Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis

Abstract

Shengnan Wang,1,2,* Jing Wu,1,* Congcong Guo,3 Hongxia Shang,4 Jinming Yao,4 Lin Liao,4,5 Jianjun Dong6 1Laboratory of Endocrinology, Medical Research Center, Shandong Provincial Qianfoshan Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, People’s Republic of China; 2Department of Occupational Disease, Yantai Shan Hospital, Yantai, People’s Republic of China; 3Department of Endocrinology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, People’s Republic of China; 4Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shandong First Medical University, Jinan, People’s Republic of China; 5Department of Endocrinology and Metabology, Shandong Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 6Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lin LiaoDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Shandong First Medical University, Jinan 250014, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected] DongDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected]: The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC.Methods: Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P‐values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score > 0.4 as the cut-off criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan–Meier plotter from those hub genes and validated by RT-qPCR.Results: A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan–Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3– 1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells.Conclusion: Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.Keywords: anaplastic thyroid carcinoma, bioinformatics analysis, Gene Expression Omnibus database, differential expressed genes

Keywords

• anaplastic thyroid carcinoma
• bioinformatics analysis
• gene expression omnibus database
• differential expressed genes