CancerSCEM


39460627	CancerSCEM 2.0: an updated data resource of single-cell expression map across various human cancers. [PMID: 39460627] Zeng J, Nie Z, Shang Y, Mai J, Zhang Y, Yang Y, Xu C, Zhao J, Fan Z, Xiao J. Abstract The field of single-cell RNA sequencing (scRNA-seq) has advanced rapidly in the past decade, generating significant amounts of valuable data for researchers to study complex tumor profiles. This data is crucial for gaining innovative insights into cancer biology. CancerSCEM (https://ngdc.cncb.ac.cn/cancerscem) is a public resource that integrates, analyzes and visualizes scRNA-seq data related to cancer, and it provides invaluable support to numerous cancer-related studies. With CancerSCEM 2.0, scRNA-seq data have increased from 208 to 1466 datasets, covering tumor, matching-normal and peripheral blood samples across 127 research projects and 74 cancer types. The new version of this resource enhances transcriptome analysis by adding copy number variation evaluation, transcription factor enrichment, pseudotime trajectory construction, and diverse biological feature scoring. It also introduces a new cancer metabolic map at the single-cell level, providing an intuitive understanding of metabolic regulation across different cancer types. CancerSCEM 2.0 has a more interactive analysis platform, including four modules and 14 analytical functions, allowing researchers to perform cancer scRNA-seq data analyses in various dimensions. These enhancements are expected to expand the usability of CancerSCEM 2.0 to a broader range of cancer research and clinical applications, potentially revolutionizing our understanding of cancer mechanisms and treatments. Nucleic Acids Res. 2025:53(D1) \| 0 Citations (from Europe PMC, 2025-03-29)
34643725	CancerSCEM: a database of single-cell expression map across various human cancers. [PMID: 34643725] Zeng J, Zhang Y, Shang Y, Mai J, Shi S, Lu M, Bu C, Zhang Z, Zhang Z, Li Y, Du Z, Xiao J. Abstract With the proliferating studies of human cancers by single-cell RNA sequencing technique (scRNA-seq), cellular heterogeneity, immune landscape and pathogenesis within diverse cancers have been uncovered successively. The exponential explosion of massive cancer scRNA-seq datasets in the past decade are calling for a burning demand to be integrated and processed for essential investigations in tumor microenvironment of various cancer types. To fill this gap, we developed a database of Cancer Single-cell Expression Map (CancerSCEM, https://ngdc.cncb.ac.cn/cancerscem), particularly focusing on a variety of human cancers. To date, CancerSCE version 1.0 consists of 208 cancer samples across 28 studies and 20 human cancer types. A series of uniformly and multiscale analyses for each sample were performed, including accurate cell type annotation, functional gene expressions, cell interaction network, survival analysis and etc. Plus, we visualized CancerSCEM as a user-friendly web interface for users to browse, search, online analyze and download all the metadata as well as analytical results. More importantly and unprecedentedly, the newly-constructed comprehensive online analyzing platform in CancerSCEM integrates seven analyze functions, where investigators can interactively perform cancer scRNA-seq analyses. In all, CancerSCEM paves an informative and practical way to facilitate human cancer studies, and also provides insights into clinical therapy assessments. Nucleic Acids Res. 2022:50(D1) \| 45 Citations (from Europe PMC, 2025-03-29)

CancerSCEM 2.0: an updated data resource of single-cell expression map across various human cancers. [PMID: 39460627]

Zeng J, Nie Z, Shang Y, Mai J, Zhang Y, Yang Y, Xu C, Zhao J, Fan Z, Xiao J.

