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Background Prostate cancer (PCa) is the fifth leading cause of cancer-related death worldwide. Finding novel therapeutic strategies to tackle PCa, especially its most advanced phenotype, named castration-resistant PCa (CRPC), is urgently needed. In this sense, although the dysregulation of the splicing process has emerged as a distinctive feature of advanced PCa, the potential role that splicing regulators may play in advanced PCa remains understudied. In this project, we aimed to explore the levels, pathophysiological role, and associated molecular landscape of the splicing factor SRSF6 in PCa.Methods SRSF6 alterations (CNA/mRNA/protein) were analyzed in eight well-characterized cohorts of PCa patients and in the Hi-MYC transgenic model. The effect of SRSF6 overexpression and silencing was tested in vitro (cell proliferation, migration, colony and tumorspheres formation), and in vivo (xenograft tumors). RNA-Seq was performed in PCa cells to analyze gene expression and splicing pattern changes in response to SRSF6 silencing.Results Our results showed that SRSF6 levels (mRNA/protein) were upregulated in PCa vs. non-tumor prostate samples, linked to clinical parameters of tumor aggressiveness (e.g., Gleason score, T-stage, perineural infiltration, metastasis at diagnosis), and associated with poor prognosis (i.e., shorter progression-free survival time) in PCa patients. Moreover, SRSF6 overexpression increased, while its silencing decreased, relevant functional parameters of aggressiveness in vitro and tumor growth in vivo. Mechanistically, SRSF6 modulation resulted in the dysregulation of key oncogenic pathways, especially AR-activity through transcriptional regulation of APPBP2 and TOP2BConclusions SRSF6 could represent a new therapeutic target to inhibit persistent AR-signaling in advanced PCa.

(1) Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Wide application of prostate specific antigen test has historically led to over-treatment, starting from excessive biopsies. Risk calculators based on molecular and clinical variables can be of value to determine the risk of PCa and as such, reduce unnecessary and invasive biopsies. Urinary molecular studies have been mostly focusing on sampling after initial intervention (digital rectal examination and/or prostate massage). (2) Methods: Building on previous proteomics studies, in this manuscript, we aimed at developing a biomarker model for PCa detection based on urine sampling without prior intervention. Capillary electrophoresis coupled to mass spectrometry was applied to acquire proteomics profiles from 970 patients from two different clinical centers. (3) Results: A case-control comparison was performed in a training set of 413 patients and 181 significant peptides were subsequently combined by a support vector machine algorithm. Independent validation was initially performed in 272 negative for PCa and 138 biopsy-confirmed PCa, resulting in an AUC of 0.81, outperforming current standards, while a second validation phase included 147 PCa patients. (4) Conclusions: This multi-dimensional biomarker model holds promise to improve the current diagnosis of PCa, by guiding invasive biopsies.

