| ISSN |
1948-5204 (online) |
| 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/ |
| Copyright |
© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Oncology |
| Manuscript Type |
Basic Study |
| Article Title |
Blood-based machine learning classifiers for early diagnosis of gastric cancer via multiple microRNAs
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
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 and Lian-Ying Ge |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| 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 |
| Future Academic Star of Guangxi Medical University |
No. WLXSZX23109 |
|
| 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 |
| Key Words |
Gastric cancer; MicroRNA; Biological marker; Machine learning; Serum |
| Core Tip |
This was an in-house single 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 microRNAs (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 10026 serum samples via machine learning techniques and is anticipated to become a novel biomarker for GC. |
| Publish Date |
2025-03-25 16:19 |
| Citation |
<p>Ma FC, Zhang GL, Chi BT, Tang YL, Peng W, Liu AQ, Chen G, Gao JB, Wei DM, Ge LY. Blood-based machine learning classifiers for early diagnosis of gastric cancer via multiple microRNAs. <i>World J Gastrointest Oncol</i> 2025; 17(4): 103679</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i4/103679.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i4.103679 |
