Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

Abstract

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

Gene Co-Expression in Breast Cancer: A Matter of Distance

“…The effect of loss in long range co-expression is consistent with previous works of regulatory networks in breast cancer ( 10 , 12 – 14 , 33 , 53 ). The block-type structure of the basal subtype matrix suggests the utility of clustering analysis to compare the structural properties of the correlation matrices.…”

Modularity in Biological Networks

“…The aforementioned methods have different postulates and different approaches to detect communities. In that work (García-Cortés et al, 2021) it was demonstrated that, independent of the algorithm used to detect communities, the results were very similar in terms of the number of detected communities and the nature of the genes observed in each community.…”

“…However, this is not always the case. For example, in García-Cortés et al ( 2021 ), for Luminal A breast cancer, an RNA-Seq-derived gene co-expression network was decomposed into communities by using four different methods: Fast greedy (Clauset et al, 2004 ), Infomap (Rosvall and Bergstrom, 2008 ), Leading eigenvector (Newman, 2006b ) and Louvain (Blondel et al, 2008 ).…”

DataSheet_1.zip

“…The apparently contradictory result suggested that the copy number alterations do not influence the structure of that community. On the other hand, a gene community formed by HLA family genes, presented a common pattern of amplification, but those genes were not differentially expressed (53). With the aforementioned in mind, we argue that CNVs are not as relevant as one could expect in terms of the gene clustering shown here.…”