Deciphering aging-associated prognosis and heterogeneity in gastric cancer through a machine learning-driven approach

IF 4.1 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES iScience Pub Date : 2025-03-28 DOI:10.1016/j.isci.2025.112316

Jiang Li , Chuanlai Yang , Yunxiao Zhang , Xiaoning Hong , Mingye Jiang , Zhongxu Zhu , Jiang Li

{"title":"Deciphering aging-associated prognosis and heterogeneity in gastric cancer through a machine learning-driven approach","authors":"Jiang Li , Chuanlai Yang , Yunxiao Zhang , Xiaoning Hong , Mingye Jiang , Zhongxu Zhu , Jiang Li","doi":"10.1016/j.isci.2025.112316","DOIUrl":null,"url":null,"abstract":"<div><div>Gastric cancer (GC) is a prevalent malignancy with a high mortality rate and limited treatment options. Aging significantly contributes to tumor progression, and GC was confirmed as an aging-related heterogeneous disease. This study established an Aging-Associated Index (AAI) using a machine learning-derived gene panel to stratify GC patients. High AAI scores associated with poor prognosis and indicated potential benefits from adjuvant chemotherapy, while showing resistance to immunotherapy. Single-cell transcriptome analysis revealed that AAI was enriched in monocyte cells within the tumor microenvironment. Two distinct molecular subtypes of GC were identified through unsupervised clustering, leading to the development of a subtype-specific regulatory network highlighting <em>SOX7</em> and <em>ELK3</em> as potential therapeutic targets. Drug sensitivity analyses indicated that patients with high <em>ELK3</em> expression may respond to FDA-approved drugs (axitinib, dacarbazine, crizotinib, and vincristine). Finally, a user-friendly Shiny application was created to facilitate access to the prognostic model and molecular subtype classifier for GC.</div><div>Subject areas</div><div>Aging, prognostic model, regulatory network, consensus subtype, drug repurposing</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"28 5","pages":"Article 112316"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"iScience","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589004225005772","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Gastric cancer (GC) is a prevalent malignancy with a high mortality rate and limited treatment options. Aging significantly contributes to tumor progression, and GC was confirmed as an aging-related heterogeneous disease. This study established an Aging-Associated Index (AAI) using a machine learning-derived gene panel to stratify GC patients. High AAI scores associated with poor prognosis and indicated potential benefits from adjuvant chemotherapy, while showing resistance to immunotherapy. Single-cell transcriptome analysis revealed that AAI was enriched in monocyte cells within the tumor microenvironment. Two distinct molecular subtypes of GC were identified through unsupervised clustering, leading to the development of a subtype-specific regulatory network highlighting SOX7 and ELK3 as potential therapeutic targets. Drug sensitivity analyses indicated that patients with high ELK3 expression may respond to FDA-approved drugs (axitinib, dacarbazine, crizotinib, and vincristine). Finally, a user-friendly Shiny application was created to facilitate access to the prognostic model and molecular subtype classifier for GC.

Subject areas

Aging, prognostic model, regulatory network, consensus subtype, drug repurposing

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

通过机器学习驱动的方法解读胃癌的衰老相关预后和异质性

胃癌（GC）是一种常见的恶性肿瘤，死亡率高，治疗方案有限。衰老显著促进肿瘤进展，GC被证实是一种与衰老相关的异质性疾病。本研究使用机器学习衍生的基因面板建立了衰老相关指数（AAI）来对GC患者进行分层。AAI评分高与预后差相关，表明辅助化疗有潜在益处，同时对免疫治疗有耐药性。单细胞转录组分析显示，AAI在肿瘤微环境中的单核细胞中富集。通过无监督聚类鉴定出GC的两种不同的分子亚型，导致亚型特异性调控网络的发展，突出SOX7和ELK3作为潜在的治疗靶点。药物敏感性分析表明，ELK3高表达的患者可能对fda批准的药物（阿西替尼、达卡巴嗪、克里唑替尼和长春新碱）有反应。最后，创建了一个用户友好的Shiny应用程序，以方便访问GC的预后模型和分子亚型分类器。主题领域：衰老、预后模型、监管网络、共识亚型、药物再利用

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

期刊介绍： Science has many big remaining questions. To address them, we will need to work collaboratively and across disciplines. The goal of iScience is to help fuel that type of interdisciplinary thinking. iScience is a new open-access journal from Cell Press that provides a platform for original research in the life, physical, and earth sciences. The primary criterion for publication in iScience is a significant contribution to a relevant field combined with robust results and underlying methodology. The advances appearing in iScience include both fundamental and applied investigations across this interdisciplinary range of topic areas. To support transparency in scientific investigation, we are happy to consider replication studies and papers that describe negative results. We know you want your work to be published quickly and to be widely visible within your community and beyond. With the strong international reputation of Cell Press behind it, publication in iScience will help your work garner the attention and recognition it merits. Like all Cell Press journals, iScience prioritizes rapid publication. Our editorial team pays special attention to high-quality author service and to efficient, clear-cut decisions based on the information available within the manuscript. iScience taps into the expertise across Cell Press journals and selected partners to inform our editorial decisions and help publish your science in a timely and seamless way.