Extracellular Vesicle Protein Panel Enables Early Lung Cancer Detection in a Large Clinical Cohort

IF 14.5 1区 医学 Q1 CELL BIOLOGY
Linxiao Han, Yuanlin Song, Lin Tong, Jiayuan Sun, Xiaoju Zhang, Shujing Chen, Ying Li, Ziqi Wang, Lei Gao, Qiaoliang Zhu, Yencheng Chao, Xiaocen Wang, Ge Zhang, Wensi Zhu, Ludan He, Jie Liu, Qin Wang, Zuoren Wu, Yuanyuan Ji, Chunxue Bai, Xiuzhen Lv, Jian Zhou
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Abstract

The early detection and diagnosis of lung cancer through extracellular vesicle (EV)-based liquid biopsy show substantial promise for enhancing clinical outcomes. Nonetheless, there is a scarcity of large-scale clinical investigations validating EV-based liquid biopsy. To evaluate the EV membrane protein panel as a diagnostic tool for early-stage cancer detection and validate its efficacy and clinical applicability, a cohort comprised of 302 individuals without cancer and 645 with lung cancer was recruited. Participants were randomly divided into training and validation cohorts at a 1:1 ratio while maintaining the proportion of different subtypes. A diagnostic panel (EV early lung cancer membrane protein 5, EVELC-M5) consisting of five EV membrane proteins (CD81, PDL1, GLIPR1, LBR and SFTPA1) was developed using a High-throughput Nano-biochip Integrated System for Liquid Biopsy (HNCIB) to realize rapid analysis of a large cohort of patient samples at a single EV level. EVELC-M5 could accurately differentiate patients with early lung cancer from the control group. The area under the curve (AUC) of EVELC-M5 for distinguishing patients with early lung cancer from the control group in the validation cohort was 0.926, and the AUC for diagnosing patients with early lung cancer with lung nodules ≤ 8 mm was 0.931. EV-SFTPA1 proved to be the most effective marker, exhibiting a sensitivity of 89.4% in patients with early lung cancer. To our knowledge, this is the first study to use EV-SFTPA1 for early lung cancer detection, elucidating its robust tissue specificity. Collectively, the findings highlight that EVELC-M5 in conjunction with HNCIB is an effective diagnostic toolset for detecting early lung cancer and substantially promotes its diagnosis.

Trial Registration: ClinicalTrials.gov identifier: ChiCTR2300072317

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细胞外囊泡蛋白检测可在大型临床队列中实现早期肺癌检测
基于细胞外囊泡(EV)的液体活检对肺癌的早期检测和诊断显示出极大的希望,可以提高临床结果。然而,目前还缺乏大规模的临床研究来验证基于ev的液体活检。为了评估EV膜蛋白面板作为早期癌症检测的诊断工具,并验证其有效性和临床适用性,我们招募了302名非癌症患者和645名肺癌患者。参与者按1:1的比例随机分为训练组和验证组,同时保持不同亚型的比例。采用高通量纳米生物芯片集成液体活检系统(HNCIB)开发了一种由5种EV膜蛋白(CD81、PDL1、GLIPR1、LBR和SFTPA1)组成的EV早期肺癌膜蛋白5 (EVELC-M5)诊断试剂盒,实现了单个EV水平下对大量患者样本的快速分析。EVELC-M5能够准确区分早期肺癌患者与对照组。验证队列中EVELC-M5鉴别早期肺癌患者与对照组的曲线下面积(AUC)为0.926,诊断肺结节≤8 mm的早期肺癌患者的AUC为0.931。EV-SFTPA1被证明是最有效的标志物,在早期肺癌患者中显示出89.4%的敏感性。据我们所知,这是首次使用EV-SFTPA1进行早期肺癌检测的研究,阐明了其强大的组织特异性。总之,研究结果强调,EVELC-M5联合HNCIB是发现早期肺癌的有效诊断工具集,并显著促进其诊断。试验注册:ClinicalTrials.gov标识符:ChiCTR2300072317
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来源期刊
Journal of Extracellular Vesicles
Journal of Extracellular Vesicles Biochemistry, Genetics and Molecular Biology-Cell Biology
CiteScore
27.30
自引率
4.40%
发文量
115
审稿时长
12 weeks
期刊介绍: The Journal of Extracellular Vesicles is an open access research publication that focuses on extracellular vesicles, including microvesicles, exosomes, ectosomes, and apoptotic bodies. It serves as the official journal of the International Society for Extracellular Vesicles and aims to facilitate the exchange of data, ideas, and information pertaining to the chemistry, biology, and applications of extracellular vesicles. The journal covers various aspects such as the cellular and molecular mechanisms of extracellular vesicles biogenesis, technological advancements in their isolation, quantification, and characterization, the role and function of extracellular vesicles in biology, stem cell-derived extracellular vesicles and their biology, as well as the application of extracellular vesicles for pharmacological, immunological, or genetic therapies. The Journal of Extracellular Vesicles is widely recognized and indexed by numerous services, including Biological Abstracts, BIOSIS Previews, Chemical Abstracts Service (CAS), Current Contents/Life Sciences, Directory of Open Access Journals (DOAJ), Journal Citation Reports/Science Edition, Google Scholar, ProQuest Natural Science Collection, ProQuest SciTech Collection, SciTech Premium Collection, PubMed Central/PubMed, Science Citation Index Expanded, ScienceOpen, and Scopus.
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