Department of Precision and Regenerative Medicine and Polo Jonico, University of Bari Medical School, Piazza Giulio Cesare 11, Bari, Italy.
Department of Mathematics, University of Bari Aldo Moro, via Edoardo Orabona 4, 70125 Bari, Italy; INDAM-GNCS Research Group, Piazzale Aldo Moro, 5, 00185 Roma, Italy.
Pathol Res Pract. 2023 Feb;242:154347. doi: 10.1016/j.prp.2023.154347. Epub 2023 Jan 30.
Breast cancer has become a leading cause of death for women as the economy has grown and the number of women in the labor force has increased. Several biomarkers with diagnostic, prognostic, and therapeutic implications for breast cancer have been identified in studies, leading to therapeutic advances. Resistance, on the other hand, is one of clinical practice's limitations. In this paper, we use Nonnegative Matrix Factorization to automatically extract two gene signatures from gene expression profiles of wild-type and resistance MCF-7 cells, which were then investigated further using pathways analysis and proved useful in relating resistance pathways to breast cancer regardless of the stimulus that caused it. A few extracted genes (including MAOA, IL4I1, RRM2, DUT, NME4, and SUMO3) represent new elements in the functional network for resistance in MCF-7 ER+ breast cancer. As a result of this research, a better understanding of how resistance occurs or the pathways that contribute to it may allow more effective therapies to be developed.
随着经济的发展和劳动力中女性人数的增加，乳腺癌已成为女性死亡的主要原因。在研究中已经确定了一些具有诊断、预后和治疗意义的乳腺癌生物标志物，从而推动了治疗方法的进步。另一方面，耐药性是临床实践的局限性之一。在本文中，我们使用非负矩阵分解方法从野生型和耐药 MCF-7 细胞的基因表达谱中自动提取两个基因特征，然后使用途径分析进一步研究，并证明无论引起耐药性的刺激因素如何，都有助于将耐药途径与乳腺癌联系起来。一些提取的基因（包括 MAOA、IL4I1、RRM2、DUT、NME4 和 SUMO3）代表 MCF-7 ER+乳腺癌耐药功能网络中的新元素。这项研究的结果可能使人们对耐药性的发生或导致耐药性的途径有更好的了解，从而开发出更有效的治疗方法。
