CTPC, a combined transcriptome data set of human prostate cancer cell lines
Siyuan Cheng, Xiuping Yu
Abstract
BACKGROUND: Cell lines are the most used model system in cancer research. The transcriptomic data of established prostate cancer (PCa) cell lines help researchers explore differential gene expressions across the various PCa cell lines. METHODS: Through large scale datamining, we established a curated Combined Transcriptome dataset of PCa Cell lines (CTPC) which contains the transcriptomic data of 1840 samples of 9 commonly used PCa cell lines including LNCaP, LNCaP-95, LNCaP-abl, C4-2, VCaP, 22Rv1, PC3, DU145, and NCI-H660. RESULTS: The CTPC dataset provides an opportunity for researchers to not only compare gene expression across different PCa cell lines but also retrieve the experiment information and associate the differential gene expression data with meta data, such as gene manipulation and drug treatment information. Additionally, based on the CTPC dataset, we built a platform for users to visualize the data (https://pcatools.shinyapps.io/CTPC_V2/). CONCLUSIONS: It is our hope that the combined CTPC dataset and the user-friendly platform are of great service to the PCa research community.