Frontiers in OncologySystems BiologyPeer-reviewed journal article
Gene co-expression is distance-dependent in breast cancer
Diana García-CortésiD, Guillermo de Anda‐JáureguiiD, Cristóbal FresnoiD, Enrique Hernandez LemusiD, Jesús Espinal EnríqueziD
Abstract
Breast carcinomas are characterized by anomalous gene regulatory programs. As is well known, gene expression programs are able to shape phenotypes. Hence, the understanding of gene co-expression may shed light on the underlying mechanisms behind the transcriptional regulatory programs affecting tumor development and evolution. For instance, in breast cancer, there is a clear loss of inter-chromosomal (trans-) co-expression, compared with healthy tissue. At the same time cis- (intra-chromosomal) interactions are favored in breast tumors. In order to have a deeper understanding of regulatory phenomena in cancer, here, we constructed Gene Co-expression Networks by using 848 RNA-seq whole-genome samples corresponding to the four breast cancer molecular subtypes, as well as healthy tissue. We quantify the cis-/trans- co-expression imbalance in all phenotypes. Additionally, we measured the association between co-expression and physical distance between genes, and characterized the proportion of intra/inter-cytoband interactions per phenotype. We confirmed loss of trans- co-expression in all molecular subtypes. We also observed that gene cisco-expression decays abruptly with distance in all tumors in contrast with healthy tissue. We observed co-expressed gene hotspots, that tend to be connected at cytoband regions, and coincide accurately with already known copy number altered regions, such as Chr17q12, or Chr8q24.3 for all subtypes. Our methodology recovered different alterations already reported for specific breast cancer subtypes, showing how co-expression network approaches might help to capture distinct events that modify the cell regulatory program.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 34
- FWCI
- 1.97
- Field percentile
- top 13%
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