A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk.

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation.

Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer.

In our previous study, we developed a genome-wide DNA methylation model for the diagnosis of prostate cancer, and we pointed out that a considerable average error is associated with the current method for the diagnosis of prostate cancer, which is predicated on pathological assessment of biopsied tissue. In this study, we utilized whole exome and transcriptome RNA-sequencing (RNA-seq) data that were derived from 468 tumor samples and 51 normal samples of prostatic tissue, and we analyzed over 20,000 genes per sample. We were able to develop a mathematical model that classified tumor tissue versus normal tissue with a high accuracy.