Among breast cancer subtypes, triple-negative breast cancer (TNBC) is the most aggressive with the worst prognosis and the highest rates of metastatic disease. To identify TNBC gene signatures, we applied the network-based methodology implemented by the SWIM software to gene expression data of TNBC patients in The Cancer Genome Atlas (TCGA) database. SWIM enables to predict key (switch) genes within the co-expression network, whose perturbations in expression pattern and abundance may contribute to the (patho)biological phenotype. Here, SWIM analysis revealed an interesting interplay between the genes encoding the transcription factors HMGA1, FOXM1, and MYBL2, suggesting a potential cooperation among these three switch genes in TNBC development. The correlative nature of this interplay in TNBC was assessed by in vitro experiments, demonstrating how they may actually modulate the expression of each other.
Keywords: correlation networks; network medicine; triple-negative breast cancer.
© 2021 Federation of European Biochemical Societies.