Endometrial cancer (EC) is the most frequent of the invasive tumors of the female genital tract. Although usually detected in its initial stages, a 20% of the patients present with advanced disease. To date, no characterized molecular marker has been validated for the diagnosis of EC. In addition, new methods for prognosis and classification of EC are needed to combat this deadly disease. We thus aimed to identify new molecular markers of EC and to evaluate their validity on endometrial aspirates. Gene expression screening on 52 carcinoma samples and series of real‐time quantitative PCR validation on 19 paired carcinomas and normal tissue samples and on 50 carcinoma and noncarcinoma uterine aspirates were performed to identify and validate potential biomarkers of EC. Candidate markers were further confirmed at the protein level by immunohistochemistry and Western blot. We identified ACAA1, AP1M2, CGN, DDR1, EPS8L2, FASTKD1, GMIP, IKBKE, P2RX4, P4HB, PHKG2, PPFIBP2, PPP1R16A, RASSF7, RNF183, SIRT6, TJP3, EFEMP2, SOCS2 and DCN as differentially expressed in ECs. Furthermore, the differential expression of these biomarkers in primary endometrial tumors is correlated to their expression level in corresponding uterine fluid samples. Finally, these biomarkers significantly identified EC with area under the receiver‐operating‐characteristic values ranging from 0.74 to 0.95 in uterine aspirates. Interestingly, analogous values were found among initial stages. We present the discovery of molecular biomarkers of EC and describe their utility in uterine aspirates. These findings represent the basis for the development of a highly sensitive and specific minimally invasive method for screening ECs.

Rapid and reliable diagnosis of prostate cancer (PCa) is highly desirable as current used methods lack specificity. In addition, identification of PCa biomarkers that can classify patients into high- and low-risk groups for disease progression at early stage will improve treatment decision-making. Here, we describe a set of protein-combination panels in urinary extracellular vesicles (EVs), defined by targeted proteomics and immunoblotting techniques that improve early non-invasive detection and stratification of PCa patients.We report a two-protein combination in urinary EVs that classifies benign and PCa patients (ADSV-TGM4), and a combination of five proteins able to significantly distinguish between high- and low-grade PCa patients (CD63-GLPK5-SPHM-PSA-PAPP). Proteins composing the panels were validated by immunohistochemistry assays in tissue microarrays (TMAs) confirming a strong link between the urinary EVs proteome and alterations in PCa tissues. Moreover, ADSV and TGM4 abundance yielded a high diagnostic potential in tissue and promising TGM4 prognostic power. These results suggest that the proteins identified in urinary EVs distinguishing high- and low grade PCa are a reflection of histological changes that may be a consequence of their functional involvement in PCa development. In conclusion, our study resulted in the identification of protein-combination panels present in urinary EVs that exhibit high sensitivity and specificity for PCa detection and patient stratification. Moreover, our study highlights the potential of targeted proteomic approaches–such as selected reaction monitoring (SRM)–as diagnostic assay for liquid biopsies via urinary EVs to improve diagnosis and prognosis of suspected PCa patients.

In order to successfully cure patients with prostate cancer (PCa), it is important to detect the disease at an early stage. The existing clinical biomarkers for PCa are not ideal, since they cannot specifically differentiate between those patients who should be treated immediately and those who should avoid over-treatment. Current screening techniques lack specificity, and a decisive diagnosis of PCa is based on prostate biopsy. Although PCa screening is widely utilized nowadays, two thirds of the biopsies performed are still unnecessary. Thus the discovery of non-invasive PCa biomarkers remains urgent. In recent years, the utilization of urine has emerged as an attractive option for the non-invasive detection of PCa. Moreover, a great improvement in high-throughput “omic” techniques has presented considerable opportunities for the identification of new biomarkers. Herein, we will review the most significant urine biomarkers described in recent years, as well as some future prospects in that field.

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Taken together, these results provide a strategy for the development of a more accurate model for PCa diagnosis. In the future, a multiplexed, urine-based diagnostic test for PCa with a higher specificity, but the same sensitivity as the serum-PSA test, could be used to determine better which patients should undergo biopsy.

Nowadays prostate cancer is the most common solid tumor in men from industrialized countries and the second leading cause of death. At the ages when PCa is usually diagnosed, mortality related to cardiovascular morbidity is high; therefore, men at risk for PCa frequently receive chronic lipid-lowering and antiplatelet treatment. The aim of this study was to analyze how chronic treatment with statins, aspirin, and their combination influenced the risk of PCa detection. The tumorigenic properties of these treatments were evaluated by proliferation, colony formation, invasion, and migration assays using different PCa cell lines, in order to assess how these treatments act at molecular level. The results showed that a combination of statins and aspirin enhances the effect of individual treatments and seems to reduce the risk of PCa detection (OR: 0.616 (95% CI: 0.467–0.812), P < 0.001). However, if treatments are maintained, aspirin (OR: 1.835 (95% CI: 1.068–3.155), P = 0.028) or the combination of both drugs (OR: 3.059 (95% CI: 1.894–4.939), P < 0.001) represents an increased risk of HGPCa. As observed at clinical level, these beneficial effects in vitro are enhanced when both treatments are administered simultaneously, suggesting that chronic, concomitant treatment with statins and aspirin has a protective effect on PCa incidence.

Prostate cancer (PCa) is the most frequently diagnosed type of cancer in developed countries. The decisive method of diagnosis is based on the results of biopsies, morphologically evaluated to determine the presence or absence of cancer. Although this approach leads to a confident diagnosis in most cases, it can be improved by using the molecular markers present in the tissue. Both miRNAs and proteins are considered excellent candidates for biomarkers in formalin-fixed paraffin-embedded (FFPE) tissues, due to their stability over long periods of time. In the last few years, a concerted effort has been made to develop the necessary tools for their reliable measurement in these types of samples. Furthermore, the use of these kinds of markers may also help in establishing tumor grade and aggressiveness, as well as predicting the possible outcomes in each particular case for the different treatments available. This would aid clinicians in the decision-making process. In this review, we attempt to summarize and discuss the potential use of microRNA and protein profiles in FFPE tissue samples as markers to better predict PCa diagnosis, progression, and response to therapy.

Using multiplex RTqPCR-based models in urine sediment it is possible to improve the current diagnostic method of choice (PCA3) to differentiate between benign HGPIN and PCa cases.