The Non-Coding Transcriptome of Prostate Cancer: Implications for Clinical Practice

Prostate cancer (PCa) is the most common type of cancer and the second leading cause of cancer-related death in men. Despite extensive research, the molecular mechanisms underlying PCa initiation and progression remain unclear, and there is increasing need of better biomarkers that can distinguish indolent from aggressive and life-threatening disease. With the advent of advanced genomic technologies in the last decade, it became apparent that the human genome encodes tens of thousands non-protein-coding RNAs (ncRNAs) with yet to be discovered function. It is clear now that the majority of ncRNAs exhibit highly specific expression patterns restricted to certain tissues and organs or developmental stages and that the expression of many ncRNAs is altered in disease and cancer, including cancer of the prostate. Such ncRNAs can serve as important biomarkers for PCa diagnosis, prognosis, or prediction of therapy response. In this review, we give an overview of the different types of ncRNAs and their function, describe ncRNAs relevant for the diagnosis and prognosis of PCa, and present emerging new aspects of ncRNA research that may contribute to the future utilization of ncRNAs as clinically useful therapeutic targets

DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000