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.

Highlights

  • Gene co-expression networks (GCN) enable the study of interactions of highly correlated genes in a transcriptional program, capturing global and local connectivity properties emerging from those interactions (Sonawane et al, 2019)

  • To evaluate the previous observation, communities were detected in both networks using four algorithms for weighted networks implemented in the igraph package: Fast Greedy, Infomap, Leading Eigenvector, and Louvain

  • Overexpression of DTL and HMGB2 has been associated with tumor progression in breast cancer (Perez-Peña et al, 2017; Fu et al, 2018), and resistance to endocrine therapies (Redmond et al, 2015). These results suggest a strong contribution of transcription factors (TFs), from FOXM1 and CENPA, and their interactions found in the NUSAP1 community, to the process of tumorigenesis and progression in Luminal A breast cancer

Read more

Summary

Introduction

Gene co-expression networks (GCN) enable the study of interactions of highly correlated genes in a transcriptional program, capturing global and local connectivity properties emerging from those interactions (Sonawane et al, 2019) These type of networks are built from gene expression profiles, a measurable output of transcription. They outline the contribution of the regulatory elements operating at different levels of the transcription process to ensure the expression of specific sets of genes In this sense, GCNs might provide insights about shared regulatory mechanisms and their alterations in a disease, such as cancer (Emmert-Streib et al, 2014; Yang et al, 2014; Wu et al, 2019; Liao et al, 2020). There are multiple studies where GCNs are constructed and important aspects of the connectivity structure are analyzed to identify genes prognosis markers (Hsu et al, 2019), metabolic deregulation (Serrano-Carbajal et al, 2020), and differences in transcriptional profiles (van Dam et al, 2018)

Methods
Results
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.