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Defining Functional Hub Genes in Basal-like Breast Cancer Networks

ABSTRACT: Defining Functional Hub Genes in Basal-like Breast Cancer Networks

PROVIDER: PRJNA1305004 | ENA |

REPOSITORIES: ENA

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Defining Functional Hub Genes in Basal-like Breast Cancer Networks

Project description:The behaviour of complex biological systems emerges from the coordinated activity of networked molecular components. In this context, gene regulatory networks (aka gene coexpression networks) offer insights into the regulation of gene expression programs. In cancer, aberrant gene expression underlies molecular and clinical features, and identifying key networked transcriptional regulators may enable targeted therapeutic interventions. However, computationally inferred regulatory nodes have so far hardly been experimentally validated. Here we combined gene expression network analysis with gene perturbation experiments to test whether computationally identified hub genes act as upstream regulators of their coexpression modules in breast cancer. To better capture the context-dependent nature of gene regulation and minimize confounding effects from inter-subtype heterogeneity, we also constructed subtype-specific networks. Using the METABRIC transcriptomic dataset of primary breast tumours, we identified clinically-informative gene modules in the highly aggressive basal-like subtype. Candidate regulatory hubs were prioritized based on network centrality, and their functional relevance was assessed both in silico and in vitro. CRISPR-mediated knockout of selected hub genes resulted in coordinated down-regulation of module genes and impaired cellular functions, demonstrating causal links between hub gene function, module expression and phenotypic outcome. Moreover, we observed a significant correlation between the transcriptional impact of each knockout and its functional effects—highlighting the biological relevance of coexpression modules and supporting the hypothesis that their structure reflects functional dependencies.

2025-08-20 | GSE305293 | GEO

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Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.

Project description:Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated activities regulate essential pathways in normal physiology and in disease. Due to their complexity, the challenge remains in understanding their direct associations in cancer development. Basal-like breast cancer is an aggressive form of breast cancer that often lacks ER, PR and Her2. The absence of these receptors limits the treatment for patients to the non-selective cytotoxic and cytostatic drugs. To identify potential drug targets it is essential to identify the most important nuclear receptor association network motifs in Basal-like subtype progression. This research aimed to reveal the transcriptional network patterns, in the hope to capture the underlying molecular state driving Basal-like oncogenesis. In this work, we illustrate a multidisciplinary approach of integrating an unsupervised machine learning clustering method with network modelling to reveal unique transcriptional patterns (network motifs) underlying Basal-like breast cancer. The unsupervised clustering method provides a natural stratification of breast cancer patients, revealing the underlying heterogeneity in Basal-like. Identification of gene correlation networks (GCNs) from Basal-like patients in both the TCGA and METABRIC databases revealed three critical transcriptional regulatory constellations that are enriched in Basal-like. These represent critical NR components implicated in Basal-like breast cancer transcription. This approach is easily adaptable and applicable to reveal critical signalling relationships in other diseases.

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