CTPC, a combined transcriptome data set of human prostate cancer cell lines

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Cell lines are crucial models in cancer research.
• Transcriptomic data from prostate cancer (PCa) cell lines facilitates the study of differential gene expression.
• Understanding gene expression variations across PCa cell lines is vital for research.

Purpose of the Study:

• To establish a comprehensive dataset of prostate cancer cell line transcriptomics.
• To provide a resource for comparing gene expression across multiple PCa cell lines.
• To develop a user-friendly platform for data visualization and analysis.

Main Methods:

• Large-scale data mining was employed to create the dataset.
• The Combined Transcriptome dataset of PCa Cell lines (CTPC) was curated.
• Transcriptomic data from 1840 samples across 9 common PCa cell lines were included.

Main Results:

• The CTPC dataset enables comparison of gene expression across PCa cell lines.
• Researchers can retrieve experiment information and associate gene expression with metadata (e.g., gene manipulation, drug treatment).
• A web-based platform was developed for data visualization (https://pcatools.shinyapps.io/CTPC_V2/).

Conclusions:

• The CTPC dataset and platform offer a valuable resource for the PCa research community.
• Facilitates deeper insights into PCa biology through comparative transcriptomics.
• Aims to advance prostate cancer research through accessible data and tools.