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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Nov 15, 2025; 17(11): 111670
Published online Nov 15, 2025. doi: 10.4251/wjgo.v17.i11.111670
Published online Nov 15, 2025. doi: 10.4251/wjgo.v17.i11.111670
Early cancer diagnosis via interpretable two-layer machine learning of plasma extracellular vesicle long RNA
Shi-Cai Liu, School of Medical Information, Wannan Medical College, Wuhu 241002, Anhui Province, China
Han Zhang, School of Basic Medical Sciences, Wannan Medical College, Wuhu 241002, Anhui Province, China
Co-corresponding authors: Shi-Cai Liu and Han Zhang.
Author contributions: Liu SC and Zhang H collected and analyzed the data, wrote the manuscript, and made equal contributions as co-corresponding authors; Liu SC supervised the project. Both authors have read and approved the final version to be published.
Supported by Talent Scientific Research Start-up Foundation of Wannan Medical College, No. WYRCQD2023045.
Institutional review board statement: This study did not involve human participants or animal subjects; therefore, neither Institutional Review Board nor Institutional Animal Care and Use Committee approval was required.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data that support the findings of this study are available from the authors upon reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Shi-Cai Liu, PhD, School of Medical Information, Wannan Medical College, No. 22 Wenchang West Road, Wuhu 241002, Anhui Province, China.
Received: July 7, 2025
Revised: August 7, 2025
Accepted: October 9, 2025
Published online: November 15, 2025
Processing time: 131 Days and 14.2 Hours
Revised: August 7, 2025
Accepted: October 9, 2025
Published online: November 15, 2025
Processing time: 131 Days and 14.2 Hours
Core Tip
Core Tip: The early diagnosis rate of pancreatic ductal adenocarcinoma is low and the prognosis is poor. It is important to develop an interpretable noninvasive early diagnostic model in clinical practice. In this study, an interpretable two-layer machine learning framework was proposed for the early diagnosis and prediction of pancreatic ductal adenocarcinoma based on plasma extracellular vesicle long RNA. This study provides new insights into the clinical value of extracellular vesicle long RNA for promoting the development of precision medicine.