Basic Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Apr 15, 2025; 17(4): 103679
Published online Apr 15, 2025. doi: 10.4251/wjgo.v17.i4.103679
Blood-based machine learning classifiers for early diagnosis of gastric cancer via multiple miRNAs
Fu-Chao Ma, Guan-Lan Zhang, Bang-Teng Chi, Yu-Lu Tang, Wei Peng, Ai-Qun Liu, Gang Chen, Jin-Biao Gao, Dan-Ming Wei, Lian-Ying Ge
Fu-Chao Ma, Wei Peng, Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
Guan-Lan Zhang, Bang-Teng Chi, Yu-Lu Tang, Gang Chen, Jin-Biao Gao, Dan-Ming Wei, Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
Ai-Qun Liu, Lian-Ying Ge, Department of Endoscopy, Guangxi Medical University Cancer Hospital, Nanning 530021, Guangxi Zhuang Autonomous Region, China
Co-first authors: Fu-Chao Ma and Guan-Lan Zhang.
Co-corresponding authors: Dan-Ming Wei and Lian-Ying Ge.
Author contributions: Ma FC, Liu AQ, Chen G, Wei DM, and Ge LY designed the research; Ma FC and Chi BT collected clinical information; Ma FC, Zhang GL, Chen G, and Wei DM conducted the small RNA-sequencing and real-time quantitative reverse transcription polymerase chain reaction experiments; Zhang GL and Tang YL screened and processed the public small RNA sequencing datasets; Tang YL, Peng W and Gao JB analysed the data; Ma FC, Zhang GL, Chi BT, Tang YL, Peng W, and Gao JB wrote the draft; Liu AQ, Chen G, Wei DM, and Ge LY participated in manuscript revision; All authors read and approved the final manuscript.
Supported by the Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project, No. Z-A20220465; Guangxi Key R and D Plan, No. AB20297021; Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project, No. S2022107; China Undergraduate Innovation and Entrepreneurship Training Program, No. S202310598074; and Future Academic Star of Guangxi Medical University, No. WLXSZX23109.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Boards of the Guilin People’s Hospital (No. 2020-102KY), Guangxi Medical University Cancer Hospital (No. KY2020148) and Youjiang Medical University Affiliated Hospital (No. YYFY-LL-2024-005).
Institutional animal care and use committee statement: This research did not use animals and did not require ethnics.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The data used to support the findings of this study are available from the corresponding author upon request at gxgly@hotmail.com.
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: Lian-Ying Ge, Department of Endoscopy, Guangxi Medical University Cancer Hospital, No. 71 Hedi Road, Nanning 530021, Guangxi Zhuang Autonomous Region, China. gxgly@hotmail.com
Received: November 27, 2024
Revised: January 16, 2025
Accepted: February 11, 2025
Published online: April 15, 2025
Processing time: 119 Days and 2.6 Hours
Abstract
BACKGROUND

Early screening methods for gastric cancer (GC) are lacking; therefore, the disease often progresses to an advanced stage when patients first start to exhibit typical symptoms. Endoscopy and pathological biopsy remain the primary diagnostic approaches, but they are invasive and not yet widely applicable for early population screening. miRNA is a highly conserved type of RNA that exists stably in plasma. Dysfunction of miRNA is linked to tumorigenesis and progression, indicating that individual miRNAs or combinations of multiple miRNAs may serve as potential biomarkers.

AIM

To identify effective plasma miRNA biomarkers and investigate the clinical value of combining multiple miRNAs for early detection of GC.

METHODS

Plasma samples from multiple centres were collected. Differentially expressed genes among healthy controls, early-stage GC patients, and advanced-stage GC patients were identified through small RNA sequencing (sRNA-seq) and validated via real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR). A Wilcoxon signed-rank test was used to investigate the differences in miRNAs. Sequencing datasets of GC serum samples were retrieved from the Gene Expression Omnibus (GEO), ArrayExpress, and The Cancer Genome Atlas databases, and a multilayer perceptron-artificial neural network (MLP-ANN) model was constructed for the key risk miRNAs. The pROC package was used to assess the discriminatory efficacy of the model.

RESULTS

Plasma samples of 107 normal, 71 early GC and 97 advanced GC patients were obtained from three centres, and serum samples of 8443 normal and 1583 GC patients were obtained from the GEO database. The sRNA-seq and RT-qPCR experiments revealed that miR-452-5p, miR-5010-5p, miR-27b-5p, miR-5189-5p, miR-552-5p and miR-199b-5p were significantly increased in early GC patients compared with healthy controls and in advanced GC patients compared with early GC patients (P < 0.05). An MLP-ANN model was constructed for the six key miRNAs. The area under the curve (AUC) within the training cohort was 0.983 [95% confidence interval (CI): 0.980–0.986]. In the two validation cohorts, the AUCs were 0.995 (95%CI: 0.987 to nearly 1.000) and 0.979 (95%CI: 0.972–0.986), respectively.

CONCLUSION

Potential miRNA biomarkers, including miR-452-5p, miR-5010-5p, miR-27b-5p, miR-5189-5p, miR-552-5p and miR-199b-5p, were identified. A GC classifier based on these miRNAs was developed, benefiting early detection and population screening.

Keywords: Gastric cancer; miRNA; Biological marker; Machine learning; Serum

Core Tip: This was an in-house small RNA sequencing analysis of five healthy, five early gastric cancer (GC) and five advanced GC plasma samples, and the top 15 differentially expressed genes were verified in 275 plasma samples via real-time quantitative reverse transcription polymerase chain reaction. Six key miRNAs, miR-452-5p, miR-5010-5p, miR-27b-5p, miR-5189-5p, miR-552-5p and miR-199b-5p, were ultimately identified. A multilayer perceptron-artificial neural network classifier incorporating these six miRNAs was innovatively constructed based on 10 026 serum samples via machine learning techniques and is anticipated to become a novel biomarker for GC.