Simple SummaryOur study used NanoString technology, a high-throughput platform measuring gene expression at the mRNA level to identify a set of genes predictive of clinical outcomes in bladder cancer patients. Twenty-seven differentially expressed genes were correlated with clinicopathological variables including molecular subtypes (luminal, basal, null/double-negative), histological subtypes (conventional urothelial carcinoma or carcinoma with variant histology), clinical subtype (NMIBC and MIBC), tumor stage category (Ta, T1 and T2-4), tumor grade, PD-L1 expression (high vs. low expression), and clinical risk categories (low, intermediate, high, and very high). Then, two risk models integrating the molecular subtypes and the level of expression of TP53, CCND1 and MKI67 were developed. These models provided a score ranging from 0 (best prognosis) to 7 (worst prognosis) that could be used to predict patient’ outcome and guide treatment decisions in bladder cancer.AbstractThis study evaluated a panel including the molecular taxonomy subtype and the expression of 27 genes as a diagnostic tool to stratify bladder cancer patients at risk of aggressive behavior, using a well-characterized series of non-muscle invasive bladder cancer (NMIBC) as well as muscle-invasive bladder cancer (MIBC). The study was conducted using the novel NanoString nCounter gene expression analysis. This technology allowed us to identify the molecular subtype and to analyze the gene expression of 27 bladder-cancer-related genes selected through a recent literature search. The differential gene expression was correlated with clinicopathological variables, such as the molecular subtypes (luminal, basal, null/double negative), histological subtype (conventional urothelial carcinoma, or carcinoma with variant histology), clinical subtype (NMIBC and MIBC), tumor stage category (Ta, T1, and T2–4), tumor grade, PD-L1 expression (high vs. low expression), and clinical risk categories (low, intermediate, high and very high). The multivariate analysis of the 19 genes significant for cancer-specific survival in our cohort study series identified TP53 (p = 0.0001), CCND1 (p = 0.0001), MKI67 (p < 0.0001), and molecular subtype (p = 0.005) as independent predictors. A scoring system based on the molecular subtype and the gene expression signature of TP53, CCND1, or MKI67 was used for risk assessment. A score ranging from 0 (best prognosis) to 7 (worst prognosis) was obtained and used to stratify our patients into two (low [score 0–2] vs. high [score 3–7], model A) or three (low [score 0–2] vs. intermediate [score 3–4] vs. high [score 5–7], model B) risk categories with different survival characteristics. Mean cancer-specific survival was longer (122 + 2.7 months) in low-risk than intermediate-risk (79.4 + 9.4 months) or high-risk (6.2 + 0.9 months) categories (p < 0.0001; model A); and was longer (122 + 2.7 months) in low-risk than high-risk (58 + 8.3 months) (p < 0.0001; model B). In conclusion, the molecular risk assessment model, as reported here, might be used better to select the appropriate management for patients with bladder cancer. This study evaluated a panel including the molecular taxonomy subtype and the expression of 27 genes as a diagnostic tool to stratify bladder cancer patients at risk of aggressive behavior, using a well-characterized series of non-muscle invasive bladder cancer (NMIBC) as well as muscle-invasive bladder cancer (MIBC). The study was conducted using the novel NanoString nCounter gene expression analysis. This technology allowed us to identify the molecular subtype and to analyze the gene expression of 27 bladder-cancer-related genes selected through a recent literature search. The differential gene expression was correlated with clinicopathological variables, such as the molecular subtypes (luminal, basal, null/double negative), histological subtype (conventional urothelial carcinoma, or carcinoma with variant histology), clinical subtype (NMIBC and MIBC), tumor stage category (Ta, T1, and T2–4), tumor grade, PD-L1 expression (high vs. low expression), and clinical risk categories (low, intermediate, high and very high). The multivariate analysis of the 19 genes significant for cancer-specific survival in our cohort study series identified TP53 (p = 0.0001), CCND1 (p = 0.0001), MKI67 (p < 0.0001), and molecular subtype (p = 0.005) as independent predictors. A scoring system based on the molecular subtype and the gene expression signature of TP53, CCND1, or MKI67 was used for risk assessment. A score ranging from 0 (best prognosis) to 7 (worst prognosis) was obtained and used to stratify our patients into two (low [score 0–2] vs. high [score 3–7], model A) or three (low [score 0–2] vs. intermediate [score 3–4] vs. high [score 5–7], model B) risk categories with different survival characteristics. Mean cancer-specific survival was longer (122 + 2.7 months) in low-risk than intermediate-risk (79.4 + 9.4 months) or high-risk (6.2 + 0.9 months) categories (p < 0.0001; model A); and was longer (122 + 2.7 months) in low-risk than high-risk (58 + 8.3 months) (p < 0.0001; model B). In conclusion, the molecular risk assessment model, as reported here, might be used better to select the appropriate management for patients with bladder cancer.

BackgroundProstate cancer (PCa) is the most common cancer and one of the leading causes of death worldwide. However, a significant proportion of PCa are low risk PCa which do not require an active treatment due to its low mortality rates. Thus, one major issue in PCa management is to accurately distinguish between indolent and clinically significant (cs) PCa to reduce overdiagnosis and overtreatment. In this study, we aim to validate the performance of diagnostic nomograms (DN) based on previously published urinary biomarkers for discriminating csPCa.Patients and MethodsCapillary electrophoresis/ mass spectrometry has been employed to validate a published biomarker model based on 19 urinary peptides specific for csPCa. Added value of the 19-biomarker model (19-BM) was assessed in diagnostic nomograms including prostate specific antigen (PSA), PSA density and the risk calculator from The European Randomized Study of Screening for Prostate Cancer (ERSPC). For this purpose, urine samples from 147 PCa patients (including 80 low, 44 intermediate and 17 high risk patients) were collected prior to prostate biopsy. The 19-BM score was calculated via a support vector machine-based software based on the pre-defined cut-off criterion of -0.07. DNs were subsequently developed to assess added value of integrative diagnostics.ResultsIndependent validation of the 19-BM resulted in 87% sensitivity and 65% specificity, with an AUC of 0.81, outperforming PSA (AUCPSA:0.64), PSA density (AUCPSAD: 0.64) and ERSPC-3/4 risk calculator (0.67). Integration of 19-BM with the other clinical variables into distinct DN, resulted in improved (AUC range: 0.82-0.88) but not significantly better performance compared to 19-BM alone.Conclusions19-BM alone or combined with clinical variables into DN, demonstrated value for detecting csPCa, and decreasing the number of biopsies.