Asim Ayesha, Kiani Yusra Sajid, Saeed Muhammad Tariq, Jabeen Ishrat
School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology, (NUST), Islamabad, Pakistan.
Front Mol Biosci. 2022 Jul 11;9:882738. doi: 10.3389/fmolb.2022.882738. eCollection 2022.
Breast carcinogenesis is known to be instigated by genetic and epigenetic modifications impacting multiple cellular signaling cascades, thus making its prevention and treatments a challenging endeavor. However, epigenetic modification, particularly DNA methylation-mediated silencing of key TSGs, is a hallmark of cancer progression. One such tumor suppressor gene (TSG) (Runt-related transcription factor 3) has been a new insight in breast cancer known to be suppressed due to local promoter hypermethylation mediated by DNA methyltransferase 1 (DNMT1). However, the precise mechanism of epigenetic-influenced silencing of the signaling resulting in cancer invasion and metastasis remains inadequately characterized. In this study, a biological regulatory network (BRN) has been designed to model the dynamics of the DNMT1- network augmented by other regulators such as p21, c-myc, and p53. For this purpose, the René Thomas qualitative modeling was applied to compute the unknown parameters and the subsequent trajectories signified important behaviors of the DNMT1- network (i.e., recovery cycle, homeostasis, and bifurcation state). As a result, the biological system was observed to invade cancer metastasis due to persistent activation of oncogene c-myc accompanied by consistent downregulation of TSG Conversely, homeostasis was achieved in the absence of c-myc and activated TSG . Furthermore, DNMT1 was endorsed as a potential epigenetic drug target to be subjected to the implementation of machine-learning techniques for the classification of the active and inactive DNMT1 modulators. The best-performing ML model successfully classified the active and least-active DNMT1 inhibitors exhibiting 97% classification accuracy. Collectively, this study reveals the underlined epigenetic events responsible for -implicated breast cancer metastasis along with the classification of DNMT1 modulators that can potentially drive the perception of epigenetic-based tumor therapy.
已知乳腺癌的发生是由影响多个细胞信号级联反应的遗传和表观遗传修饰所引发的，因此其预防和治疗是一项具有挑战性的工作。然而，表观遗传修饰，特别是关键肿瘤抑制基因（TSG）的DNA甲基化介导的沉默，是癌症进展的一个标志。一种这样的肿瘤抑制基因（Runt相关转录因子3）在乳腺癌中是一个新的见解，已知由于DNA甲基转移酶1（DNMT1）介导的局部启动子高甲基化而被抑制。然而，导致癌症侵袭和转移的信号传导的表观遗传影响沉默的确切机制仍未得到充分表征。在本研究中，设计了一个生物调节网络（BRN）来模拟由其他调节因子如p21、c-myc和p53增强的DNMT1网络的动态。为此，应用勒内·托马斯定性建模来计算未知参数，随后的轨迹表示DNMT1网络的重要行为（即恢复周期、稳态和分叉状态）。结果，观察到生物系统由于癌基因c-myc的持续激活以及TSG的持续下调而侵袭癌症转移。相反，在没有c-myc和激活的TSG的情况下实现了稳态。此外，DNMT1被认可为潜在的表观遗传药物靶点，将应用机器学习技术对活性和非活性DNMT1调节剂进行分类。表现最佳的机器学习模型成功地对活性和活性最低的DNMT1抑制剂进行了分类，分类准确率为97%。总体而言，本研究揭示了与乳腺癌转移相关的潜在表观遗传事件，以及DNMT1调节剂的分类，这可能推动基于表观遗传的肿瘤治疗的认识。
