A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-06-13 DOI:10.1016/j.isci.2025.112881

Qinran Zhang , Huizhong Chi , Yanhua Qi , Rongrong Zhao , Fuzhong Xue , Gang Li , Hao Xue

{"title":"A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids","authors":"Qinran Zhang , Huizhong Chi , Yanhua Qi , Rongrong Zhao , Fuzhong Xue , Gang Li , Hao Xue","doi":"10.1016/j.isci.2025.112881","DOIUrl":null,"url":null,"abstract":"<div><div>Traditional glioma diagnostic methods have limitations, while liquid biopsy is a promising non-invasive option. This study developed the glioma-related cell signature (GRCS), a prediction model that integrates machine learning with biological insights. Trained on tumor-educated platelet samples, the GRCS model demonstrated consistent performance across validation cohorts comprising platelet, extracellular vesicle, and tumor tissue specimens. The GRCS score showed significant associations with patient age, histological grade, survival outcome, and mutational landscape. Moreover, the GRCS model effectively distinguished responses to bevacizumab and immunotherapy and identified potential candidates for combination therapies. Furthermore, a miRNA-based simplified GRCS model (GRCSS) was developed and validated across different specimen cohorts, demonstrating its robust diagnostic and prognostic capabilities in glioma. This work highlights the potential of GRCS as a versatile tool for personalized glioma management across multiple biopsy specimen types.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"28 7","pages":"Article 112881"},"PeriodicalIF":4.6000,"publicationDate":"2025-06-13","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/S2589004225011423","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

Traditional glioma diagnostic methods have limitations, while liquid biopsy is a promising non-invasive option. This study developed the glioma-related cell signature (GRCS), a prediction model that integrates machine learning with biological insights. Trained on tumor-educated platelet samples, the GRCS model demonstrated consistent performance across validation cohorts comprising platelet, extracellular vesicle, and tumor tissue specimens. The GRCS score showed significant associations with patient age, histological grade, survival outcome, and mutational landscape. Moreover, the GRCS model effectively distinguished responses to bevacizumab and immunotherapy and identified potential candidates for combination therapies. Furthermore, a miRNA-based simplified GRCS model (GRCSS) was developed and validated across different specimen cohorts, demonstrating its robust diagnostic and prognostic capabilities in glioma. This work highlights the potential of GRCS as a versatile tool for personalized glioma management across multiple biopsy specimen types.

Abstract Image

查看原文本刊更多论文

基于组织和生物体液分析的胶质瘤诊断和治疗评价的肿瘤微环境模型

传统的胶质瘤诊断方法有局限性，而液体活检是一种有前途的非侵入性选择。这项研究开发了胶质瘤相关细胞特征（GRCS），这是一种将机器学习与生物学见解相结合的预测模型。经过肿瘤诱导血小板样本的训练，GRCS模型在包括血小板、细胞外囊泡和肿瘤组织样本在内的验证队列中表现出一致的性能。GRCS评分与患者年龄、组织学分级、生存结局和突变景观有显著相关性。此外，GRCS模型有效地区分了对贝伐单抗和免疫治疗的反应，并确定了联合治疗的潜在候选药物。此外，研究人员开发了一种基于mirna的简化GRCS模型（GRCSS），并在不同的标本队列中进行了验证，证明了其在胶质瘤中的强大诊断和预后能力。这项工作强调了GRCS作为跨多种活检标本类型的个性化胶质瘤管理的多功能工具的潜力。

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分钟内获得全文求助全文

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.