Abstract
BackgroundAberrant DNA methylation plays an crucial role in tumorigenesis through regulating gene expression. Nevertheless, the exact role of methylation in the carcinogenesis of thyroid cancer and its association with prognosis remains unclear. The purpose of this study is to explore the DNA methylation-driven genes in thyroid cancer by integrative bioinformatics analysis.MethodsThe transcriptome profiling data and DNA methylation data of thyroid cancer were downloaded from The Cancer Genome Atlas (TCGA) database. The methylmix R package was used to screen DNA methylation-driven genes in thyroid cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to annotate the function of methylation-driven genes. Univariate Cox regression analyses was performed to distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses was utilized to build a prognostic multi-gene signature. A survival analysis was carried out to determine the individual prognostic significance of this multi-gene signature.ResultsA total of 51 methylation-driven genes were identified. The functional analysis indicated that these genes were significantly enriched in diverse biological processes (BP) and pathways related to the malignancy processes. Four of these genes (RDH5, TREM1, BIRC7, and SLC26A7) were selected to construct the risk evaluation model. Patients in the low-risk group had an conspicuously better overall survival (OS) than those in high-risk group (p < 0.001). The area under the receiver operating characteristic (ROC) curve for this model was 0.836, suggesting a good specificity and sensitivity. Subsequent survival analysis revealed that this four-gene signature served as an independent indicator for the prognosis of thyroid cancer. Moreover, the prognostic signature was well validated in a external thyroid cancer cohort.ConclusionWe identified methylation-driven genes in thyroid cancer with independent prognostic value, which may offer new insight into molecular mechanisms of thyroid cancer and provide new possibility for individualized treatment of thyroid cancer patients.
Highlights
The incidence of thyroid cancer has increased rapidly in the United States over the last four decades (Lim et al, 2017; Bray et al, 2018)
The available RNA-seq transcriptome data, DNA methylation data, and clinicopathological information of thyroid carcinoma were downloaded from the TCGA database1
DNA methylation data were extracted from 570 thyroid carcinoma specimens, including 56 normal samples and 514 tumor samples
Summary
The incidence of thyroid cancer has increased rapidly in the United States over the last four decades (Lim et al, 2017; Bray et al, 2018). As the fifth most commonly diagnosed cancer in women, thyroid cancer accounts for 40900 new cases estimated by the latest cancer statistic report in the United States (Siegel et al, 2018) This is driven largely by increasing prevalence of papillary thyroid cancer (PTC) which is identified as the most common and least aggressive histologic type in thyroid cancer (Lim et al, 2017). Accumulating evidence have indicated that aberrant DNA methylation occurred on CpG islands of promoters is capable of regulating expression of many tumor-associated genes and is critical for cancer development (Ferry et al, 2017; Zheng et al, 2017). Aberrant DNA methylation plays an crucial role in tumorigenesis through regulating gene expression. The purpose of this study is to explore the DNA methylation-driven genes in thyroid cancer by integrative bioinformatics analysis
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