PSA screening has led to an over-diagnosis of prostate cancer (PCa) and unnecessary biopsies of benign conditions due to its low cancer specificity. Consequently, more accurate, preferentially non-invasive, tests are needed. We aim to evaluate the potential of semen sEV (small extracellular vesicles) tsRNAs (tRNA-derived small RNAs) as PCa indicators. Initially, following a literature review in the OncotRF database and high-throughput small RNA-sequencing studies in PCa tissue together with the sncRNA profile in semen sEVs, we selected four candidate 5′tRF tsRNAs for validation as PCa biomarkers. RT-qPCR analysis in semen sEVs from men with moderately elevated serum PSA levels successfully shows that the differential expression of the four tRFs between PCa and healthy control groups can be detected in a non-invasive manner. The combined model incorporating PSA and specific tRFs (5′-tRNA-Glu-TTC-9-1_L30 and 5′-tRNA-Val-CAC-3-1_L30) achieved high predictive accuracy in identifying samples with a Gleason score ≥ 7 and staging disease beyond IIA, supporting that the 5′tRF fingerprint in semen sEV can improve the PSA predictive value to discriminate between malignant and indolent prostate conditions. The in silico study allowed us to map target genes for the four 5′tRFs possibly involved in PCa. Our findings highlight the synergistic use of multiple biomarkers as an efficient approach to improve PCa screening and prognosis.

Small RNA-sequencing (small RNA-seq) has revealed the presence of small RNA-naturally occurring variants such as microRNA (miRNA) isoforms or isomiRs. Due to their small size and the sequence similarity among miRNA isoforms, their validation by RT-qPCR is challenging. We previously identified two miR-31-5p isomiRs—the canonical and a 3′isomiR variant (3′ G addition)—which were differentially expressed between individuals with azoospermia of different origin. Here, we sought to determine the discriminatory capacity between these two closely-related miRNA isoforms of three alternative poly(A) based-RT-qPCR strategies in both synthetic and real biological context. We found that these poly(A) RT-qPCR strategies exhibit a significant cross-reactivity between these miR-31-5p isomiRs which differ by a single nucleotide, compromising the reliable quantification of individual miRNA isoforms. Fortunately, in the biological context, given that the two miRNA variants show changes in the same direction, RT-qPCR results were consistent with the findings of small RNA-seq study. We suggest that miRNA selection for RT-qPCR validation should be performed with care, prioritizing those canonical miRNAs that, in small RNA-seq, show parallel/homogeneous expression behavior with their most prevalent isomiRs, to avoid confounding RT-qPCR-based results. This is suggested as the current best strategy for robust biomarker selection to develop clinically useful tests.

The need for prostate cancer (PCa)-specific biomarkers that enable more accurate detection of the disease and better prediction of tumor aggressiveness remains ongoing due to the low cancer specificity of PSA screening. Several potential mRNA markers for diagnosing PCa, in tissue and urine, have been reported in the literature. In this study, we aim to explore the potential of selected prostate-specific molecules and transcripts contained in small extracellular vesicles (sEVs) in semen to predict PCa risk reclassification for patients with moderately elevated PSA levels—a clinical scenario where identifying truly non-invasive biomarkers is especially critical. RT-qPCR analysis in semen sEVs successfully showed differential expression of KLK3 and PCA3 genes between PCa and healthy controls, whereas CREB3L4, CCNQ and DUSP23 levels were related to the severity or degree of PCa affectation. Our findings also present strong evidence that classifiers based on combined long transcript levels in semen sEVs serve as effective biomarkers. They can be used alone or in combination with blood PSA and/or semen citric acid levels to improve the diagnosis of PCa and assess its severity and disease progression with high accuracy. This strategy would allow a more comprehensive assessment, increase prognostic accuracy, and facilitate accurate clinical decision-making in the management of PCa.

PSA testing plays an important role in the diagnostic workup of prostate cancer; despite this, its cancer specificity is a well-recognised limitation. Consequently, more reliable, non-invasive diagnostic tools that are capable of improving specificity while maintaining sensitivity are needed, thereby enabling better risk stratification, personalised patient management, and reduction in unnecessary invasive procedures. In this study, we characterised the small RNA profile—including miRNAs and tsRNAs—in seminal small extracellular vesicles (sEVs) using high-throughput sequencing to expand biomarker discovery for distinguishing benign from malignant prostate conditions in patients with moderately elevated PSA levels, a setting where non-invasive biomarkers are critically needed. Our analysis confirms a small number of differentially represented sncRNA transcripts contained in seminal sEVs between benign and malignant prostate disease, most of which are of low abundance. This result suggests that shared underlying molecular mechanisms are likely to occur between both prostate disease conditions, especially in patients with moderate PSA levels. Subsequent RT-qPCR analysis revealed differences in expression in PCa compared with healthy controls but not when compared with benign prostatic disease. Given the complexity of the underlying pathophysiological process and the heterogeneity in clinical phenotypes, this limitation was addressed through the application of multivariate approaches (including isomiRs or tRFs), which are proposed as fluid-based biomarkers for prostate cancer, collectively offering improved accuracy and enhancing the negative predictive value of PSA used in clinical settings.