Genes and comorbidities of thyroid cancer

Informatics in Medicine Unlocked

Magbubah Essack¹  Martin Pavlovski²  Adil Salhi³  Zoran Obradovic⁴  Vladimir B. Bajic⁴  Christophe Van Neste⁵  Branimir Ljubic⁵  Shoumik Roychoudhury⁵ 

[1] King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Thuwal, Saudi Arabia;Rutgers University, the Office of Advanced Research Computing (OARC), Piscataway, NJ, 08854, USA;Center for Medical Genetics (CMGG), Ghent University, Ghent, Belgium;King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Thuwal, Saudi Arabia;Temple University, Center for Data Analytics and Biomedical Informatics (DABI), Philadelphia, PA, 19122, USA;

关键词: Thyroid cancer;    Comorbidities;    Genes;    Text mining;    Biomedical informatics;    DisGeNET;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown