CTR-DB 2.0:更新的癌症临床转录组资源,扩展了原发性耐药性数据集,并新增加了获得的耐药性数据集,加强了预测性生物标志物的发现和验证。

IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Nucleic Acids Research Pub Date : 2024-11-04 DOI:10.1093/nar/gkae993
Jianzhou Jiang, Yajie Ma, Lele Yang, Shurui Ma, Zixuan Yu, Xinyi Ren, Xiangya Kong, Xinlei Zhang, Dong Li, Zhongyang Liu
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引用次数: 0

摘要

耐药性是限制癌症治疗的主要因素。癌症治疗反应基因特征数据库(Cancer Treatment Response gene signature DataBase,CTR-DB)是首个具有癌症治疗反应的临床转录组数据资源,同时支持多种数据分析功能,有助于深入了解耐药性的分子决定因素。在此,我们提出了一个升级版本,即 CTR-DB 2.0 (http://ctrdb.ncpsb.org.cn)。目前已收集到约 190 个包含主要耐药性信息的最新源数据集(与 1.0 版相比增加了 129%)和 13 个获得的耐药性数据集(一种新的数据集类型),涵盖 10 856 个患者样本(增加了 111%)、39 种癌症类型(增加了 39%)和 346 种治疗方案(增加了 26%)。在功能方面,针对单数据集分析和多数据集比较模块,CTR-DB 2.0 新增了基因组富集、肿瘤微环境(TME)和特征连通性分析功能,有助于阐明耐药机制及其同质性/异质性,发现候选联合疗法。此外,还大大扩展了生物标记相关功能。CTR-DB 2.0 新支持将 TME 中的细胞类型验证为治疗反应的预测性生物标志物,特别是验证组合生物标志物面板,甚至使用用户定制的 CTR-DB 患者样本直接发现最佳生物标志物面板。此外,还增加了分析用户自己的数据集、应用程序接口和数据众筹等功能。
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CTR-DB 2.0: an updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers.

Drug resistance is a principal limiting factor in cancer treatment. CTR-DB, the Cancer Treatment Response gene signature DataBase, is the first data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. Here we proposed an upgraded version, CTR-DB 2.0 (http://ctrdb.ncpsb.org.cn). Around 190 up-to-date source datasets with primary resistance information (129% increase compared to version 1.0) and 13 acquired-resistant datasets (a new dataset type), covering 10 856 patient samples (111% increase), 39 cancer types (39% increase) and 346 therapeutic regimens (26% increase), have been collected. In terms of function, for the single dataset analysis and multiple-dataset comparison modules, CTR-DB 2.0 added new gene set enrichment, tumor microenvironment (TME) and signature connectivity analysis functions to help elucidate drug resistance mechanisms and their homogeneity/heterogeneity and discover candidate combinational therapies. Furthermore, biomarker-related functions were greatly extended. CTR-DB 2.0 newly supported the validation of cell types in the TME as predictive biomarkers of treatment response, especially the validation of a combinational biomarker panel and even the direct discovery of the optimal biomarker panel using user-customized CTR-DB patient samples. In addition, the analysis of users' own datasets, application programming interface and data crowdfunding were also added.

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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.
期刊最新文献
A genomic database furnishes minimal functional glycyl-tRNA synthetases homologous to other, designed class II urzymes. ALS-associated FUS mutation reshapes the RNA and protein composition of stress granules. COG database update 2024. CTR-DB 2.0: an updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers. DNAproDB: an updated database for the automated and interactive analysis of protein-DNA complexes.
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