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Updated: Dec 3, 2025
Spontaneous Murine Model of Anaplastic Thyroid Cancer
Published on: February 3, 2023
Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis
Shengnan Wang1,2, Jing Wu1, Congcong Guo3
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.
Purpose:
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.
Insights
This study identified key genes in anaplastic thyroid carcinoma (ATC) by analyzing microarray data. CENPF, KIF2C, and TPX2 show potential as therapeutic targets for improving ATC patient survival.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Anaplastic thyroid carcinoma (ATC) has a poor prognosis despite conventional treatments like surgery, chemotherapy, and radiotherapy.
- Identifying novel therapeutic targets is crucial for improving survival rates in ATC patients.
Purpose of the Study:
- To identify differentially expressed genes (DEGs) between ATC and normal thyroid tissue using bioinformatics analysis.
- To discover potential therapeutic targets for ATC by analyzing gene expression profiles.
Main Methods:
- Downloaded and analyzed multiple microarray datasets (GSE9115, GSE29265, GSE33630, GSE53072, GSE65144) from the Gene Expression Omnibus (GEO) database.
- Utilized GEO2R for DEG screening, STRING database for protein-protein interaction (PPI) network analysis, and Cytoscape for hub gene identification.
- Performed survival analysis using Kaplan-Meier plotter and validated key gene expression using RT-qPCR.
Main Results:
- Identified 284 overlapping DEGs (121 upregulated, 161 downregulated) between ATC and normal thyroid tissue.
- Selected 50 hub genes from the PPI network and identified ANLN, CENPF, KIF2C, TPX2, and NDC80 as significantly correlated with patient survival.
- RT-qPCR confirmed upregulation of KIF2C and CENPF in ATC cell lines, with TPX2 also upregulated in one cell line.
Conclusions:
- CENPF, KIF2C, and TPX2 are suggested as significant players in ATC development.
- These genes hold potential as novel biomarkers for ATC treatment and warrant further investigation.
