Handling Big Data in Precision Medicine

Hernández‐Lemus, Enrique; Espinal-Enríquez, Jesús; Herrera, Ricardo A. Garcia

doi:10.1016/b978-0-12-809411-2.00014-3

Cited by 3 publications

(1 citation statement)

References 94 publications

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“…Furthermore, the expression signature of molecules associated with the proteasome complex indicates that cell death-related events are strongly decreased. We suggest that communities found with this approach must be further investigated experimentally looking for alternative therapeutic options in the spirit of personalized medicine (Espinal-Enríquez et al, 2017b ; Hernández-Lemus et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Network Modularity in Breast Cancer Molecular Subtypes

et al. 2017

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Breast cancer is a heterogeneous and complex disease, a clear manifestation of this is its classification into different molecular subtypes. On the other hand, gene transcriptional networks may exhibit different modular structures that can be related to known biological processes. Thus, modular structures in transcriptional networks may be seen as manifestations of regulatory structures that tightly controls biological processes. In this work, we identify modular structures on gene transcriptional networks previously inferred from microarray data of molecular subtypes of breast cancer: luminal A, luminal B, basal, and HER2-enriched. We analyzed the modules (communities) found in each network to identify particular biological functions (described in the Gene Ontology database) associated to them. We further explored these modules and their associated functions to identify common and unique features that could allow a better level of description of breast cancer, particularly in the basal-like subtype, the most aggressive and poor prognosis manifestation. Our findings related to the immune system and a decrease in cell death-related processes in basal subtype could help to understand it and design strategies for its treatment.

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Section: Introductionmentioning

confidence: 99%