Gene co-expression is distance-dependent in breast cancer
Garcia-Cortes, D.; de Anda-Jauregui, G.; Fresno, C.; Hernandez-Lemus, E.; Espinal-Enriquez, J.
Show 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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Directed Bayesian Networks established functional differences between breast cancer subtypes 95%
- Cluster analysis on high dimensional RNA-seq data with applications to cancer research- An evaluation study 94%
- The Impact of Variance in Carnitine Palmitoyltransferase-1 Expression on Breast Cancer Prognosis is Stratified by Clinical and Anthropometric Factors 94%
Similar papers in this journal
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning 95%
- Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method 94%
- Novel ratio-metric features enable the identification of new driver genes across cancer types 93%
Similar papers in this journal
- Unveiling epigenetic regulatory elements associated with breast cancer development 96%
- Immunohistochemical Profiling of Histone Modification Biomarkers Identifies Subtype-Specific Epigenetic Signatures and Potential Drug Targets in Breast Cancer 93%
- From miRNA target gene network to miRNA function: miR-375 might regulate apoptosis and actin dynamics in the heart muscle via Rho-GTPases-dependent pathways 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.