Search for New Target Genes of MicroRNA for Differential Diagnosis of Benign and Malignant Neoplasms of the Thyroid Gland by In Silico Methods

Search for New Target Genes of MicroRNA for Differential Diagnosis of Benign and Malignant Neoplasms of the Thyroid Gland by In Silico Methods

Differential diagnosis of thyroid gland neoplasms is an urgent problem in modern oncothyroidology. This is especially true for the diagnosis of follicular thyroid cancer and follicular thyroid adenoma at the preoperative stage. In this study, in silico methods were used to search for potential marke...

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Bibliographic Details
Published in:Bulletin of experimental biology and medicine Vol. 173; no. 2; pp. 246 - 251
Main Authors: Kononchuk, V. V., Kopeikina, E. V., Kalinina, T. S., Saik, O. V., Alekseenok, E. Yu, Kolyagina, M. K., Matashova, V. A., Shevchenko, S. P., Gulyaeva, L. F., Kushlinskii, N. E.
Format: Journal Article
Language:English
Published: New York Springer US 01-06-2022
Springer
Springer Nature B.V
Subjects:
Adenoma
Analysis
Anopheles
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Cancer
Cell Biology
Differential diagnosis
EGR-1 protein
ErbB-3 protein
Fibroblast growth factor receptor 1
Genes
Genetic aspects
Internal Medicine
Laboratory Medicine
Methods
MicroRNA
MicroRNAs
miRNA
Neoplasia
Pathology
Polo-like kinase 1
Thyroid cancer
Thyroid diseases
Thyroid gland
Tumors
microRNA
thyroid cancer
follicular adenoma
target gene
bioinformatics
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Summary:Differential diagnosis of thyroid gland neoplasms is an urgent problem in modern oncothyroidology. This is especially true for the diagnosis of follicular thyroid cancer and follicular thyroid adenoma at the preoperative stage. In this study, in silico methods were used to search for potential markers that are microRNA target genes. A list of 19 microRNAs was compiled, the expression of which varies depending on the type of thyroid neoplasms. For these microRNAs, the target genes were selected considering tissue specificity and association with thyroid diseases. We selected 9 target genes ( MCM2 , RASSF2 , SPAG9 , SSTR2 , TP53BP1 , INPP4B , CCDC80 , GNAS , and PLK1 ), which can be considered as promising markers according to published data. Also, 6 new potential markers ( CDK4 , FGFR1 , ERBB3 , EGR1 , MYLK , and SRC ) were found, which make it possible to distinguish between follicular thyroid cancer and follicular thyroid adenoma. The proposed algorithm using various bioinformatics tools allows us to identify potential markers for the differential diagnosis of thyroid neoplasms.
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ISSN:0007-4888
1573-8221
DOI:10.1007/s10517-022-05527-x