits known as strongly interconnected modules or communities (Alcalá-Corona et al, 2018b ); that is, it contains a greater number of links between nodes within the community, than the number of links to nodes outside it (Alcalá-Corona et al, 2018a ).…”

Gene Expression and Co-expression Networks Are Strongly Altered Through Stages in Clear Cell Renal Carcinoma

“…We used the mutual information (MI) statistical dependence measure to quantify co-expression between genes. We used the MI implementation on the ARACNe algorithm (Margolin et al, 2006 ), as previously described (Alcalá-Corona et al, 2017 , 2018 ; Espinal-Enriquez et al, 2017 ; de Anda-Jáuregui et al, 2019c ; García-Cortés et al, 2020 ), to determine all gene-gene interactions in the genome for the four ccRC stages and for control networks. With this procedure we inferred five networks, one for each stage and one for the control phenotype.…”

“…It is worth noticing that in order to consider the network structure in the functional enrichment, the g:SCS algorithm was implemented over network communities, and not over the whole networks. For community detection in networks we performed the Infomap algorithm (Rosvall and Bergstrom, 2008 ), as implemented in Alcalá-Corona et al ( 2016 ), Alcalá-Corona et al ( 2017 ), and Alcalá-Corona et al ( 2018 ).…”

“…We decided to separate gene-gene interactions into intra-chromosome ( cis- ) and inter-chromosome ( trans- ). We performed functional enrichment analyses for each whole-network, and also by communities inside networks, by assuming that network structure may guard functional features of an oncogenic phenotype (Alcalá-Corona et al, 2016 , 2017 , 2018 ; Hernández-Lemus et al, 2019 ).…”

Highly-connected, non-redundant microRNAs functional control in breast cancer molecular subtypes

“…Our group has found that representing the transcriptional program of breast cancer molecular subtypes as co-expression networks, it is possible by to capture the differences found between each cancer manifestation [15]. We have also shown how genes with coordinated expression patterns are found associated to each cancer subtype, and through these, it is possible to identify and associate functional perturbations to molecular subtypes [1,2].…”