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

Abstract

Transcriptional patterns are altered in breast cancer. These alterations capture the heterogeneity of breast cancer, leading to the emergence of molecular subtypes. Network biology approaches to study gene co-expression are able to capture the differences between breast cancer subtypes. Network biology approaches may be extended to include other co-expression patterns, like those found between genes and non-coding RNA: such as mi-croRNAs (miRs). Commodore miRs are microRNAs that, based on their connectivity and redundancy in co-expression networks, have been proposed as potential control elements of biological functions. In this work, we reconstructed miR-gene co-expression networks for each breast cancer molecular subtype. We identified Commodore miRs in three out of four molecular subtypes. We found that in each subtype, each cdre-miR had a different set of associated genes, as well as a different set of associated biological functions. We used a systematic literature validation strategy, and identified that the associated biological functions to these cdre-miRs are hallmarks of cancer.

Gene-Microbiome Co-expression Networks in Colon Cancer

“…Since it is capable to capture non-linear relationships between features, it has been successfully used for gene co-expression network reconstruction (de Anda-Jáuregui et al, 2016 ; He et al, 2017 ). It has also been previously used for bipartite network reconstruction of multiomic data (de Anda-Jáuregui et al, 2018 , 2019 ). In this work, we calculated MI using the package in R.…”

“…For completeness, the reconstructed networks contained all measured microorganisms ( N = 4, 450) and protein-coding genes ( N = 16, 593), even if they do not participate in any link (that is, they have connectivity degree k = 0). The threshold was selected based on previous analyses of multi-omic bipartite networks (de Anda-Jáuregui et al, 2018 , 2019 ); we must acknowledge that by using this threshold we guarantee fair comparisons between the reconstructed networks; however, the structure and composition of these networks will not be comparable to networks generated through other methods (including the selection of a different threshold).…”

“…Networks provide flexible frameworks to represent the relevant physio-pathological interactions present in the tumor environments. For instance, bipartite networks have been used to represent gene expression control by micro-RNAs; a strategy that allows not only to describe statistical associations, but also to identify putative functional associations (de Anda-Jáuregui et al, 2018 , 2019 ).…”