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Integrated Bioinformatics Analysis of DNA Methylation Biomarkers in Thyroid Cancer Based on TCGA Database
Biochemical Genetics ( IF 2.1 ) Pub Date : 2021-08-13 , DOI: 10.1007/s10528-021-10117-z
Lifeng Zhao 1 , Yuanyuan Jia 1 , Ying Liu 1 , Baoling Han 1 , Jian Wang 1 , Xia Jiang 1
Affiliation  

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.



中文翻译:

基于TCGA数据库的甲状腺癌DNA甲基化生物标志物综合生物信息学分析

以前的研究报道了一组异常的启动子甲基化变化,这些变化与甲状腺癌 (TC) 中肿瘤抑制基因的沉默相关,但这些单个基因的结果还远远不够。在这项工作中,我们旨在通过信息学分析研究 TC 进展过程中甲基化变化的发生和模式。我们从癌症基因组图谱下载了专注于 TC 的 DNA 甲基化和 RNA 测序数据集。对异常甲基化的差异表达基因 (DEG) 进行分类并分析通路。然后使用 KEGG 和 GO 对已识别的途径和基因进行富集和功能分析。应用基因药物相互作用网络和人类蛋白质图谱获得特征DNA甲基化生物标志物。我们总共鉴定了 2170 个甲基化驱动的 DEG,包括筛选步骤中的 1054 个高甲基化低表达 DEGs 和 1116 个低甲基化高表达 DEGs。进一步分析共筛选了八种特征 DNA 甲基化生物标志物(RXRG、MET、PDGFRA、FCGR3A、VEGFA、CSF1R、FCGR1A 和 C1QA)。通路分析表明,异常甲基化的 DEG 主要与癌症中的转录失调、MAPK 信号传导和 TC 中的内在凋亡信号传导有关。总之,我们已经确定了与 TC 相关的新的异常甲基化基因和途径,它们可能作为精确诊断和疾病治疗的新生物标志物。通路分析表明,异常甲基化的 DEG 主要与癌症中的转录失调、MAPK 信号传导和 TC 中的内在凋亡信号传导有关。总之,我们已经确定了与 TC 相关的新的异常甲基化基因和途径,它们可能作为精确诊断和疾病治疗的新生物标志物。通路分析表明,异常甲基化的 DEG 主要与癌症中的转录失调、MAPK 信号传导和 TC 中的内在凋亡信号传导有关。总之,我们已经确定了与 TC 相关的新的异常甲基化基因和途径,它们可能作为精确诊断和疾病治疗的新生物标志物。

更新日期:2021-08-19
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