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Genetic profile identified in thyroid tumors affect tumor cell metabolism?

Abstract

Our group identified a new mutation in exon 8 (G533C) of the RET gene in a large family with NEM 2A (Multiple Endocrine Neoplasia type 2A). In this family, we evaluated 728 individuals, 119 being carriers of the G533C mutation. Of these, 46 presented medullary thyroid carcinoma (CMT) and only 1 individual developed pheochromocytoma (FEO). Considering the high prevalence of OVD in Greek and American families with the G533C mutation, compared with the low prevalence observed in our study, we suggest that polymorphisms in the RET gene, modifying genes or environmental factors may affect the penetrance of the disease. In addition, we identified families with a new mutation in the RET gene (M918V), as well as 5 families with double mutations (C634Y / Y791F) in this gene. It is still unclear in the literature the effect of different mutations in the RET gene on the biological behavior of the tumor, especially of these new mutations, or double mutations in the RET gene. We know that mutations in genes associated with the development of cancer (drivers) promote, directly or indirectly, a reprogramming of cellular metabolism, to acquire nutrients necessary for cellular viability. Thus, it would be essential to evaluate the cellular metabolism after ectopic expression of each of these mutations.  Parallel to this project, our group has sought molecular markers that can be used in the preoperative diagnosis of thyroid nodules. CONCLUSIONS: We identified differentially expressed transcripts (P <0.001) between the benign thyroid follicular adenoma and the malignant thyroid follicular carcinoma. Validation data by qPCR expression and immunohistochemistry have confirmed that these markers (DDIT3, ITM1, C1orf24 and PVALB) may aid in the diagnosis of thyroid nodules. We later demonstrated by ectopic expression of PVALB that this gene changes the intracellular calcium flux, as well as the number and morphology of the mitochondria, suggesting changes in cellular homeostasis and in the metabolic profile of the cells. This proposal presents two subprojects that predict the metabolic profile of normal cells with expression of different mutations in the RET gene identified by our group (SUBPROJECT 1), and the metabolic profile of thyroid carcinoma cells after ectopic expression of the PVALB gene (SUBPROJECT 2). Comparative analysis of the cell metabolome with the ectopic expression of different mutations in the RET gene may provide important data about the role of these mutations in the phenotype of a cell. Similarly, the characterization of metabolites after ectopic expression of the PVALB gene will provide additional information that will allow to determine with more precision the function of this gene in the pathogenesis of thyroid tumors. Furthermore, we propose to evaluate the genetic or epigenetic mechanism associated with differential expression of the PVALB gene in Hürthle cell tumors. It is an INNOVATIVE proposal, whose results can help to determine the function of these genes, as well as to identify metabolites that can be identified in biofluids and, thus, help in the diagnosis, follow-up and therapy of patients. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
COLOZZA-GAMA, GABRIEL A.; CALLEGARI, FABIANO; BESIC, NIKOLA; PAVIZA, ANA C. DE J.; CERUTTI, JANETE M. Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples. SCIENTIFIC REPORTS, v. 11, n. 1 JUN 16 2021. Web of Science Citations: 1.

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