A splice variant of androgen receptor (AR), AR-V7, lacks in androgen-binding portion and leads to aggressive cancer characteristics. Reverse transcription-polymerase chain reactions (PCRs) and subsequent nested PCRs for the amplification of AR-V7 and prostate-specific antigen (PSA) transcripts were done for whole blood of patients with prostate cancer and male controls. With primary reverse transcription PCRs, AR-V7 and PSA were detected in 4.5% and 4.7% of prostate cancer, respectively. With nested PCRs, AR-V7 messenger RNA (mRNA) was positive in 43.8% of castration-sensitive prostate cancer and 48.1% of castration-resistant prostate cancer (CRPC), while PSA mRNA was positive in 6.3% of castration-sensitive prostate cancer and 18.5% of CRPC. Whole-blood samples of controls showed AR-V7 mRNA expression by nested PCR. Based on multivariate analysis, expression of AR-V7 mRNA in whole blood was not significantly correlated with clinical parameters and PSA mRNA in blood, while univariate analysis showed a correlation between AR-V7 mRNA and metastasis at initial diagnosis. Detection of AR-V7 mRNA did not predict the reduction of serum PSA in patients with CRPC following abiraterone and enzalutamide administration. In conclusion, AR-V7 mRNA expression in normal hematopoietic cells may have annihilated the manifestation of aggressiveness of prostate cancer and the prediction of the effectiveness of abiraterone and enzalutamide by the assessment of AR-V7 mRNA in blood.

Introduction: To predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning using a multilayer artificial neural network was investigated. Methods: A total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables, as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis, were input into the constructed multilayer artificial neural network (ANN) programs; 232 patients were used as training cases of ANN programs and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model. Results: With any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and nine explanatory variables, respectively, from 22. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5–10% higher compared to that with logistic regression analysis (LR). The area under the curves (AUC) with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise LR compared with the AUC with LR. The ANN had a higher net benefit than LR between prostate cancer probability cutoff values of 0.38 and 0.6. Conclusions: ANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

Various conditions including distal renal tubular acidosis (dRTA) can induce stone formation in the kidney. dRTA is characterized by an impairment of urine acidification in the distal nephron. dRTA is caused by variations in genes functioning in intercalated cells including SLC4A1/AE1/Band3 transcribing two kinds of mRNAs encoding the Cl−/HCO3− exchanger in erythrocytes and that expressed in α-intercalated cells (kAE1). With the acid-loading test, 25% of urolithiasis patients were diagnosed with incomplete dRTA. In erythroid intron 3 containing the promoter region of kAE1, rs999716 SNP showed a significantly higher minor allele A frequency in incomplete dRTA compared with non-dRTA patients. The promoter regions of the kAE1 gene with the minor allele A at rs999716 downstream of the TATA box showed reduced promoter activities compared that with the major allele G. Patients with the A allele at rs999716 may express less kAE1 mRNA and protein in the intercalated cells, developing incomplete dRTA.

Objectives; To predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning utilizing a multilayer artificial neural network was investigated.Materials and methods; A total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography-guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis were input into the constructed multilayer artificial neural network (ANN) programs. 232 patients were used as training cases of ANN programs, and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model.Results; With any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and 9 explanatory variables from 22, respectively. In common between them, age at biopsy, findings on digital rectal examination, findings in the peripheral zone on MRI diffusion-weighted imaging, and body mass index were positively influential variables, while numbers of previous prostatic biopsy and prostate volume were negatively influential. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5-10% higher compared with that with logistic regression analysis (LR). The AUCs with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise logistic regression compared with the AUC with LR. The ANN had a higher net-benefit than LR between prostate cancer probability cut-off values of 0.38 and 0.6.Conclusion; ANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

This study examined the effects of single-nucleotide polymorphisms (SNPs) on the development of bladder cancer, adding longest-held occupational and industrial history as regulators. The genome purified from blood was genotyped, followed by SNP imputation. In the genome-wide association study (GWAS), several patterns of industrial/occupational classifications were added to logistic regression models. The association test between bladder cancer development and the calculated genetic score for each gene region was evaluated (gene-wise analysis). In the GWAS and gene-wise analysis, the gliomedin gene satisfied both suggestive association levels of 10−5 in the GWAS and 10−4 in the gene-wise analysis for male bladder cancer. The expression of the gliomedin protein in the nucleus of bladder cancer cells decreased in cancers with a tendency to infiltrate and those with strong cell atypia. It is hypothesized that gliomedin is involved in the development of bladder cancer.