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
{"title":"Extracellular Vesicle Protein Panel Enables Early Lung Cancer Detection in a Large Clinical Cohort","authors":"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","doi":"10.1002/jev2.70129","DOIUrl":null,"url":null,"abstract":"<p>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, EV<sup>ELC-M5</sup>) 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. EV<sup>ELC-M5</sup> could accurately differentiate patients with early lung cancer from the control group. The area under the curve (AUC) of EV<sup>ELC-M5</sup> 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 EV<sup>ELC-M5</sup> in conjunction with HNCIB is an effective diagnostic toolset for detecting early lung cancer and substantially promotes its diagnosis.</p><p><b>Trial Registration</b>: ClinicalTrials.gov identifier: ChiCTR2300072317</p>","PeriodicalId":15811,"journal":{"name":"Journal of Extracellular Vesicles","volume":"14 8","pages":""},"PeriodicalIF":14.5000,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jev2.70129","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Extracellular Vesicles","FirstCategoryId":"3","ListUrlMain":"https://isevjournals.onlinelibrary.wiley.com/doi/10.1002/jev2.70129","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
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.