Laboratory of Human Thyroid Cancers Preclinical and Translational Research, Division of Experimental Pathology, Cancer Research Institute (CRI), Cancer Center, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
Department of Pathology, Center for Vascular Biology Research (CVBR), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
Molecules. 2021 May 19;26(10):3022. doi: 10.3390/molecules26103022.
Thyroid cancer (TC) is the most common endocrine malignancy. Most TCs have a favorable prognosis, whereas anaplastic thyroid carcinoma (ATC) is a lethal form of cancer. Different genetic and epigenetic alterations have been identified in aggressive forms of TC such as ATC. Non-coding RNAs (ncRNAs) represent functional regulatory molecules that control chromatin reprogramming, including transcriptional and post-transcriptional mechanisms. Intriguingly, they also play an important role as coordinators of complex gene regulatory networks (GRNs) in cancer. GRN analysis can model molecular regulation in different species. Neural networks are robust computing systems for learning and modeling the dynamics or dependencies between genes, and are used for the reconstruction of large data sets. Canonical network motifs are coordinated by ncRNAs through gene production from each transcript as well as through the generation of a single transcript that gives rise to multiple functional products by post-transcriptional modifications. In non-canonical network motifs, ncRNAs interact through binding to proteins and/or protein complexes and regulate their functions. This article overviews the potential role of ncRNAs GRNs in TC. It also suggests prospective applications of deep neural network analysis to predict ncRNA molecular language for early detection and to determine the prognosis of TC. Validation of these analyses may help in the design of more effective and precise targeted therapies against aggressive TC.
甲状腺癌（TC）是最常见的内分泌恶性肿瘤。大多数 TC 预后良好，而间变性甲状腺癌（ATC）是一种致命的癌症形式。在侵袭性 TC 如 ATC 中已经确定了不同的遗传和表观遗传改变。非编码 RNA（ncRNA）是控制染色质重编程的功能性调节分子，包括转录和转录后机制。有趣的是，它们在癌症中作为复杂基因调控网络（GRN）的协调因子也发挥着重要作用。GRN 分析可以模拟不同物种的分子调控。神经网络是用于学习和建模基因之间动态或依赖性的强大计算系统，并且用于重建大型数据集。规范网络基元通过从每个转录本产生基因产物以及通过转录后修饰产生多个功能产物的单个转录本来由 ncRNA 协调。在非规范网络基元中，ncRNA 通过与蛋白质和/或蛋白质复合物结合来相互作用，并调节它们的功能。本文综述了 ncRNA-GRN 在 TC 中的潜在作用。它还提出了深度神经网络分析在预测 ncRNA 分子语言以进行早期检测和确定 TC 预后方面的潜在应用。这些分析的验证可能有助于设计针对侵袭性 TC 的更有效和精确的靶向治疗。
