Abstract
The discovery of prostate cancer biomarkers has been boosted by the advent of next-generation sequencing (NGS) technologies. Nevertheless, many challenges still exist in exploiting the flood of sequence data and translating them into routine diagnostics and prognosis of prostate cancer. Here we review the recent developments in prostate cancer biomarkers by high throughput sequencing technologies. We highlight some fundamental issues of translational bioinformatics and the potential use of cloud computing in NGS data processing for the improvement of prostate cancer treatment.
Highlights
Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer deaths among males in western societies [1]
The retrieved Gene Expression Omnibus (GEO) series generally fall into 5 categories: gene expression profiling, noncoding RNA profiling, genome binding/occupancy profiling, genome methylation profiling, and genome variation profiling
Grasso et al [36] sequenced the exomes of 11 treatment-naive and 50 lethal castrate-resistant prostate cancer (CRPC) and identified recurrent mutations in multiple chromatin- and histone-modifying genes, including MLL2 and FOXA1. These two studies reported mutated genes (SPOP [35] and CHD1 [36]) that may define prostate cancer subtypes which are ETS gene family fusion negative. Together these findings present a comprehensive list of specific genes that might be involved in prostate cancer and prioritize candidates for future study
Summary
Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer deaths among males in western societies [1]. Since its discovery over 20 years ago, Prostate Specific Antigen (PSA) has been the mainstay for diagnosis and prognosis of prostate cancer. PSA fails to differentiate PCa from common prostate disorders; it cannot discriminate between aggressive tumors and low-risk ones that may otherwise never have been diagnosed without screening [2]. The resolution for genomic mutation discovery was improved first with arraybased methods and with next-generation sequencing (NGS) technologies. These high throughput technologies open up the possibility to individualize the diagnosis and treatment of cancer. We overview the NGS-based strategies in prostate cancer research, with focus on upcoming biomarker candidates that show promise for the diagnosis and prognosis of prostate cancer. We outline future perspectives for translational informatics and cloud computation to improve prostate cancer management
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