BACKGROUND It is becoming increasingly evident that microRNAs (miRNA) are associated with the development and progression of prostate cancer (PCa). METHODS We examined the hypothesis that plasma miRNA levels can differentiate patients by aggressiveness in 82 PCa patients. Taqman based quantitative RT-PCR assays were performed to measure copy number of target miRNAs. RESULTS miR-20a was signficantly overexpressed in plasma from patients with stage 3 tumors compared to stage 2 or below (p=0.03). The expression levels for miR-20a and miR-21 were significantly increased in patients with high risk CAPRA scores (16,623 and 1,595 copies, respectively). Significantly increased miR-21 and miR-145 expression were observed for patients with intermediate or high risk D’Amico scores compared to patients with low risk scores (p=0.047 and 0.011, respectively). The relapse rates for CAPRA scores ranged from 1.9% for low risk to 9.5% for intermediate risk and to 22.2% for high risk patients (p=0.023). For D’Amico scores, the relapse rates ranged from 0.0% for low risk to 7.4% for intermediate risk and 17.6% for high risk patients (p=0.039). Expression of miR-21 and miR-221 significantly differentiated patients with intermediate risk from those with low risk CAPRA scores (AUC=0.801, p=0.002). Four miRNAs (miR-20a, miR-21, miR-145 and miR-221) could also distinguish high vs. low risk in PCa patients by D’Amico score with an AUC of 0.824. CONCLUSIONS These preliminary data suggest that altered plasma miRNAs may be useful predictors to distinguish PCa patients with varied aggressiveness. Further larger studies to validate this promising finding are warranted.

The LigaSure 5 was superior to the other devices tested regarding the ability to seal vessels up to 7 mm. The ACE is an efficient vessel sealing system with the ability to seal vessels up to 5 mm.

This study presents EliIE, an OMOP CDM-based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.