Open Access

ARTICLE

Identifying Driver Genes Mutations with Clinical Significance in Thyroid Cancer

Hyeong Won Yu¹, Muhammad Afzal², Maqbool Hussain², Hyungju Kwon³, Young Joo Park⁴, June Young Choi^1,*, Kyu Eun Lee⁵

1 Department of Surgery, Seoul National University Bundang Hospital, Seongnam-si, Korea
2 Department of Software, Sejong University, Seoul, Korea
3 Department of Surgery, College of Medicine, Ewha Womans University, Seoul, Korea
4 Department of Internal Medicine, Seoul National University Hospital and College of Medicine, Seoul, Korea
5 Department of Surgery, Seoul National University Hospital and College of Medicine, Seoul, Korea

* Corresponding Author: June Young Choi. Email:

(This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 67(1), 1241-1251. https://doi.org/10.32604/cmc.2021.014910

Received 26 October 2020; Accepted 30 November 2020; Issue published 12 January 2021

Abstract

Advances in technology are enabling gene mutations in papillary thyroid carcinoma (PTC) to be analyzed and clinical outcomes, such as recurrence, to be predicted. To date, the most common genetic mutation in PTC is in BRAF kinase (BRAF). However, whether mutations in other genes coincide with those in BRAF remains to be clarified. The aim of this study was to find mutations in other genes that co-exist with mutated BRAF, and to analyze their frequency and clinical relevance in PTC. Clinical and genetic data were collected from 213 PTC patients with a total of 36,572 mutation sites in 735 genes. After matching with genes from PTC entries in a global database (NCBI Gene), 69 genes with mutations in coding regions were chosen for further study. Through frequency-based analysis, we identified commonly mutated genes co-existing with mutated BRAF and, using the mutation count correlation matrix (MCCM) method, analyzed their incidence according to age and gender. We designed Chord diagrams to reveal gene relationships concerning age and gender, and found that mutations in ALK, ATM, COL1A1, MSTIR, PRKCA, and WNK1 most commonly coincide with mutated BRAF, followed by APC, AURKA, and AURKB. These findings provide further insight into the genetic profile of PTC.

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Keywords

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