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Comprehensive DNA Methylation Profiling of Medullary Thyroid Carcinoma: Molecular Classification, Potential Therapeutic Target, and Classifier System

DNA甲基化 甲基化 甲状腺癌 计算生物学 甲状腺髓样癌 表观遗传学 肿瘤科 医学 生物 生物信息学 癌症研究 甲状腺 内科学 遗传学 基因表达 基因
作者
Cenkai Shen,Xiao Shi,Duo Wen,Yuqing Zhang,Yuxin Du,Yu Zhang,Ben Ma,Haitao Tang,Min Yin,Naisi Huang,Tian Liao,Ting-Ting Zhang,Chang’e Kong,Wenjun Wei,Qinghai Ji,Yu Wang
出处
期刊:Clinical Cancer Research [American Association for Cancer Research]
卷期号:30 (1): 127-138 被引量:6
标识
DOI:10.1158/1078-0432.ccr-23-2142
摘要

Abstract Purpose: Medullary thyroid carcinoma (MTC) presents a distinct biological context from other thyroid cancers due to its specific cellular origin. This heterogeneous and rare tumor has a high prevalence of advanced diseases, making it crucial to address the limited therapeutic options and enhance complex clinical management. Given the high clinical accessibility of methylation information, we construct the largest MTC methylation cohort to date. Experimental Design: Seventy-eight fresh-frozen MTC samples constituted our methylation cohort. The comprehensive study process incorporated machine learning, statistical analysis, and in vitro experiments. Results: Our study pioneered the identification of a three-class clustering system for risk stratification, exhibiting pronounced epigenomic heterogeneity. The elevated overall methylation status in MTC-B, combined with the “mutual exclusivity” of hypomethylated sites displayed by MTC-A and MTC-C, distinctively characterized the MTC-specific methylation pattern. Integrating with the transcriptome, we further depicted the features of these three clusters to scrutinize biological properties. Several MTC-specific aberrant DNA methylation events were emphasized in our study. NNAT expression was found to be notably reduced in poor-prognostic MTC-C, with its promoter region overlapping with an upregulated differentially methylated region. In vitro experiments further affirmed NNAT's therapeutic potential. Moreover, we built an elastic-net logistic regression model with a relatively high AUC encompassing 68 probes, intended for future validation and systematic clinical application. Conclusions: Conducting research on diseases with low incidence poses significant challenges, and we provide a robust resource and comprehensive research framework to assist in ongoing MTC case inclusion and facilitate in-depth dissection of its molecular biological features.
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