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Integrated Bioinformatics Analysis of DNA Methylation Biomarkers in Thyroid Cancer Based on TCGA Database

生物 DNA甲基化 甲基化 基因 小桶 遗传学 癌症研究 计算生物学 基因表达 转录组
作者
Lifeng Zhao,Yuanyuan Jia,Ying Liu,Baoling Han,Jian Wang,Xia Jiang
出处
期刊:Biochemical Genetics [Springer Science+Business Media]
卷期号:60 (2): 629-639 被引量:5
标识
DOI:10.1007/s10528-021-10117-z
摘要

Previous studies have reported a cluster of aberrant promoter methylation changes associated with silencing of tumor suppressor genes in thyroid cancer (TC), but these results of individual genes are far from enough. In this work, we aimed to investigate the onset and pattern of methylation changes during the progression of TC by informatics analysis. We downloaded the DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas focusing on TC. Abnormally methylated differentially expressed genes (DEGs) were sorted and pathways were analyzed. The KEGG and GO were then used to perform enrichment and functional analysis of identified pathways and genes. Gene-drug interaction network and human protein atlas were applied to obtain feature DNA methylation biomarkers. In total, we identified 2170 methylation-driven DEGs, including 1054 hypermethylatedlow-expression DEGs and 1116 hypomethylated-high-expression DEGs at the screening step. Further analysis screened total of eight feature DNA methylation biomarkers (RXRG, MET, PDGFRA, FCGR3A, VEGFA, CSF1R, FCGR1A and C1QA). Pathway analysis showed that aberrantly methylated DEGs mainly associated with transcriptional misregulation in cancer, MAPK signaling, and intrinsic apoptotic signaling in TC. Taken together, we have identified novel aberrantly methylated genes and pathways linked to TC, which might serve as novel biomarkers for precision diagnosis and disease treatment.
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