TIMER3:肿瘤免疫分析的增强资源。

IF 13.1 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Hao Cui,Guile Zhao,Yiwen Lu,Siying Zuo,Dingyu Duan,Xiaobo Luo,Hang Zhao,Jing Li,Zexian Zeng,Qianming Chen,Taiwen Li
{"title":"TIMER3:肿瘤免疫分析的增强资源。","authors":"Hao Cui,Guile Zhao,Yiwen Lu,Siying Zuo,Dingyu Duan,Xiaobo Luo,Hang Zhao,Jing Li,Zexian Zeng,Qianming Chen,Taiwen Li","doi":"10.1093/nar/gkaf388","DOIUrl":null,"url":null,"abstract":"The tumor immune microenvironment plays a critical role in tumor progression and immunotherapy response, with the abundance and composition of infiltrating immune cells serving as key determinants of therapeutic outcomes. Given the limitations of direct experimental methods, computational deconvolution algorithms are widely applied to infer immune cell infiltration from bulk RNA-seq data. While such estimates have proven valuable for studying tumor-immune interactions, large-scale, systematic analyses across multiple cancer types and treatment contexts remain limited. To address this need, we developed TIMER3 (https://compbio.cn/timer3/), an upgraded web server that substantially extends the functionality and scope of its predecessors. TIMER3 integrates 15 state-of-the-art immune deconvolution algorithms, including both human-specific and mouse-specific methods, to improve the robustness and interpretability of immune cell estimation. It incorporates an expanded collection of public RNA-seq datasets of immunotherapy-related cohorts, enabling large-scale analyses of immune dynamics across pre-treatment and post-treatment conditions. TIMER3 introduces new analytical modules for immunotherapy response, signature-based functional profiling, and interactive visualization of cell composition, gene expression, and survival associations. Collectively, TIMER3 provides a comprehensive, user-friendly platform for dissecting the TIME across diverse datasets, facilitating translational research and precision immuno-oncology.","PeriodicalId":19471,"journal":{"name":"Nucleic Acids Research","volume":"23 1","pages":""},"PeriodicalIF":13.1000,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TIMER3: an enhanced resource for tumor immune analysis.\",\"authors\":\"Hao Cui,Guile Zhao,Yiwen Lu,Siying Zuo,Dingyu Duan,Xiaobo Luo,Hang Zhao,Jing Li,Zexian Zeng,Qianming Chen,Taiwen Li\",\"doi\":\"10.1093/nar/gkaf388\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The tumor immune microenvironment plays a critical role in tumor progression and immunotherapy response, with the abundance and composition of infiltrating immune cells serving as key determinants of therapeutic outcomes. Given the limitations of direct experimental methods, computational deconvolution algorithms are widely applied to infer immune cell infiltration from bulk RNA-seq data. While such estimates have proven valuable for studying tumor-immune interactions, large-scale, systematic analyses across multiple cancer types and treatment contexts remain limited. To address this need, we developed TIMER3 (https://compbio.cn/timer3/), an upgraded web server that substantially extends the functionality and scope of its predecessors. TIMER3 integrates 15 state-of-the-art immune deconvolution algorithms, including both human-specific and mouse-specific methods, to improve the robustness and interpretability of immune cell estimation. It incorporates an expanded collection of public RNA-seq datasets of immunotherapy-related cohorts, enabling large-scale analyses of immune dynamics across pre-treatment and post-treatment conditions. TIMER3 introduces new analytical modules for immunotherapy response, signature-based functional profiling, and interactive visualization of cell composition, gene expression, and survival associations. Collectively, TIMER3 provides a comprehensive, user-friendly platform for dissecting the TIME across diverse datasets, facilitating translational research and precision immuno-oncology.\",\"PeriodicalId\":19471,\"journal\":{\"name\":\"Nucleic Acids Research\",\"volume\":\"23 1\",\"pages\":\"\"},\"PeriodicalIF\":13.1000,\"publicationDate\":\"2025-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nucleic Acids Research\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/nar/gkaf388\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nucleic Acids Research","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/nar/gkaf388","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

肿瘤免疫微环境在肿瘤进展和免疫治疗反应中起着关键作用,浸润免疫细胞的丰度和组成是治疗结果的关键决定因素。考虑到直接实验方法的局限性,计算反褶积算法被广泛应用于从大量RNA-seq数据推断免疫细胞浸润。虽然这些估计已被证明对研究肿瘤免疫相互作用有价值,但对多种癌症类型和治疗背景的大规模系统分析仍然有限。为了满足这一需求,我们开发了TIMER3 (https://compbio.cn/timer3/),这是一个升级的web服务器,大大扩展了其前身的功能和范围。TIMER3集成了15种最先进的免疫反褶积算法,包括人类特异性和小鼠特异性方法,以提高免疫细胞估计的鲁棒性和可解释性。它整合了免疫治疗相关队列的公共RNA-seq数据集的扩展集合,能够对治疗前和治疗后的免疫动力学进行大规模分析。TIMER3引入了新的分析模块,用于免疫治疗反应,基于特征的功能分析,以及细胞组成,基因表达和生存关联的交互式可视化。总的来说,TIMER3提供了一个全面的、用户友好的平台,用于跨不同数据集剖析TIME,促进转化研究和精确免疫肿瘤学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TIMER3: an enhanced resource for tumor immune analysis.
The tumor immune microenvironment plays a critical role in tumor progression and immunotherapy response, with the abundance and composition of infiltrating immune cells serving as key determinants of therapeutic outcomes. Given the limitations of direct experimental methods, computational deconvolution algorithms are widely applied to infer immune cell infiltration from bulk RNA-seq data. While such estimates have proven valuable for studying tumor-immune interactions, large-scale, systematic analyses across multiple cancer types and treatment contexts remain limited. To address this need, we developed TIMER3 (https://compbio.cn/timer3/), an upgraded web server that substantially extends the functionality and scope of its predecessors. TIMER3 integrates 15 state-of-the-art immune deconvolution algorithms, including both human-specific and mouse-specific methods, to improve the robustness and interpretability of immune cell estimation. It incorporates an expanded collection of public RNA-seq datasets of immunotherapy-related cohorts, enabling large-scale analyses of immune dynamics across pre-treatment and post-treatment conditions. TIMER3 introduces new analytical modules for immunotherapy response, signature-based functional profiling, and interactive visualization of cell composition, gene expression, and survival associations. Collectively, TIMER3 provides a comprehensive, user-friendly platform for dissecting the TIME across diverse datasets, facilitating translational research and precision immuno-oncology.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Nucleic Acids Research
Nucleic Acids Research 生物-生化与分子生物学
CiteScore
27.10
自引率
4.70%
发文量
1057
审稿时长
2 months
期刊介绍: Nucleic Acids Research (NAR) is a scientific journal that publishes research on various aspects of nucleic acids and proteins involved in nucleic acid metabolism and interactions. It covers areas such as chemistry and synthetic biology, computational biology, gene regulation, chromatin and epigenetics, genome integrity, repair and replication, genomics, molecular biology, nucleic acid enzymes, RNA, and structural biology. The journal also includes a Survey and Summary section for brief reviews. Additionally, each year, the first issue is dedicated to biological databases, and an issue in July focuses on web-based software resources for the biological community. Nucleic Acids Research is indexed by several services including Abstracts on Hygiene and Communicable Diseases, Animal Breeding Abstracts, Agricultural Engineering Abstracts, Agbiotech News and Information, BIOSIS Previews, CAB Abstracts, and EMBASE.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信