Network Modularity and Hierarchical Structure in Breast Cancer Molecular Subtypes

Alcalá-Corona, Sergio Antonio; Anda-Jáuregui, Guillermo de; Espinal-Enríquez, Jesús; Tovar, Hugo; Hernández‐Lemus, Enrique

doi:10.1007/978-3-319-96661-8_36

Cited by 8 publications

(12 citation statements)

References 16 publications

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“…Along with this, IFI 44 L sm is associated to viral response (Figure 4). This effect has been previously observed by our group (23, 63, 64). Again, the topology of modules in the network is strongly associated to specific and separated functions.…”

Section: Discussionsupporting

confidence: 88%

Unveiling the Link Between Inflammation and Adaptive Immunity in Breast Cancer

et al. 2019

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Inflammation has been recognized as an important driver in the development and growth of malignancies. Inflammatory signaling in cancer emerges from the combinatorial interaction of several deregulated pathways. Pathway deregulation is often driven by changes in the underlying gene regulatory networks. Confronted with such complex scenario, it can be argued that a closer analysis of the structure of such regulatory networks will shed some light on how gene deregulation led to sustained inflammation in cancer. Here, we inferred an inflammation-associated gene regulatory network from 641 breast cancer and 78 healthy samples. A modular structure analysis of the regulatory network was carried out, revealing a hierarchical modular structure. Modules show significant overrepresentation score p-values for biological processes unveiling a definite association between inflammatory processes and adaptive immunity. Other modules are enriched for T-cell activation, differentiation of CD8+ lymphocytes and immune cell migration, thus reinforcing the aforementioned association. These analyses suggest that in breast cancer tumors, the balance between antitumor response and immune tolerance involving CD8+ T cells is tipped in favor of the tumor. One possible mechanism is the induction of tolerance and anergization of these cells by persistent antigen exposure.

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Section: Discussionsupporting

confidence: 88%

Unveiling the Link Between Inflammation and Adaptive Immunity in Breast Cancer

et al. 2019

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“…This means that the clustering of the genes is not known a priori or imposed from a biological knowledge database like KEGG or GO. The co-expression of genes belonging to a function or pathway in different network modules has been previously observed (Alcalá-Corona et al, 2018).…”

Section: Methodsmentioning

confidence: 86%

Master Regulators of Signaling Pathways: An Application to the Analysis of Gene Regulation in Breast Cancer

et al. 2019

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Analysis of gene regulatory networks allows the identification of master transcriptional factors that control specific groups of genes. In this work, we inferred a gene regulatory network from a large dataset of breast cancer samples to identify the master transcriptional factors that control the genes within signal transduction pathways. The focus in a particular subset of relevant genes constitutes an extension of the original Master Regulator Inference Algorithm (MARINa) analysis. This modified version of MARINa utilizes a restricted molecular signature containing genes from the 25 human pathways in KEGG's signal transduction category. Our breast cancer RNAseq expression dataset consists of 881 samples comprising tumors and normal mammary gland tissue. The top 10 master transcriptional factors found to regulate signal transduction pathways in breast cancer we identified are: TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3, GLI2, and CLOCK. The functional enrichment of the regulons of these master transcriptional factors showed an important proportion of processes related to morphogenesis. Our results suggest that, as part of the aberrant regulation of signaling pathways in breast cancer, pathways similar to the regulation of cell differentiation, cardiovascular system development, and vasculature development may be dysregulated and co-opted in favor of tumor development through the action of these transcription factors.

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“…Modularity or clustering is a property that allows further analysis of local structural properties of a network that lead to the appearance of subunits known as strongly interconnected modules or communities (Alcalá-Corona et al, 2018b ); that is, it contains a greater number of links between nodes within the community, than the number of links to nodes outside it (Alcalá-Corona et al, 2018a ).…”

Section: Methodsmentioning

confidence: 99%

Comorbidity Networks in Cardiovascular Diseases

et al. 2020

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Background: Cardiovascular diseases are the leading causes of mortality worldwide. One reason behind this lethality lies in the fact that often cardiovascular illnesses develop into systemic failure due to the multiple connections to organismal metabolism. This in turn is associated with co-morbidities and multimorbidity. The prevalence of coexisting diseases and the relationship between the molecular origins adds to the complexity of the management of cardiovascular diseases and thus requires a profound knowledge of the genetic interaction of diseases. Objective: In order to develop a deeper understanding of this phenomenon, we examined the patterns of comorbidity as well as their genetic interaction of the diseases (or the lack of evidence of it) in a large set of cases diagnosed with cardiovascular conditions at the national reference hospital for cardiovascular diseases in Mexico. Methods: We performed a cross-sectional study of the National Institute of Cardiology. Socioeconomic information, principal diagnosis that led to the hospitalization and other conditions identified by an ICD-10 code were obtained for 34,099 discharged cases. With this information a cardiovascular comorbidity networks were built both for the full database and for ten 10-years age brackets. The associated cardiovascular comorbidities modules were found. Data mining was performed in the comprehensive ClinVar database with the disease names (as extracted from ICD-10 codes) to establish (when possible) connections between the genetic associations of the genetic interaction of diseases. The rationale is that some comorbidities may have a stronger genetic origin, whereas for others, the environment and other factors may be stronger. Results: We found that comorbidity networks are highly centralized in prevalent diseases, such as cardiac arrhythmias, heart failure, chronic kidney disease, hypertension, and ischemic diseases. Said comorbidity networks are actually modular on their connectivity. Modules recapitulate physiopathological commonalities, e.g., ischemic diseases clustering together. This is also the case of chronic systemic diseases, of congenital malformations and others. The genetic and environmental commonalities behind some of the relations in these modules were also found by resorting to clinical genetics databases and functional pathway enrichment studies. Conclusions: This methodology, hence may allow the clinician to look up for non-evident comorbidities whose knowledge will lead to improve therapeutically designs. By continued and consistent analysis of these types of patterns, we envisaged that it may be possible to acquire, strong clinical and basic insights that may further our advance toward a better understanding of cardiovascular diseases as a whole. Hopefully these may in turn lead to further development of better, integrated therapeutic strategies.

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Network Modularity and Hierarchical Structure in Breast Cancer Molecular Subtypes

Cited by 8 publications

References 16 publications

Unveiling the Link Between Inflammation and Adaptive Immunity in Breast Cancer

Unveiling the Link Between Inflammation and Adaptive Immunity in Breast Cancer

Master Regulators of Signaling Pathways: An Application to the Analysis of Gene Regulation in Breast Cancer

Comorbidity Networks in Cardiovascular Diseases

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