Abstract
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.
Highlights
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Most mutations which occur in Thyroid cancer (TC) affect the MAPK or PI3K–AKT pathways, for example point mutations in BRAF (BRAFV600E) or RAS, which are fundamental for TC initiation and progression [8]
Some studies using a next-generation sequencing approach showed that Anaplastic thyroid carcinoma (ATC) are characterized by the accumulation of several different oncogenic alterations [3,9]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. To understand genetic interactions and assess gene networks in ATC, deep neural network methods for the reconstruction of GRNs may be an effective tool [14]. Knockdown of MALAT1 inhibited cell proliferation and invasion of human thyroid tumor cell lines [25].
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