Gene Co-Expression in Breast Cancer: A Matter of Distance - González-Espinoza, Alfredo; Zamora-Fuentes, Jose; Hernández-Lemus, Enrique; Espinal-Enríquez, Jesús (Frontiers in Oncology, 2021)
Gene regulation is a crucial player underlying the establishment of cellular phenotypes. Regulatory programs are disrupted in cancer, leading to the onset and development of malignant phenotypes. Gene co-expression matrices have allowed us to compare and analyze complex phenotypes such as breast cancer and their control counterparts. Global co-expression patterns have revealed, for instance, that the highest gene-gene co-expression interactions often occur between same-chromosome genes (cis-), meanwhile inter-chromosome (trans-) interactions are scarce and have lower correlation values. Furthermore, strength of cis- correlations decay with the chromosome distance of gene couples. Despite this loss of long-distance co-expression has been clearly identified, it has been observed only in a small fraction of the whole co-expression landscape. For that reason, an approach that takes into account all interactions results appealing. In this work, we developed a hybrid method to analyze whole-chromosome Pearson correlation matrices for the four subtypes of breast cancer (Luminal A, Luminal B, HER2+ and Basal), as well as adjacent-normal breast tissue derived matrices. We implemented a systematic method for clustering gene couples, by using eigenvalue spectral decomposition and the k-medoids algorithm, allowing us to determine a number of clusters without removing interactions. We compared, for each chromosome in the five phenotypes: i) Whether co-expression decays with the distance in the breast cancer subtypes ii) the chromosome location of cis- clusters of gene couples, and iii) whether the loss of long-distance co-expression is a phenomenon observed in the whole range of interactions. We found that, in the control correlation matrix, positive and negative Pearson correlations deviate from a random null model independently of the distance between couples. Conversely, for all breast cancer subtypes, in all chromosomes, positive correlations decay with distance, and negative correlations do not differ from the null model. We also observe how clusters of gene couples interacting in breast cancer subtypes are distance-dependent, meanwhile for the adjacent-normal phenotype, chromosome location does not determine the clustering. Since this method uses the whole cis- interaction geneset, combination with other -omics approaches may provide further evidence to understand in a more integrative fashion, the mechanisms that disrupt gene regulation in cancer.