Some tumors are known to have a definite cause-effect etiology, but renal cell carcinoma (RCC) is not one of them precisely. With regard to RCC we can only try to identify some clinical and occupational factors as well as substances related to tumorigenesis. Smoking, chemical carcinogens like asbestos or organic solvents are some of these factors that increase the risk of the RCC. Viral infections and radiation therapy have also been described as risk factors. Some drugs can increase the incidence of RCC as well as other neoplasms. Of course, genetics plays an outstanding role in the development of some cases of kidney cancer. Chronic renal failure, hypertension, and dialysis need to be considered as special situations. Diet, obesity, lifestyle, and habits can also increase the risk of RCC. The aim of this review is to summarize the well-defined causes of renal cell carcinoma.

Predicting biochemical recurrence after radical prostatectomy based on clinicopathological data can be significantly improved by including patient genetic information.

Objectives • To implement the use of nomograms in clinical practiceshowing how to choose thresholds in nomograms' predictions to select risk groups.• To validate and compare the predictive ability and clinical utility of the Hospital Universitario 'Miguel Servet' (HUMS) and the updated Partin Tables 2012 (PT-2012) nomograms to predict organ-confined disease (OCD) after radical prostatectomy (RP). Patients and Methods• Cohort of 1285 patients with prostate cancer treated with RP at Instituto Valenciano de Oncología (IVO) between 1986 and 2011.• The predictive value of the nomograms was assessed by means of calibration curves, discrimination ability (area under the receiver operating characteristic (ROC) curve (AUC) and probability density functions).• The clinical utility was evaluated through Vickers' decision curves and thresholds were chosen through probability density functions. Results• The calibration curves showed a minimal underestimation in low probabilities (<20%), a minimal overestimation in • The decision curves show similar net benefits for both models.• In this study we advocate for a threshold of 53% for the identification of OCD. Conclusions• The HUMS-nomogram and the PT-2012 predictions of OCD confirm their utility in a contemporary cohort of patients.• Patients with a probability of OCD >53% should be classified as OCD, helping physicians to better counsel their patients.• A selection of adequate thresholds, as presented in this paper, makes nomograms more accessible tools.

What's known on the subject? and What does the study add?• Currently available nomograms to predict preoperative risk of early biochemical recurrence (EBCR) after radical prostatectomy are solely based on classic clinicopathological variables. Despite providing useful predictions, these models are not perfect. Indeed, most researchers agree that nomograms can be improved by incorporating novel biomarkers. In the last few years, several single nucleotide polymorphisms (SNPs) have been associated with prostate cancer, but little is known about their impact on disease recurrence.• We have identified four SNPs associated with EBCR. The addition of SNPs to classic nomograms resulted in a significant improvement in terms of discrimination and calibration. The new nomogram, which combines clinicopathological and genetic variables, will help to improve prediction of prostate cancer recurrence. Objectives• To evaluate genetic susceptibility to early biochemical recurrence (EBCR) after radical prostatectomy (RP), as a prognostic factor for early systemic dissemination.• To build a preoperative nomogram to predict EBCR combining genetic and clinicopathological factors. Patients and Methods• We evaluated 670 patients from six University Hospitals who underwent RP for clinically localized prostate cancer (PCa), and were followed-up for at least 5 years or until biochemical recurrence.• EBCR was defined as a level prostate-specific antigen >0.4 ng/mL within 1 year of RP; preoperative variables studied were: age, prostate-specific antigen, clinical stage, biopsy Gleason score, and the genotype of 83 PCa-related single nucleotide polymorphisms (SNPs).• Univariate allele association tests and multivariate logistic regression were used to generate predictive models for EBCR, with clinicopathological factors and adding SNPs.• We internally validated the models by bootstrapping and compared their accuracy using the area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, calibration plots and Vickers' decision curves. Results• Four common SNPs at KLK3, KLK2, SULT1A1 and BGLAP genes were independently associated with EBCR.• A significant increase in AUC was observed when SNPs were added to the model: AUC (95% confidence interval) 0.728 (0.674-0.784) vs 0.763 (0.708-0.817).• Net reclassification improvement showed a significant increase in probability for events of 60.7% and a decrease for non-events of 63.5%. • Integrated discrimination improvement and decision curves confirmed the superiority of the new model. Conclusions• Four SNPs associated with EBCR significantly improved the accuracy of clinicopathological factors.• We present a nomogram for preoperative prediction of EBCR after RP.

It is shown a better prognostic relationship between PLR and NLR low values and OS that is statistically significant in mCPRC patients treated with abiraterone. Furthermore, it was not shown a relation between PLR and NLR values and PSA response.