The Integrated Analyses of Driver Genes Identify Key Biomarkers in Thyroid Cancer

Overview

Journal Technol Cancer Res Treat

Specialties Biomedical Engineering
Oncology
Pharmacology

Date 2020 Aug 20

PMID 32812852

Citations 4

Authors

Qili Xu

Aili Song

Qigui Xie

Affiliations

Soon will be listed here.

Abstract

Aim: Thyroid cancer is the most common endocrine cancer, the incidence rate has continuously increased worldwide. However, there are still lack of effective molecular biomarkers for the diagnosis and treatment of the disease. The study was conducted to identify driver genes that may serve as potential biomarkers for the disease.

Methods: The computational tools oncodriveCLUST, oncodriveFM, icages and drgap were used to detect driver genes in thyroid cancer using somatic mutations from The Cancer Genome Atlas database. Integrated analyses were performed on the driver genes using multiomics data from the TCGA database.

Results: A set of 291 driver genes were identified in thyroid cancer. were the top 5 frequently mutated genes in thyroid cancer. The weighted gene co-expression network analysis identified 4 coexpression modules. The modules 1-3 were significantly associated with patients' tumor size, residual tumor, cancer stage, distant metastasis and multifocality. and were the hub genes in the modules 1-3 respectively. Hierarchical clustering analysis of the 20 driver genes with the most frequent copy number changes revealed 3 clusters of PRAD patients. Cluster 1 tumors exhibited significantly older age, tumor size, cancer stages, and poorer prognosis than cluster 2 and 3 tumors. 16 genes were significantly associated with number of lymph nodes, tumor size and pathologic stage, such as and

Conclusions: The set of cancer genes and subgroups of patients shed insight on the tumorigenesis of thyroid cancer and open up avenues for developing prognostic biomarkers and driver gene-targeted therapies in thyroid cancer.

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