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
Abstract
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.
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.