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.
Keywords: bioinformatics; follicular adenoma; microRNA; target gene; thyroid cancer.
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