Abstract

The field of single-cell RNA sequencing (scRNA-seq) has advanced rapidly in the past decade, generating significant amounts of valuable data for researchers to study complex tumor profiles. This data is crucial for gaining innovative insights into cancer biology. CancerSCEM (https://ngdc.cncb.ac.cn/cancerscem) is a public resource that integrates, analyzes and visualizes scRNA-seq data related to cancer, and it provides invaluable support to numerous cancer-related studies. With CancerSCEM 2.0, scRNA-seq data have increased from 208 to 1466 datasets, covering tumor, matching-normal and peripheral blood samples across 127 research projects and 74 cancer types. The new version of this resource enhances transcriptome analysis by adding copy number variation evaluation, transcription factor enrichment, pseudotime trajectory construction, and diverse biological feature scoring. It also introduces a new cancer metabolic map at the single-cell level, providing an intuitive understanding of metabolic regulation across different cancer types. CancerSCEM 2.0 has a more interactive analysis platform, including four modules and 14 analytical functions, allowing researchers to perform cancer scRNA-seq data analyses in various dimensions. These enhancements are expected to expand the usability of CancerSCEM 2.0 to a broader range of cancer research and clinical applications, potentially revolutionizing our understanding of cancer mechanisms and treatments.

Nucleic Acids Res. 2025:53(D1) | 0 Citations (from Europe PMC, 2025-03-29)

CancerSCEM: a database of single-cell expression map across various human cancers. [PMID: 34643725]

Zeng J, Zhang Y, Shang Y, Mai J, Shi S, Lu M, Bu C, Zhang Z, Zhang Z, Li Y, Du Z, Xiao J.

Abstract

With the proliferating studies of human cancers by single-cell RNA sequencing technique (scRNA-seq), cellular heterogeneity, immune landscape and pathogenesis within diverse cancers have been uncovered successively. The exponential explosion of massive cancer scRNA-seq datasets in the past decade are calling for a burning demand to be integrated and processed for essential investigations in tumor microenvironment of various cancer types. To fill this gap, we developed a database of Cancer Single-cell Expression Map (CancerSCEM, https://ngdc.cncb.ac.cn/cancerscem), particularly focusing on a variety of human cancers. To date, CancerSCE version 1.0 consists of 208 cancer samples across 28 studies and 20 human cancer types. A series of uniformly and multiscale analyses for each sample were performed, including accurate cell type annotation, functional gene expressions, cell interaction network, survival analysis and etc. Plus, we visualized CancerSCEM as a user-friendly web interface for users to browse, search, online analyze and download all the metadata as well as analytical results. More importantly and unprecedentedly, the newly-constructed comprehensive online analyzing platform in CancerSCEM integrates seven analyze functions, where investigators can interactively perform cancer scRNA-seq analyses. In all, CancerSCEM paves an informative and practical way to facilitate human cancer studies, and also provides insights into clinical therapy assessments.

Nucleic Acids Res. 2022:50(D1) | 45 Citations (from Europe PMC, 2025-03-29)

URL:	https://ngdc.cncb.ac.cn/cancerscem
Full name:	Cancer Single-cell Expression Map
Description:	A public database dedicated to integrating, analyzing, visualizing single-cell RNA-seq data of human pan-cancer. Multidimensional analyses including single-cell metabolic profiling were carried out to deeply explore the complex tumor microenvironment of different types of human cancer. More interestingly, an interactive analysis platform encompassing four modules and 14 functions was equipped in the database.
Year founded:	2021
Last update:	2024-10-12
Version:	2.0
Accessibility:	Accessible
Country/Region:	China

Data type:	RNA
Data object:	Animal
Database category:	Expression Interaction Metadata
Major species:	Homo sapiens
Keywords:	scRNA-seq cancer tumor microenvironment (TMI) expression

University/Institution:	Beijing Institute of Genomics, Chinese Academy of Sciences
Address:	No. 104 building, No.1 Beichen West Road, Chaoyang District
City:	Beijing
Province/State:	Beijing
Country/Region:	China
Contact name (PI/Team):	Jingfa Xiao
Contact email (PI/Helpdesk):	xiaojingfa@big.ac.cn

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Database Commons a catalog of worldwide biological databases