| URL: | http://biocc.hrbmu.edu.cn/CancerState |
| Full name: | Cancer Single-cell State Atlas |
| Description: | CancerSEA-X is a comprehensive cancer single-cell state resource, systematically characterizing 156 distinct cell states across malignant, immune, and stromal cells. By analyzing 239 scRNA-seq datasets covering 32 cancer types, it provides a pan-cancer tumor microenvironment(TME) cell state atlas. CancerSEA-X is the advanced version of CancerSEA (available at http://biocc.hrbmu.edu.cn/CancerSEA/) |
| Year founded: | 2019 |
| Last update: | |
| Version: | 2025 |
| Accessibility: |
Accessible
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| Country/Region: | China |
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| University/Institution: | Harbin Medical University |
| Address: | College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China |
| City: | Harbin |
| Province/State: | Heilongjiang |
| Country/Region: | China |
| Contact name (PI/Team): | Yun Xiao |
| Contact email (PI/Helpdesk): | xiaoyun@ems.hrbmu.edu.cn |
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CancerSEA-X: A Single-cell Resource for Tumor Microenvironment Cell States Across over 30 Cancer Types. [PMID: 41546374]
Single-cell studies have significantly advanced our understanding of the transcriptional and functional heterogeneity in cancers. Recent studies have identified distinct states of cancer, immune, and stromal cells in the tumor microenvironment (TME), with growing evidence highlighting their clinical significance and therapeutic potential. Here, we present CancerSEA-X, an expanded version of CancerSEA that offers a comprehensive atlas of TME cell states. CancerSEA-X integrates 25 cancer cell states, 105 immune cell states, and 26 stromal cell states from systematically curated publications. Combining 239 single-cell datasets across 32 cancer types, encompassing over 9 million cells from 2120 patients, CancerSEA-X provides functional activity spectra and cancer-specific gene associations for these 156 cell states. These cell state-gene relationships were mapped onto networks, providing a systematic view of the TME. To improve usability, we redesigned the user interface to feature cell state characterization, state-gene correlation analysis, and interactive visualization of cell state-gene networks, enabling researchers to comprehensively explore these states and their functional relevance. Overall, CancerSEA-X serves as a valuable platform for investigating TME cell states, deepening our understanding of cancer heterogeneity, and potentially advancing the design of more effective clinical therapies. CancerSEA-X is freely available at http://biocc.hrbmu.edu.cn/CancerState. |
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CancerSEA: a cancer single-cell state atlas. [PMID: 30329142]
High functional heterogeneity of cancer cells poses a major challenge for cancer research. Single-cell sequencing technology provides an unprecedented opportunity to decipher diverse functional states of cancer cells at single-cell resolution, and cancer scRNA-seq datasets have been largely accumulated. This emphasizes the urgent need to build a dedicated resource to decode the functional states of cancer single cells. Here, we developed CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/ or http://202.97.205.69/CancerSEA/), the first dedicated database that aims to comprehensively explore distinct functional states of cancer cells at the single-cell level. CancerSEA portrays a cancer single-cell functional state atlas, involving 14 functional states (including stemness, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation and quiescence) of 41 900 cancer single cells from 25 cancer types. It allows querying which functional states are associated with the gene (or gene list) of interest in different cancers. CancerSEA also provides functional state-associated PCG/lncRNA repertoires across all cancers, in specific cancers, and in individual cancer single-cell datasets. In summary, CancerSEA provides a user-friendly interface for comprehensively searching, browsing, visualizing and downloading functional state activity profiles of tens of thousands of cancer single cells and the corresponding PCGs/lncRNAs expression profiles. |