Abstract
Prostate cancer (PCa) is a heterogeneous disease with highly variable clinical outcomes which presents enormous challenges in the clinical management. A vast amount of transcriptomics data from large PCa cohorts have been generated, providing extraordinary opportunities for the molecular characterization of the PCa disease and the development of diagnostic and prognostic signatures. The lack of an inclusive collection and harmonization of the scattered public datasets constrains the extensive use of the valuable resources. In this study, we present a user-friendly database, PCaDB, for a comprehensive and interactive analysis and visualization of gene expression profiles from 77 transcriptomics datasets with 9,068 patient samples. PCaDB also includes a single-cell RNA-sequencing (scRNAseq) dataset for normal human prostates and 30 published PCa prognostic signatures. The comprehensive data resources and advanced analytical methods equipped in PCaDB would greatly facilitate data mining to understand the heterogeneity of PCa and to develop machine learning models for accurate PCa diagnosis and prognosis to assist on clinical decision-making. PCaDB is publicly available at http://bioinfo.jialab-ucr.org/PCaDB/.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Financial Support This work was supported by Z.J.’s UC Riverside Faculty Start-up Fund and UC Cancer Research Coordinating Committee Competition Award. J.Z. was supported by the Science and Technology Project of Guizhou Province in 2017 ([2017]5803), the High-level innovative talent project of Guizhou Province in 2018 ([2018]5639), and the Science and Technology Plan Project of Guiyang in 2019 ([2019]2-15). W.Z. was supported by the grants from National Natural Science Foundation of China (82072813, 8157142) and Guangzhou Municipal Science and Technology Project (201803040001).
Competing interests The authors declare no potential conflicts of interest