Using Saccharomyces cerevisiae as a model organism, we analyzed the consequences of disrupting mitochondrial function on mutagenesis of the nuclear genome. We measured the frequency of canavanine-resistant colonies as a measure of nuclear mutator phenotype. Our data suggest that mitochondrial dysfunction leads to a nuclear mutator phenotype (i) when oxidative phosphorylation is blocked in wild-type yeast at mitochondrial complex III by antimycin A and (ii) in mutant strains lacking the entire mitochondrial genome (rho(0)) or those with deleted mitochondrial DNA (rho(-)). The nuclear mutation frequencies obtained for antimycin A-treated cells as well as for rho(-) and rho(0) cells were approximately 2- to 3-fold higher compared to untreated control and wild-type cells, respectively. Blockage of oxidative phosphorylation by antimycin A treatment led to increased intracellular levels of reactive oxygen species (ROS). In contrast, inactivation of mitochondrial activity (rho(-) and rho(0)) led to decreased intracellular levels of ROS. We also demonstrate that in rho(0) cells the REV1, REV3 and REV7 gene products, all implicated in error-prone translesion DNA synthesis (TLS), mediate mutagenesis in the nuclear genome. However, TLS was not involved in nuclear DNA mutagenesis caused by inhibition of mitochondrial function by antimycin A. Together, our data suggest that mitochondrial dysfunction is mutagenic and multiple pathways are involved in this nuclear mutator phenotype.
Using two large patient cohorts, we searched for novel prostate cancer biomarkers in urine. We found two new sets of microRNA biomarkers in urine that could accurately predict the presence of prostate cancer and the likelihood of recurrence after prostatectomy. Further studies are needed before an actual clinical test can be developed.
Improved biomarkers for prostate cancer (PC) risk stratification are urgently needed. Here, we aimed to develop a novel multimarker model for prediction of biochemical recurrence (BCR) after curatively intended radical prostatectomy (RP), based on minimally invasive sampling of blood and urine. We initially measured the levels of 45 selected miRNAs by RT‐qPCR in exosome enriched cell‐free urine samples collected prior to RP from 215 PC patients (Cohort 1, training). We trained a novel logistic regression model (pCaP), comprising five urine miRNAs (miR‐151a‐5p, miR‐204‐5p, miR‐222‐3p, miR‐23b‐3p and miR‐331‐3p) and serum prostate‐specific antigen (PSA), which significantly predicted time to BCR in Cohort 1 (univariate Cox regression analysis: HR = 3.12, p < 0.001). Next, using the same exact numeric cutoff for dichotomization as trained in Cohort 1, we tested and successfully validated the prognostic potential of pCaP in two additional cohorts, including 199 (Cohort 2, HR = 2.24, p = 0.002) and 205 (Cohort 3, HR = 2.15, p = 0.004) RP patients, respectively. pCaP remained a significant predictor of BCR, also after adjustment for pathological T‐stage, surgical margin status and Gleason grade group (p < 0.05 in multivariate Cox regression analysis: HR = 2.72, 1.94 and 1.83 for Cohorts 1, 2 and 3, respectively). Additionally, pCaP scores correlated positively with the established clinical risk stratification nomogram CAPRA in all three PC cohorts (Pearson's rho: 0.45, 0.39 and 0.44). Together, our results suggest that the minimally invasive pCaP model could potentially be used in the future to improve PC risk stratification and to guide more personalized treatment decisions. Further clinical validation studies are warranted.
BackgroundNew molecular biomarkers for prostate cancer (PC) prognosis are urgently needed. Ratio-based models are attractive, as they require no additional normalization. Here, we train and independently validate a novel 4-miRNA prognostic ratio model for PC.Patients and methodsBy genome-wide miRNA expression profiling of PC tissue samples from 123 men who underwent radical prostatectomy (RP) (PCA123, training cohort), we identified six top candidate prognostic miRNAs and systematically tested their ability to predict postoperative biochemical recurrence (BCR). The best miRNA-based prognostic ratio model (MiCaP) was validated in two independent cohorts (PCA352 and PCA476) including >800 RP patients in total. Clinical end points were BCR and prostate cancer-specific survival (CSS). The prognostic potential of MiCaP was assessed by univariate and multivariate Cox-regression analyses and Kaplan–Meier analyses.ResultsWe identified a 4-miRNA ratio model, MiCaP (miR-23a-3p×miR-10b-5p)/(miR-133a×miR-374b-5p), that predicted time to BCR independently of routine clinicopathologic variables in the training cohort (PCA123) and was successfully validated in two independent RP cohorts. In addition, MiCaP was a significant predictor of CSS in univariate analysis [HR 3.35 (95% CI 1.34 − 8.35), P = 0.0096] and in multivariate analysis [HR 2.43 (95% CI 1.45–4.07), P = 0.0210]. As proof-of-principle, we also analyzed MiCaP in plasma samples from 111 RP patients. A high MiCaP score in plasma was significantly associated with BCR (P = 0.0036, Kaplan–Meier analysis). Limitations include low mortality rates (CSS: 5.4%).ConclusionsWe identified a novel 4-miRNA ratio model (MiCaP) with significant independent prognostic value in three RP cohorts, indicating promising potential to improve PC risk stratification.
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