Rhodes Daniel R, Yu Jianjun, Shanker K, Deshpande Nandan, Varambally Radhika, Ghosh Debashis, Barrette Terrence, Pandey Akhilesh, Chinnaiyan Arul M
Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Neoplasia. 2004 Jan-Feb;6(1):1-6. doi: 10.1016/s1476-5586(04)80047-2.
DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.
DNA微阵列技术引发了肿瘤基因组分析的热潮,产生了大量数据,揭示了癌症复杂的基因表达模式。遗憾的是,由于缺乏统一的生物信息资源,这些数据在发表后大多处于停滞和分散状态,癌症研究界对其利用严重不足。在此,我们推出了ONCOMINE,这是一个癌症微阵列数据库和基于网络的数据挖掘平台,旨在促进从全基因组表达分析中进行发现。截至目前,ONCOMINE包含65个基因表达数据集,涵盖了来自4700多个微阵列实验的近4800万个基因表达测量值。可进行将大多数主要癌症类型与相应正常组织进行比较的差异表达分析,以及各种癌症亚型分析、基于临床和基于病理的分析以供探索。可以针对所有分析中的选定基因或选定分析中的多个基因对数据进行查询和可视化。此外,基因集可以限制为包括分泌型、激酶、膜以及已知基因-药物靶点对在内的临床重要注释,以促进新型生物标志物和治疗靶点的发现。