基于细胞外囊泡衍生转座因子的预测模型的开发和验证,用于无创检测胰腺腺癌。

IF 9.5 2区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Yueting Liang, Xin Sui, Shuai Li, Haoxin Peng, Wenyi Jiang, Minqi Jia, Shaoran Jiang, Weihu Wang, Huajing Teng
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引用次数: 0

摘要

胰腺腺癌(PAAD)是一种高度致命的恶性肿瘤,由于其非典型临床症状和缺乏有效的诊断生物标志物,导致患者错失最佳治疗机会。为了开发一种基于细胞外囊泡衍生转座因子(EV-TEs)的生物标志物面板,用于无创检测PAAD,我们分析了来自两个队列的852个ev衍生转录组的6.75 Tbp测序数据,并使用递归特征消除法确定了31个EV-TEs特征作为生物标志物面板。使用支持向量机(SVM)算法构建的预测模型在训练集(AUC: 0.90, 95% CI: 0.86-0.93)、测试集(AUC: 0.86, 95% CI: 0.79-0.92)和血液ev来源样本的独立外部验证队列(AUC: 0.88, 95% CI: 0.84-0.92)中均表现出优异的PAAD检测性能。这项研究提出了第一个基于EV-TEs的PAAD检测预测模型,展示了这些“垃圾DNA”作为癌症创新诊断生物标志物的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a predictive model based upon extracellular vesicle-derived transposable elements for non-invasive detection of pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a highly lethal malignancy that leads to patients missing optimal treatment opportunities due to its atypical clinical symptoms and the lack of effective diagnostic biomarkers. To develop a biomarker panel based on extracellular vesicle-derived transposable elements (EV-TEs) for non-invasive detection of PAAD, we analyzed 6.75 Tbp sequencing data of 852 EV-derived transcriptomes from two cohorts, and identified 31 EV-TEs features as the biomarker panel using recursive feature elimination. Predictive model constructed using the Support Vector Machine (SVM) algorithm demonstrated excellent performance for PAAD detection in the training set (AUC: 0.90, 95% CI: 0.86-0.93), the test set (AUC: 0.86, 95% CI: 0.79-0.92) and the independent external validation cohort of blood EV-derived samples (AUC: 0.88, 95% CI: 0.84-0.92). This study presents the first EV-TEs based predictive model for PAAD detection, showcasing the immense potential of these 'junk DNA' as innovative diagnostic biomarker for cancers.

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来源期刊
Biomarker Research
Biomarker Research Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
15.80
自引率
1.80%
发文量
80
审稿时长
10 weeks
期刊介绍: Biomarker Research, an open-access, peer-reviewed journal, covers all aspects of biomarker investigation. It seeks to publish original discoveries, novel concepts, commentaries, and reviews across various biomedical disciplines. The field of biomarker research has progressed significantly with the rise of personalized medicine and individual health. Biomarkers play a crucial role in drug discovery and development, as well as in disease diagnosis, treatment, prognosis, and prevention, particularly in the genome era.
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