A novel multimodal prediction model based on DNA methylation biomarkers and low-dose computed tomography images for identifying early-stage lung cancer.

IF 7 2区 医学 Q1 ONCOLOGY
Jing Zhang, Haohua Yao, Chunliu Lai, Xue Sun, Xiujuan Yang, Shurong Li, Yubiao Guo, Junhang Luo, Zhihua Wen, Kejing Tang
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引用次数: 0

Abstract

Objective: DNA methylation alterations are early events in carcinogenesis and immune signalling in lung cancer. This study aimed to develop a model based on short stature homeobox 2 gene (SHOX2)/prostaglandin E receptor 4 gene (PTGER4) DNA methylation in plasma, appearance subtype of pulmonary nodules (PNs) and low-dose computed tomography (LDCT) images to distinguish early-stage lung cancers.

Methods: We developed a multimodal prediction model with a training set of 257 individuals. The performance of the multimodal prediction model was further validated in an independent validation set of 42 subjects. In addition, we explored the association between SHOX2/PTGER4 DNA methylation and driver gene mutations in lung cancer based on data from The Cancer Genome Atlas (TCGA) portal.

Results: There were significant differences between the early-stage lung cancers and benign groups in the methylation levels. The area under a receiver operator characteristic curve (AUC) of SHOX2 in patients with solid nodules, mixed ground-glass opacity nodules and pure ground-glass opacity nodules were 0.693, 0.497 and 0.864, respectively, while the AUCs of PTGER4 were 0.559, 0.739 and 0.619, respectively. With the highest AUC of 0.894, the novel multimodal prediction model outperformed the Mayo Clinic model (0.519) and LDCT-based deep learning model (0.842) in the independent validation set. Database analysis demonstrated that patients with SHOX2/PTGER4 DNA hypermethylation were enriched in TP53 mutations.

Conclusions: The present multimodal prediction model could more efficiently distinguish early-stage lung cancer from benign PNs. A prognostic index based on DNA methylation and lung cancer driver gene alterations may separate the patients into groups with good or poor prognosis.

基于DNA甲基化生物标志物和低剂量计算机断层扫描图像的新型多模态预测模型用于识别早期肺癌。
目的:DNA甲基化改变是肺癌癌变和免疫信号传导的早期事件。本研究旨在建立一种基于血浆、肺结节(PNs)外观亚型和低剂量计算机断层扫描(LDCT)图像中矮个子同源盒2基因(SHOX2)/前列腺素E受体4基因(PTGER4) DNA甲基化的模型来区分早期肺癌。方法:建立了一个包含257个个体的多模态预测模型。在42名受试者的独立验证集中进一步验证了多模态预测模型的性能。此外,基于癌症基因组图谱(TCGA)门户网站的数据,我们探讨了肺癌中SHOX2/PTGER4 DNA甲基化与驱动基因突变之间的关系。结果:早期肺癌组与良性肺癌组在甲基化水平上存在显著差异。实性结节、混合性磨玻璃混浊结节和单纯磨玻璃混浊结节患者SHOX2的受试者算子特征曲线下面积(AUC)分别为0.693、0.497和0.864,PTGER4的AUC分别为0.559、0.739和0.619。该模型的AUC最高为0.894,在独立验证集中优于Mayo Clinic模型(0.519)和基于ldct的深度学习模型(0.842)。数据库分析表明,SHOX2/PTGER4 DNA高甲基化的患者中TP53突变富集。结论:建立的多模态预测模型能更有效地区分早期肺癌和良性PNs。基于DNA甲基化和肺癌驱动基因改变的预后指标可以将患者分为预后好或预后差的组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
自引率
9.80%
发文量
1726
审稿时长
4.5 months
期刊介绍: Chinese Journal of Cancer Research (CJCR; Print ISSN: 1000-9604; Online ISSN:1993-0631) is published by AME Publishing Company in association with Chinese Anti-Cancer Association.It was launched in March 1995 as a quarterly publication and is now published bi-monthly since February 2013. CJCR is published bi-monthly in English, and is an international journal devoted to the life sciences and medical sciences. It publishes peer-reviewed original articles of basic investigations and clinical observations, reviews and brief communications providing a forum for the recent experimental and clinical advances in cancer research. This journal is indexed in Science Citation Index Expanded (SCIE), PubMed/PubMed Central (PMC), Scopus, SciSearch, Chemistry Abstracts (CA), the Excerpta Medica/EMBASE, Chinainfo, CNKI, CSCI, etc.
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