Integrated Bioinformatics Analysis of Key Genes Involved in Progression of Papillary and Follicular Thyroid Cancer
Aisha Ibrahim Usman
School of Clinical Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, 130021, China.
Dariga Boken
School of Clinical Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, 130021, China.
Siqi Liu
Precision Medicine Center Jilin Province People’s Hospital, Changchun, Jilin, 130021, China.
Musa Usman Ibrahim
Department of Physiotherapy, Aminu Kano Teaching Hospital, Kano, 700101, Nigeria.
Xiaodan Lu
Precision Medicine Center Jilin Province People’s Hospital, Changchun, Jilin, 130021, China.
Jingbin Zhang *
School of Clinical Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, 130021, China and Jilin Province People's Hospital, Changchun, Jilin, 130021, China.
Mohammed Sharif Swallah *
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, 230031, China.
*Author to whom correspondence should be addressed.
Abstract
Background: Thyroid cancer, the most common endocrine neoplasia, has seen a rapid increase in global incidence, with the USA reporting over 62,000 new cases in 2015, making it the fifth most common cancer among women. Differentiated thyroid cancer accounts for more than 95% of cases and arises from thyroid follicular epithelial cells, primarily manifesting as papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC). Despite significant advancements in understanding the molecular mechanisms underlying these cancers, the identification of relevant genes for a comprehensive view of tumorigenesis remains essential.
Methods: In this study, we conducted an integrated analysis utilizing bioinformatics approaches, including gene set enrichment analysis, gene ontology (GO) analysis, KEGG pathway analysis, and survival analysis, genetic mutations, to identify key genes associated with the development of thyroid cancer.
Results: Our candidate genes were validated using data from The Cancer Genome Atlas (TCGA) and The Human Protein Atlas. Our results identified ALB, FN1, MYC, and IL6 as significant prognostic markers in both papillary and follicular thyroid cancer, underscoring their potential as important biomarkers for improved diagnosis and therapeutic strategies.
Conclusion: ALB, FN1, MYC, and IL6 may serve as key genes for both papillary and follicular thyroid cancer. Additional molecular biology experiments are necessary to validate the functions of these identified genes.
Keywords: Network pharmacology, bioinformatic analysis, thyroid carcinoma, TCGA