The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis

Abstract

Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research.

Molecular classification of hormone‐sensitive and castration‐resistant prostate cancer, using nonnegative matrix factorization molecular subtyping of primary and metastatic specimens

“…As expected, CRPC samples were over‐represented within this subtype, supporting previous observations of enrichment for somatic alterations in the DNA repair and androgen receptor signaling pathways in CRPC 20 . Metastatic specimens were also enriched within NMF4, consistent with previous literature identifying cell cycle control and DNA damage response as key pathways in progression to metastatic disease 21 . It is noteworthy that paired primary and metastatic specimens had mostly consistent NMF classification, lending support for the clinical utility of cNMF classification using FFPE tumor material from the original diagnosis.…”

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

“…These metabolic alterations have been observed at different omics levels [45][46][47]. For instance, transcriptomics analyses facilitated the identification of three distinct metabolismassociated PCa clusters and the development of a six-gene metabolic signature associated with disease-free survival [47].…”

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

“…Transcriptome analysis of 87 samples from 23 patients with localized prostate cancer reveals that the heterogeneity among tumor foci from one patient is similar to the heterogeneity among different patients [ 159 ]. A comprehensive multi-cohort survey of publicly available transcriptomic data from normal, primary and metastatic prostate tissues highlights the role of Myc , alpha-methylacyl-CoA racemase ( AMACR ) and glutathione S-transferase P ( GSTP ) in tumor initiation, and of AR , EZH2 , steroid 5a-reductase ( SRD5A ), tumor protein TP63 , centromere protein A ( CENPA ) and PI3K catalytic subunit b ( PIK3CB ) in tumor progression [ 160 ]. In addition, several disease-stage-specific genes with prognostic significance were described.…”