Gene co-expression is distance-dependent in breast cancer

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-(intrachromosomal) interactions are favored in breast tumors. In order to have a deeper understanding of regulatory phenomena in cancer, here, we constructed Gene Coexpression 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 cis-co-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.

Gene Expression and Co-expression Networks Are Strongly Altered Through Stages in Clear Cell Renal Carcinoma

“…In previous works from our group, we observed abrupt changes in the way that genes co-express: for instance, we have documented a substantial decrease of inter-chromosome ( trans- ) gene-gene interactions in breast cancer (Espinal-Enriquez et al, 2017 ; Dorantes-Gilardi et al, 2020 ; García-Cortés et al, 2020 ). We decided to separate gene-gene interactions into intra-chromosome ( cis- ) and inter-chromosome ( trans- ).…”

“…(1) pre-normalization quality control, (2) batch and bias corrections (normalization) and (3) post-normalization quality control. Data pre-processing was conducted as previously (Drago-García et al, 2017 ; Espinal-Enriquez et al, 2017 ; de Anda-Jáuregui et al, 2019b , c ; García-Cortés et al, 2020 ; Serrano-Carbajal et al, 2020 ). Briefly, we assessed (a) biotype abundances, to assure that samples contained protein coding genes.…”

“…We used the mutual information (MI) statistical dependence measure to quantify co-expression between genes. We used the MI implementation on the ARACNe algorithm (Margolin et al, 2006 ), as previously described (Alcalá-Corona et al, 2017 , 2018 ; Espinal-Enriquez et al, 2017 ; de Anda-Jáuregui et al, 2019c ; García-Cortés et al, 2020 ), to determine all gene-gene interactions in the genome for the four ccRC stages and for control networks. With this procedure we inferred five networks, one for each stage and one for the control phenotype.…”

Spatial Organization of the Gene Regulatory Program: An Information Theoretical Approach to Breast Cancer Transcriptomics

“…Previously [6][7][8], we observed that in breast cancer, trans-(inter-chromosome) gene interactions are more scarce and weaker in cancer samples compared to the healthy phenotype. Furthermore, in breast cancer, cis-interactions become stronger, however, this is strongest between physically close genes, and this gene correlation strength decays with the distance.…”

“…, we identify that interchromosomal interactions between genes in any pair of chromosomes have more losses than gains of MI in disease, with an average MI loss greater than the average MI gain. Meanwhile, intrachromosomal interactions may exhibit three different behaviors: (i) they have more losses with higher average MI loss, although with higher GLS and GLR values than the interchromosomal interactions (chromosomes 1, 2, 5, 6, 11, 17, 19, X); (ii) they have more losses, but the average MI gain is higher (chromosomes 3,4,7,8,9,10,12,13,14,15,16,18,19,20,22) or (iii) they have more gains, with a higher average MI gain (21).…”