Gene Co-Expression in Breast Cancer: A Matter of Distance

Abstract

Gene regulatory and signaling phenomena are known to be relevant players underlying the establishment of cellular phenotypes. It is also known that such 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 (BrCa) and their control counterparts. Global co-expression patterns have revealed, for instance, that the highest gene-gene co-expression interactions often occur between genes from the same chromosome (cis-), meanwhile inter-chromosome (trans-) interactions are scarce and have lower correlation values. Furthermore, strength of cis- correlations have been shown to 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, namely the most significant interactions. For that reason, an approach that takes into account the whole interaction set results appealing. In this work, we developed a hybrid method to analyze whole-chromosome Pearson correlation matrices for the four BrCa subtypes (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 any interaction. With this method we compared, for each chromosome in the five phenotypes: a) Whether or not the gene-gene co-expression decays with the distance in the breast cancer subtypes b) the chromosome location of cis- clusters of gene couples, and c) whether or not the loss of long-distance co-expression is observed in the whole range of interactions. We found that in the correlation matrix for the control phenotype, positive and negative Pearson correlations deviate from a random null model independently of the distance between couples. Conversely, for all BrCa subtypes, in all chromosomes, positive correlations decay with distance, and negative correlations do not differ from the null model. We also found that BrCa clusters are distance-dependent, meanwhile for the control phenotype, chromosome location does not determine the clustering. To our knowledge, this is the first time that a dependence on distance is reported for gene clusters in breast cancer. 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.

Oncogenic Role of miR-217 During Clear Cell Renal Carcinoma Progression

“…The bias to intra-chromosomal interactions has been previously reported by our group in gene co-expression networks for breast cancer (28,48,(70)(71)(72), lung cancer (31), and also CCRC (32). These results show a clear trend to favor close gene correlations in terms of base pair distance.…”

The network structure of hematopoietic cancers

“…As expected, several of these smaller components are made up of genes from the same chromosome. This result coincide with the previous GCNs analyzed from breast cancer (2)(3)(4), lung (5), and clear cell renal carcinoma (6,7) suggesting a common regulatory mechanism affecting the entire system, which is manifested in the network topologies. Another aspect that we consider to be crucial in the topological analysis of the GCNs of cancer phenotypes is the regulation of inter-chromosomal interactions.…”

“…As expected, several of these smaller components are made up of genes from the same chromosome. This result coincide with the previous GCNs analyzed from breast cancer (2–4), lung (5), and clear cell renal carcinoma (6, 7) suggesting a common regulatory mechanism affecting the entire system, which is manifested in the network topologies.…”

Loss of long-range co-expression is a common trait in cancer

“…GCNs provide a topological characterization of the transcriptome, allowing us to compare not only global and local connectivity properties from different phenotypes, but also their functional implications (12)(13)(14). By analyzing gene co-expression profiles and building GCNs using RNA-Seq data in cancer tissue and its normal counterpart, we have previously described a loss in the proportion of inter-chromosomal gene co-expression interactions in breast cancer and breast cancer molecular subtypes (15)(16)(17)(18), lung cancer (19), and clear cell renal carcinoma (20). In these tissues, the majority of significant co-expression links connect gene pairs in the same chromosome and these display a highly localized co-expression pattern.…”