肺癌原生模型基因表达数据库使跨小鼠模型转录组景观的交叉研究比较成为可能。

IF 12.5 1区 医学 Q1 ONCOLOGY
Ling Cai,Fangjiang Wu,Qinbo Zhou,Ying Gao,Bo Yao,Ralph J DeBerardinis,George K Acquaah-Mensah,Vassilis Aidinis,Jennifer E Beane,Shyam Biswal,Ting Chen,Carla P Concepcion-Crisol,Barbara M Grüner,Deshui Jia,Robert A Jones,Jonathan M Kurie,Min Gyu Lee,Per Lindahl,Yonathan Lissanu,Corina Lorz,David MacPherson,Rosanna Martinelli,Pawel K Mazur,Sarah A Mazzilli,Shinji Mii,Herwig P Moll,Roger A Moorehead,Edward E Morrisey,Sheng Rong Ng,Matthew G Oser,Arun R Pandiri,Charles A Powell,Giorgio Ramadori,Mirentxu Santos,Eric L Snyder,Rocio Sotillo,Kang-Yi Su,Tetsuro Taki,Kekoa Taparra,Phuoc T Tran,Yifeng Xia,J Edward van Veen,Monte M Winslow,Guanghua Xiao,Charles M Rudin,Trudy G Oliver,Yang Xie,John D Minna
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引用次数: 0

摘要

肺癌,癌症死亡的主要原因,表现出不同的组织学亚型和遗传复杂性。已经开发了许多临床前小鼠模型来研究肺癌,但是来自这些模型的数据是不同的,孤立的,并且难以集中比较。在这项研究中,我们建立了肺癌原生模型基因表达数据库(LCAMGDB),这是一个广泛的存储库,包含来自77个转录组数据集的1,354个样本,其中包括来自基因工程小鼠模型(GEMM)的974个样本,来自致癌诱导模型的368个样本和来自自发模型的12个样本。与数据存款人的精心管理和合作产生了一个强大而全面的数据库,提高了它所描绘的遗传景观的保真度。LCAMGDB将859个来自GEMMs的肿瘤与人类肺癌突变进行了比对,从而能够进行比较分析,并揭示了扩大GEMMs中遗传畸变多样性的迫切需要。为了配合这个资源,开发了一个web应用程序,为研究人员提供深入基因表达分析的直观工具。通过对基因表达数据的标准化再处理,LCAMGDB为交叉研究比较提供了一个强大的平台,为未来的研究奠定了基础,旨在弥合小鼠模型与人类肺癌之间的差距,提高翻译相关性。意义:肺癌原生模型基因表达数据库(LCAMGDB)为研究界研究小鼠模型肺癌生物学提供了一个全面、可访问的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models.
Lung cancer, the leading cause of cancer mortality, exhibits diverse histologic subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. In this study, we established the Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMM), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCAMGDB aligned 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in the GEMMs. To accompany this resource, a web application was developed that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCAMGDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance. Significance: The Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB) provides a comprehensive and accessible resource for the research community to investigate lung cancer biology in mouse models.
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来源期刊
Cancer research
Cancer research 医学-肿瘤学
CiteScore
16.10
自引率
0.90%
发文量
7677
审稿时长
2.5 months
期刊介绍: Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research. With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445. Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.
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