ORIGINAL ARTICLE   

Minerva Endocrinologica 2020 December;45(4):295-305

DOI: 10.23736/S0391-1977.20.03252-6

Copyright © 2020 EDIZIONI MINERVA MEDICA

language: English

Codon bias analyses on thyroid carcinoma genes

Daniele PEPE ✉, Kim DE KEERSMAECKER

Department of Oncology, Laboratory for Disease Mechanisms in Cancer, Katholieke Universiteit (KU) Leuven, Leuven, Belgium

BACKGROUND: Thyroid carcinoma is one of the most common cancers in the world. Although the genetics of thyroid carcinoma was intensively studied, new mechanisms could be involved in its development as the codon bias. In this paper, we studied the codon bias of thyroid-cancer genes, considering not only the sequences but also the synonymous mutations.
METHODS: Different measures and statistical analyses were employed to characterize the thyroid-cancer genes. We considered classical measures as RSCU and ENC, the compositional and protein characteristics, but also the codon bias landscape via the %MinMax algorithm.
RESULTS: The compositional analyses highlighted two groups of thyroid cancer genes according to the GC% and GC3% content. The ENC did not show a clear codon bias in the genes. Differently, the RSCU analyses showed interesting codons that could play an important role in the development of thyroid cancer as the codon Ser-tcG. Furthermore, interesting synonymous mutations were detected that could affect the codon bias. The codon bias landscape detected genes enriched in rare codons as AKAP9 and KTN1. A cluster analysis based on %MinMax classified the thyroid cancer genes in four different groups according to the distribution of rare/frequent codons in the sequence.
CONCLUSIONS: This is the first study that analyzed the codon bias in thyroid cancer genes based also on synonymous mutations. This study provided different hints that should be further investigated by wet-lab validation and that it could open new scenarios in the understanding the molecular mechanisms involved in thyroid cancer development based on codon bias.

KEY WORDS: Codon usage; Silent mutation; Thyroid neoplasms