Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.
Fondazione per la Medicina Personalizzata, Genova, Italy.
FEBS Lett. 2021 Jun;595(11):1569-1586. doi: 10.1002/1873-3468.14085. Epub 2021 Apr 27.
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.
在乳腺癌亚型中，三阴性乳腺癌（TNBC）侵袭性最强，预后最差，转移性疾病发生率最高。为了鉴定 TNBC 的基因特征，我们应用基于网络的方法，该方法由 SWIM 软件实现，对来自癌症基因组图谱（TCGA）数据库的 TNBC 患者的基因表达数据进行分析。SWIM 可以预测共表达网络中的关键（开关）基因，这些基因的表达模式和丰度的改变可能有助于（病理）生物学表型。在这里，SWIM 分析揭示了编码转录因子 HMGA1、FOXM1 和 MYBL2 的基因之间有趣的相互作用，表明这三个开关基因在 TNBC 发展中可能存在潜在的合作。通过体外实验评估了这种相互作用在 TNBC 中的相关性，证明了它们实际上可能如何相互调节对方的表达。
