Identifying differentially expressed transcripts associated with prostate cancer progression using RNA-Seq and machine learning techniques

Background: Prostate cancer is complicated by a high level of unexplained variability in the aggressiveness of newly diagnosed disease. Given that this is one of the most prevalent cancers worldwide, finding biomarkers to effectively stratify high risk patient populations is a vital next step in improving survival rates and quality of life after treatment. Materials and Methods: In this study, we selected a dataset consisting of 106 prostate cancer samples, which represent various stages of prostate cancer and developed by RNA-Seq technology. Our objective is to identify differentially expressed transcripts associated with prostate cancer progression using pair-wise stage comparisons. Results: Using machine learning techniques, we identified 44 transcripts that are correlated to different stages of progression. Expression of an identified transcript, USP13, is reduced in stage T3 in comparison with stage T2c, a pattern also observed in breast cancer tumourigenesis. We also identified another differentially expressed transcript, PTGFR, which has also been reported to be involved in prostate cancer progression and has also been linked to breast, ovarian and renal cancers. Conclusions: The results support the use of RNA-Seq along with machine learning techniques as an essential tool in identifying potential biomarkers for prostate cancer progression. Further studies elucidating the biochemical role of identified transcripts in vitro are crucial in validating the use of these biomarkers in the prediction of disease progression and development of effective therapeutic strategies.