OncoTargets and Therapy (2021-05-01)

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

  • Li Q,
  • Jiang S,
  • Feng T,
  • Zhu T,
  • Qian B

Journal volume & issue
Vol. Volume 14
pp. 3119 – 3131

Abstract

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Qiang Li,1,2,* Sheng Jiang,3,* Tienan Feng,2 Tengteng Zhu,2 Biyun Qian2 1Public Health College, Shanghai Jiao Tong University of Medicine, Shanghai, 200025, People’s Republic of China; 2Hongqiao International Institute of Medicine, Shanghai Tongren Hospital/Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People’s Republic of China; 3The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tengteng Zhu; Biyun QianHongqiao International Institute of Medicine, Shanghai Tongren Hospital/Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, 227 Chongqing South Road, Shanghai, 200025, ChinaEmail [email protected]; [email protected]: The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial–mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.Methods: Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package “limma”. Then, the hub genes, which had a higher degree, were identified by the protein–protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.Results: A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P< 0.01) and ITGA2 expression (P< 0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.Conclusion: In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.Keywords: thyroid cancer, EMT, signature, bioinformatic analysis, immunohistochemistry

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