Genes and comorbidities of thyroid cancer

Branimir Ljubic, Martin Pavlovski, Shoumik Roychoudhury, Christophe Marc Van Neste, Adil Salhi, Magbubah Essack, Vladimir B. Bajic, Zoran Obradovic

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Introduction: Thyroid cancer represents 3.1 % of diagnosed cancers in the United States. The objective of this research was to identify comorbidities and discover additional genes potentially related to thyroid cancer and improve current knowledge of genetics and comorbidities associated with this cancer. Methods: Healthcare Cost and Utilization Project (HCUP) California State Inpatient Database (SID) was used to extract and rank the comorbidities of thyroid cancer. The text mining software - BeFree was utilized to identify and extract genes associated with thyroid cancer and the comorbidities from PubMed abstracts and the DisGeNET expert-curated repositories. Results: Female patients had 4,485, and male patients 2912 different comorbidities in early stages of thyroid cancer. Females had 3,587, and males 2817 different comorbidities in advanced stages. Through PubMed utilizing the BeFree method, 504 different genes associated with thyroid cancer were discovered, as well as five genes on DisGeNET. The most often genes on PubMed, associated with thyroid cancer were: BRAF, RET, SLC5A5, RAS, and PTEN. Genes found via DisGeNET were BRAF, RET, KRAS, NRAS, and PRKAR1A. Conclusion: Identified genes and comorbidities, as potential additional risk factors for thyroid cancer, not previously known, could improve the early diagnosis and the survival of patients with thyroid cancer. Genes discovered in this research in association with thyroid cancer could be used to direct decision making for optimal, more personalized treatment of thyroid cancer.
Original languageEnglish (US)
Pages (from-to)100680
JournalInformatics in Medicine Unlocked
Volume25
DOIs
StatePublished - Jul 26 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-08-06
Acknowledged KAUST grant number(s): BAS/1/1606-01-01, FCC/1/1976-24-01
Acknowledgements: The authors gratefully acknowledge the support of King Abdullah University of Science and Technology (KAUST), Office of Sponsored Reaserch (OSR) Awards no. OSR-2018-CPF-3657-03 and FCC/1/1976-24-01, and KAUST Baseline Research Fund (BAS/1/1606-01-01). Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality provided data used in this study. We did not need the approval of the Ethics Committee for this study, since we used the publicly available dataset (HCUP) and we followed guidance for the use of that particular database.

Cite this