An Information Theoretical Multilayer Network Approach to Breast Cancer Transcriptional Regulation

Ochoa, Soledad; Anda-Jáuregui, Guillermo de; Hernández‐Lemus, Enrique

doi:10.3389/fgene.2021.617512

Cited by 13 publications

(3 citation statements)

References 60 publications

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“…Further steps towards the understanding of co-expression patterns and the differences in clustering among adjacent normal and cancerous phenotypes may be also based on the usage of multi-layer approaches ( 11 , 70 ).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…However, for the purpose of network reconstruction, issues such as batch effects and sample sizes may introduce additional confounding factors. An alternative approach is the use of cross-validation strategies, such as the ones used for recent multiomic network reconstructions [ 63 ]. Such approaches have shown that these methods lead to robust link prediction results.…”

Section: Discussionmentioning

confidence: 99%

Highly connected, non-redundant microRNA functional control in breast cancer molecular subtypes

2021

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Breast cancer is a complex, heterogeneous disease at the phenotypic and molecular level. In particular, the transcriptional regulatory programs are known to be significantly affected and such transcriptional alterations are able to capture some of the heterogeneity of the disease, leading to the emergence of breast cancer molecular subtypes. Recently, it has been found that network biology approaches to decipher such abnormal gene regulation programs, for instance by means of gene co-expression networks, have been able to recapitulate the differences between breast cancer subtypes providing elements to further understand their functional origins and consequences. Network biology approaches may be extended to include other co-expression patterns, like those found between genes and non-coding transcripts such as microRNAs (miRs). As is known, miRs play relevant roles in the establishment of normal and anomalous transcription processes. Commodore miRs (cdre-miRs) have been defined as miRs that, based on their connectivity and redundancy in co-expression networks, are potential control elements of biological functions. In this work, we reconstructed miR–gene co-expression networks for each breast cancer molecular subtype, from high throughput data in 424 samples from the Cancer Genome Atlas consortium. We identified cdre-miRs in three out of four molecular subtypes. We found that in each subtype, each cdre-miR was linked to a different set of associated genes, as well as a different set of associated biological functions. We used a systematic literature validation strategy, and identified that the associated biological functions to these cdre-miRs are hallmarks of cancer such as angiogenesis, cell adhesion, cell cycle and regulation of apoptosis. The relevance of such cdre-miRs as actionable molecular targets in breast cancer is still to be determined from functional studies.

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“…Pre-processing has been described before ( Ochoa et al, 2021 ) and follows published guidelines ( Aryee et al, 2014 ; Tam et al, 2015 ; Tarazona et al, 2015 ). As a first step, only protein-coding transcripts were kept since for our purposes, these were considered the main functional effectors.…”

Section: Methodsmentioning

confidence: 99%

Functional impact of multi-omic interactions in breast cancer subtypes

Ochoa

Hernández‐Lemus

2023

Front. Genet.

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Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.

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An Information Theoretical Multilayer Network Approach to Breast Cancer Transcriptional Regulation

Cited by 13 publications

References 60 publications

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

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

Highly connected, non-redundant microRNA functional control in breast cancer molecular subtypes

Functional impact of multi-omic interactions in breast cancer subtypes

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