Combining methylated RNF180 and SFRP2 plasma biomarkers for noninvasive diagnosis of gastric cancer

IF 5 2区 医学 Q2 Medicine
Zhihao Dai , Jin Jiang , Qianping Chen , Minghua Bai , Quanquan Sun , Yanru Feng , Dong Liu , Dong Wang , Tong Zhang , Liang Han , Litheng Ng , Jun Zheng , Hao Zou , Wei Mao , Ji Zhu
{"title":"Combining methylated RNF180 and SFRP2 plasma biomarkers for noninvasive diagnosis of gastric cancer","authors":"Zhihao Dai ,&nbsp;Jin Jiang ,&nbsp;Qianping Chen ,&nbsp;Minghua Bai ,&nbsp;Quanquan Sun ,&nbsp;Yanru Feng ,&nbsp;Dong Liu ,&nbsp;Dong Wang ,&nbsp;Tong Zhang ,&nbsp;Liang Han ,&nbsp;Litheng Ng ,&nbsp;Jun Zheng ,&nbsp;Hao Zou ,&nbsp;Wei Mao ,&nbsp;Ji Zhu","doi":"10.1016/j.tranon.2024.102190","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><div>Gastric cancer (GC) is a common malignant tumor, and early diagnosis significantly improves patient survival rates. This study aimed to investigate the diagnostic value of ring finger protein 180 (<em>RNF180</em>) and secreted frizzled protein 2 (<em>SFRP2</em>) in GC.</div></div><div><h3>Materials &amp; Methods</h3><div>A total of 165 healthy individuals, 34 patients with precancerous gastric lesions, and 104 patients with confirmed GC were divided into training and validation sets; methylated <em>RNF180</em> and <em>SFRP2</em> were detected in circulating DNA from blood samples. Six models, including those based on logistic regression, Naive Bayes, K-nearest neighbor algorithm, glmnet, neural network, and random forest (RF) were built and validated. Area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were determined.</div></div><div><h3>Results</h3><div>In the training set, the RF model with <em>RNF180</em> and <em>SFRP2</em> (R + S) had an AUC of 0.839 (95 % CI: 0.727–0.951), sensitivity of 60.3 %, and specificity of 85.5 % for diagnosing GC. The RF model with R + S+ Tumor markers had an AUC of 0.849 (95 % CI: 0.717–0.981), sensitivity of 62.8 %, and specificity of 87.1 %. In the validation set, the RF model with R + S had an AUC of 0.844 (95 % CI: 0.774–0.923), sensitivity of 87.8 %, and specificity of 69.2 %. The RF model with R + S + Tumor markers had an AUC of 0.858 (95 % CI: 0.781–0.939), sensitivity of 85.4 %, and specificity of 76.9 %.</div></div><div><h3>Conclusion</h3><div>Our results suggest that <em>RNF180</em> and <em>SFRP2</em> could serve as diagnostic biomarkers for GC when using the RF model.</div></div>","PeriodicalId":48975,"journal":{"name":"Translational Oncology","volume":"51 ","pages":"Article 102190"},"PeriodicalIF":5.0000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1936523324003164","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction

Gastric cancer (GC) is a common malignant tumor, and early diagnosis significantly improves patient survival rates. This study aimed to investigate the diagnostic value of ring finger protein 180 (RNF180) and secreted frizzled protein 2 (SFRP2) in GC.

Materials & Methods

A total of 165 healthy individuals, 34 patients with precancerous gastric lesions, and 104 patients with confirmed GC were divided into training and validation sets; methylated RNF180 and SFRP2 were detected in circulating DNA from blood samples. Six models, including those based on logistic regression, Naive Bayes, K-nearest neighbor algorithm, glmnet, neural network, and random forest (RF) were built and validated. Area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were determined.

Results

In the training set, the RF model with RNF180 and SFRP2 (R + S) had an AUC of 0.839 (95 % CI: 0.727–0.951), sensitivity of 60.3 %, and specificity of 85.5 % for diagnosing GC. The RF model with R + S+ Tumor markers had an AUC of 0.849 (95 % CI: 0.717–0.981), sensitivity of 62.8 %, and specificity of 87.1 %. In the validation set, the RF model with R + S had an AUC of 0.844 (95 % CI: 0.774–0.923), sensitivity of 87.8 %, and specificity of 69.2 %. The RF model with R + S + Tumor markers had an AUC of 0.858 (95 % CI: 0.781–0.939), sensitivity of 85.4 %, and specificity of 76.9 %.

Conclusion

Our results suggest that RNF180 and SFRP2 could serve as diagnostic biomarkers for GC when using the RF model.
结合甲基化 RNF180 和 SFRP2 血浆生物标记物进行胃癌无创诊断
简介胃癌(GC)是一种常见的恶性肿瘤,早期诊断可显著提高患者的生存率。本研究旨在探讨环指蛋白180(RNF180)和分泌型脆裂蛋白2(SFRP2)在胃癌中的诊断价值:将165名健康人、34名胃癌前病变患者和104名确诊GC患者分为训练集和验证集;在血液样本的循环DNA中检测甲基化的RNF180和SFRP2。建立并验证了六个模型,包括基于逻辑回归、Naive Bayes、K-近邻算法、glmnet、神经网络和随机森林(RF)的模型。确定了曲线下面积(AUC)、灵敏度、特异性、阳性预测值和阴性预测值:在训练集中,含有 RNF180 和 SFRP2(R + S)的 RF 模型诊断 GC 的 AUC 为 0.839(95 % CI:0.727-0.951),灵敏度为 60.3%,特异度为 85.5%。带有 R + S+ 肿瘤标记物的 RF 模型的 AUC 为 0.849(95 % CI:0.717-0.981),灵敏度为 62.8 %,特异性为 87.1 %。在验证集中,带有 R + S 的 RF 模型的 AUC 为 0.844(95 % CI:0.774-0.923),灵敏度为 87.8 %,特异度为 69.2 %。R+S+肿瘤标记物的RF模型的AUC为0.858(95% CI:0.781-0.939),灵敏度为85.4%,特异度为76.9%:我们的研究结果表明,在使用RF模型时,RNF180和SFRP2可作为GC的诊断生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
8.40
自引率
2.00%
发文量
314
审稿时长
54 days
期刊介绍: Translational Oncology publishes the results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of oncology patients. Translational Oncology will publish laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer. Peer reviewed manuscript types include Original Reports, Reviews and Editorials.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信