{"title":"Unleashing the potential of exosome ncRNAs for early gastric cancer detection—a critical appraisal of machine learning approaches","authors":"Xuefan Zeng","doi":"10.1136/gutjnl-2025-334909","DOIUrl":null,"url":null,"abstract":"We read with great interest the article by Cai et al ,1 titled ‘Construction of exosome non-coding RNA feature for non-invasive, early detection of gastric cancer patients by machine learning: a multi-cohort study’, published in Gut . The study presents a novel approach for the early detection of gastric cancer (GC) using serum exosome non-coding RNAs (ncRNAs) and machine learning, which is a significant step forward in the field of liquid biopsy for cancer diagnostics. The study by Cai et al has several notable strengths. First, the comprehensive multi-cohort design, including both training and external validation cohorts, provides robust evidence for the diagnostic potential of the identified exosome ncRNA feature. The use of machine learning algorithms, particularly LASSO-logistic regression, to develop the combined diagnostic model (cd-score) is innovative and demonstrates high diagnostic accuracy with an area under the curve of 0.959 in the training cohort and 0.949 in the external validation cohort. Additionally, the study highlights the potential of DGCR9 as a therapeutic target, supported by in vitro and in vivo experiments. Despite these strengths, …","PeriodicalId":12825,"journal":{"name":"Gut","volume":"12 1","pages":""},"PeriodicalIF":23.0000,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gut","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/gutjnl-2025-334909","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
我们饶有兴趣地阅读了Cai等人1发表在《Gut》上的题为《Construction of exosome non-coding RNA feature for non-invasive, early detection of gastric cancer patients by machine learning: a multi-cohort study》的文章。该研究提出了一种利用血清外泌体非编码 RNA(ncRNA)和机器学习早期检测胃癌(GC)的新方法,是液体活检在癌症诊断领域迈出的重要一步。Cai等人的研究有几个显著的优点。首先,全面的多队列设计(包括训练队列和外部验证队列)为已识别的外泌体 ncRNA 特征的诊断潜力提供了有力的证据。使用机器学习算法,特别是 LASSO 逻辑回归来开发综合诊断模型(cd-score)是一项创新,它显示了很高的诊断准确性,训练队列的曲线下面积为 0.959,外部验证队列的曲线下面积为 0.949。此外,该研究还强调了 DGCR9 作为治疗靶点的潜力,并得到了体外和体内实验的支持。尽管有这些优势,...
Unleashing the potential of exosome ncRNAs for early gastric cancer detection—a critical appraisal of machine learning approaches
We read with great interest the article by Cai et al ,1 titled ‘Construction of exosome non-coding RNA feature for non-invasive, early detection of gastric cancer patients by machine learning: a multi-cohort study’, published in Gut . The study presents a novel approach for the early detection of gastric cancer (GC) using serum exosome non-coding RNAs (ncRNAs) and machine learning, which is a significant step forward in the field of liquid biopsy for cancer diagnostics. The study by Cai et al has several notable strengths. First, the comprehensive multi-cohort design, including both training and external validation cohorts, provides robust evidence for the diagnostic potential of the identified exosome ncRNA feature. The use of machine learning algorithms, particularly LASSO-logistic regression, to develop the combined diagnostic model (cd-score) is innovative and demonstrates high diagnostic accuracy with an area under the curve of 0.959 in the training cohort and 0.949 in the external validation cohort. Additionally, the study highlights the potential of DGCR9 as a therapeutic target, supported by in vitro and in vivo experiments. Despite these strengths, …
期刊介绍:
Gut is a renowned international journal specializing in gastroenterology and hepatology, known for its high-quality clinical research covering the alimentary tract, liver, biliary tree, and pancreas. It offers authoritative and current coverage across all aspects of gastroenterology and hepatology, featuring articles on emerging disease mechanisms and innovative diagnostic and therapeutic approaches authored by leading experts.
As the flagship journal of BMJ's gastroenterology portfolio, Gut is accompanied by two companion journals: Frontline Gastroenterology, focusing on education and practice-oriented papers, and BMJ Open Gastroenterology for open access original research.