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As new biomarkers, circular RNAs (circRNAs) have been largely unexplored in prostate cancer (PCa). Using an integrative approach, we aimed to evaluate the potential of circRNAs and their linear transcripts (linRNAs) to act as (i) diagnostic biomarkers for differentiation between normal and tumor tissue and (ii) prognostic biomarkers for the prediction of biochemical recurrence (BCR) after radical prostatectomy. In a first step, eight circRNAs (circ<i>ATXN10</i>, circ<i>CRIM1</i>, circ<i>CSNK1G3</i>, circ<i>GUCY1A2</i>, circ<i>LPP</i>, circ<i>NEAT1</i>, circ<i>RHOBTB3</i>, and circ<i>STIL</i>) were identified as differentially expressed via a genome-wide circRNA-based microarray analysis of six PCa samples. Additional bioinformatics and literature data were applied for this selection process. In total, 115 malignant PCa and 79 adjacent normal tissue samples were examined using robust RT-qPCR assays specifically established for the circRNAs and their linear counterparts. Their diagnostic and prognostic potential was evaluated using receiver operating characteristic curves, Cox regressions, decision curve analyses, and C-statistic calculations of prognostic indices. The combination of circ<i>ATXN10</i> and lin<i>STIL</i> showed a high discriminative ability between malignant and adjacent normal tissue PCa. The combination of lin<i>GUCY1A2</i>, lin<i>NEAT1</i>, and lin<i>STIL</i> proved to be the best predictive RNA-signature for BCR. The combination of this RNA signature with five established reference models based on only clinicopathological factors resulted in an improved predictive accuracy for BCR in these models. This is an encouraging study for PCa to evaluate circRNAs and their linRNAs in an integrative approach, and the results showed their clinical potential in combination with standard clinicopathological variables.