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.

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Publication Details

Journal
Nucleic Acids Research
Published
2025-05-08
DOI
https://doi.org/10.1093/nar/gkaf388
Citations
33
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
30.90

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article

TIMER3: an enhanced resource for tumor immune analysis

Hao Cui, Hang Zhao, Xiaobo Luo, Taiwen Li et al.
33 citations
Nucleic Acids Research
Cancer-related molecular mechanisms research
30.90
article

TIMER3: an enhanced resource for tumor immune analysis

Hao Cui, Hang Zhao, Xiaobo Luo, Taiwen Li, Dingyu Duan, Yiwen Lu, Zexian Zeng, Qianming Chen, Jing Li, Suqiong Zuo, Guile Zhao
article en
33 citations

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.

Nucleic Acids ResearchVol. 53(W1)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking University (CN), Sichuan University (CN), Center for Life Sciences (CN)
Sichuan University, Natural Science Foundation of Sichuan Province
Openalex Percentile: Top 0%
Cancer-related molecular mechanisms research
30.90
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