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Abstract
Deciphering the cell-type composition in the tumor immune microenvironment (TIME) can significantly increase the efficacy of cancer treatment and improve the prognosis of cancer. Such a task has benefited from microarrays and RNA sequencing technologies, which have been widely adopted in cancer studies, resulting in extensive expression profiles with clinical phenotypes across multiple cancers. Current state-of-the-art tools can infer cell-type composition from bulk expression profiles, providing the possibility of investigating the inter-heterogeneity and intra-heterogeneity of TIME across cancer types. Much can be gained from these tools in conjunction with a well-curated database of TIME cell-type composition data, accompanied by the corresponding clinical information. However, currently available databases fall short in data volume, multi-platform dataset integration, and tool integration. In this work, we introduce TIMEDB (https://timedb.deepomics.org), an online database for human tumor immune microenvironment cell-type composition estimated from bulk expression profiles. TIMEDB stores manually curated expression profiles, cell-type composition profiles, and the corresponding clinical information of a total of 39,706 samples from 546 datasets across 43 cancer types. TIMEDB comes readily equipped with online tools for automatic analysis and interactive visualization, and aims to serve the community as a convenient tool for investigating the human tumor microenvironment.
| Original language | English |
|---|---|
| Pages (from-to) | D1417–D1424 |
| Journal | Nucleic acids research |
| Volume | 51 |
| Issue number | D1 |
| Online published | 18 Nov 2022 |
| DOIs | |
| Publication status | Published - 6 Jan 2023 |
Funding
CityU/UGC Research Matching Grant Scheme [9229013] and SIRG [CityU SIRG 7020005] Funding for open access charge: CityU/UGC Research Matching Grant Scheme [9229013] and The Science Technology and Innovation Committee of Shenzhen Municipality (Project No. JCYJ20200109143216036).
Research Keywords
- GENE-EXPRESSION
- DECONVOLUTION
- LANDSCAPE
- REVEAL
Publisher's Copyright Statement
- This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/
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DON_RMG: Convolutional Neural Networks can Estimate Ages from Panoramic Dental X-ray Images Accurately - RMGS
LI, S. (Principal Investigator / Project Coordinator)
1/01/20 → 29/12/23
Project: Research