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Potential polymorphisms of relationship with prognosis of metastatic Thyroid Cancer: a computational analysis of the genes FBXO28, PVRL1, SNN e ITGA3

Grant number: 21/06013-3
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): November 01, 2021
Effective date (End): October 31, 2022
Field of knowledge:Interdisciplinary Subjects
Principal researcher:Laura Sterian
Grantee:Atena Saito Góes
Home Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Thyroid nodules can be detected in more than 65% of the population and their incidence has grown 20% in recent years, although the mortality rate associated with malignant nodules, which accounts for less than 10% of all detected nodules, remains stable. Current guidelines suggest less invasive procedures and even active surveillance in low-risk cases. Unfortunately, a significant percentage of patients with differentiated thyroid carcinomas (DTC) can evolve with lymph node metastasis (LNM) and, in these cases, the study of biomarkers that allow the identification of predisposition to present LNM is fundamental in predicting prognosis and, therefore, in the proper management of patients. Using online databases, a previous study by our research group identified differentially expressed microRNAs (miRs) associated with LNM in malignant thyroid nodules: miR-199a-3p, miR-199a-5p, and miR-148a-5p. According to MirWalk, FBXO28, PVRL1, SNN, and ITGA3 are predicted target genes of this miRs. Those genes participate in cellular processes, especially apoptosis, proliferation, and migration. This study aims to investigate, by bioinformatics tools, alterations in the proteins encoded by these genes due to the presence of polymorphisms (SNPs) and their possible implications in the incidence of LNM. Morph functional analysis of altered proteins will be performed through PredictSNP1.0, together with the study of these proteins with the aid of PROVEAN, MuPRO, I-MUTANT2.0, and ModPre tools to analyze protein function and stability, as well as post-translational changes. Based on this information, we hope to identify the SNPs of most impact in FBXO28, PVRL1, SNN, and ITGA3 genes, so as to discuss whether the changes caused by the SNPs have a role in the susceptibility and prognosis of thyroid cancer. A further study should validate these SNPs in samples available at GEMOCA Biorepository and compare the results obtained with the clinical evolution of the cases, in order to confirm the potential value of diagnostic and prognostic biomarkers in the thyroid nodules. (AU)

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