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Background and objective: To assess the effectiveness of a urine-based proenkephalin (PENK) methylation test using linear target enrichment-quantitative methylation-specific polymerase chain reaction (mePENK test) for detection of non–muscle-invasive bladder cancer (NMIBC) recurrence compared to cytology and the NMP22 test. Methods: We first conducted a retrospective case-control study involving 54 patients with primary BC and 29 healthy individuals. We then prospectively enrolled 186 patients (January to December 2022) undergoing cystoscopy surveillance after transurethral resection of bladder tumor, of whom 59 had recurrent tumors. We analyzed voided urine samples for PENK methylation levels in urinary DNA. Cystoscopy with histology was used as the reference standard for assessing the diagnostic accuracy of the mePENK test in detecting BC recurrence. We calculated the sensitivity and specificity using receiver operating characteristic curve analysis. Survival differences were determined using the Kaplan-Meier method and Cox proportional-hazards model. A p < 0.05 was considered statistically significant. Key findings and limitations: In the case-control study, the PENK test had sensitivity of 83.3% and specificity of 100%. For NMIBC patients undergoing cystoscopy surveillance, the sensitivity was 76.3% (95% confidence interval [CI] 63.4–86.4%) and the specificity was 85% (95% CI 77.6–90.7%), outperforming cytology (sensitivity: 28.8%, 95% CI 17.8–42.1%; p < 0.001; specificity: 97.6%, 95% CI 93.2–99.5%) and the NMP22 test (sensitivity: 54.2%, 95% CI 40.7–67.2%; p = 0.016; specificity 81.9%, 95% CI 74.1–88.2%). In the high-risk group, the mePENK test had sensitivity of 89.7% (95% CI 75.8–97.1%) and a negative predictive value of 96.9%. For the group with low/intermediate risk, the sensitivity was 41.7%. In the group with negative cystoscopy, recurrence-free survival was shorter for patients with positive than for those with negative mePENK results (245 vs 503 d), with a hazard ratio of 9.4 (p < 0.001). The main study limitation is the small sample size. Conclusions and clinical implications: The mePENK test showed good performance for detection of NMIBC recurrence and has potential for use for prognosis and prediction. Patient summary: We found that a test used to analyze urine samples showed good performance in detecting recurrence of NMIBC. This noninvasive mePENK test may help in personalized follow-up care for patients with NMIBC.

Purpose: We investigated whether the use of a phosphodiesterase-5 inhibitor (PDE5i) after robot assited radical prostatectomy has a survival benefit over non-use patients because there are controversial results on the association between PDE5i use and survival outcomes for prostate cancer patients in literature. Materials and Methods: We designed a retrospective, matched, large-sample cohort study of 5,545 patients who underwent robot assisted radical prostatectomy (RARP) during 2013–2021 in a single institute. The exclusion criteria was patients who were aged >70 years at surgery, American Society of Anesthesiologists (ASA) physical status classification grade 4 or 5, history of other malignancies, patients who started PDE5i 6 months after survery and patients with follow up period less than 24 months after surgery. Among the 1,843 included patients, 1,298 were PDE5i users, and 545 were PDE5i non-users. We performed propensity score matching (PSM) of PDE5i users (n=529) with non-users (n=529) by adjusting for the variables of age, Gleason grade group, pathological T stage, preoperative ASA physical status grade, and International Index of Erectile Function score. Results: There were no significant difference in patient characteristics according to PSM. Kaplan–Meier curve revealed the difference of overall survival for PDE5i users and non-users (clustered log-rank test p

As genetic testing plays an increasingly salient role in the realm of cancer diagnosis, prognostication, and treatment, this review aims to elucidate the current landscape and future directions of genetic testing in genitourinary cancers, with a focus on prostate cancer, urothelial carcinoma, and renal cell carcinoma. With the increasing adoption of next-generation sequencing technology, the utilization and access to genetic testing in real-world settings have become critical for practicing urologists and genitourinary oncologists, especially after the approval of poly(ADP-ribose) polymerase inhibitors for prostate cancer and the utilization of immune checkpoint inhibitors. In this rapidly evolving field, this review underscores the clinical value of interpreting genetic variations and the importance of distinguishing between germline and somatic mutations, for whom testing can be prescribed, and which genes should be tested. While the current modus operandi predominantly relies on exome sequencing, we posit that the future of genetic testing in genitourinary cancers will see an expansion to encompass whole-genome sequencing, accounting for structural and regulatory variations that impact gene expression. In the upcoming era of liquid biopsies, we envisage an increase in noninvasive cancer genetic testing for the purposes of diagnosis, prognosis, treatment response, and progression monitoring, supplementing the gold-standard tissue biopsies that provide histologic information. Ultimately, thoroughly interpreting genetic testing results and the subsequent treatment implications necessitates a multidisciplinary approach. This review strives to offer urologists a comprehensive perspective on genetic testing in these prevalent urological cancers, contributing to improved diagnosis, prognosis, and treatment decision-making.

Relaxin-like peptide family exhibit differential expression patterns in various types of cancers and play a role in cancer development. This family participates in tumorigenic processes encompassing proliferation, migration, invasion, tumor microenvironment, immune microenvironment, and anti-cancer resistance, ultimately influencing patient prognosis. In this review, we explore the mechanisms underlying the interaction between the RLN-like peptide family and tumors and provide an overview of therapeutic approaches utilizing this interaction.

Abstract Bladder cancer has a high recurrence rate which requires frequent follow-up. Cystoscopy is currently the gold standard for follow-up which is invasive and undesirable procedure for patients. We aimed to investigate the feasibility of noninvasive studies for follow-up of non-muscle invasive bladder cancer. This retrospective study was done for non-muscle invasive bladder cancer patients with abnormal lesion at follow up cystoscopy, therefore those needed transurethral resection of bladder tumor (TUR-BT). Inclusion criteria was patients who had preoperative bladder magnetic resonance imaging (MRI) within 1 month to TUR-BT and urine cytology results. MRI, urine cytology, and surgical pathology results were analyzed for sensitivity, specificity, positive and negative predictive values, accuracy, diagnostic odds ratio, and number needed to misdiagnose for the diagnostic performance of non-invasive studies. From total of 2,258 TUR-BT cases, 1,532 cases of primary TUR-BT and 481 cases which bladder MRI were not done was excluded. Finally, 245 cases of TUR-BT were included. Combined urine cytology and bladder MRI showed 96% sensitivity, 43% specificity, 89% positive and 67% negative predictive values, 87% accuracy, 16.2 diagnostic odds ratio, and 7.4 number needed to misdiagnose values. Among nine false-negative cases, three (1.2%) were missed by the radiologist, two (0.8%) had an empty bladder during magnetic resonance imaging, and three (1.2%) had gross hematuria which needed cystoscopy despite of bladder MRI or urine cytology result. Only one case (0.4%) was missed based on symptoms and noninvasive tests. However, none of the false-negative cases showed rapid extensive progression requiring radical or partial cystectomy. The combination of bladder MRI and urine cytology was comparable to cystoscopy for the follow-up of recurred lesions in non-muscle invasive bladder cancer patients for sensitivity, but not for specificity. However, it may reduce the need for cystoscopy and allowing patients to have choices for follow up diagnostic methods. Also, additional imaging tests to evaluate kidney, ureter and peri-vesical lesions can be reduced.

Abstract Background Prostate cancer (PCa) is characterized by complex genomic rearrangements such as the ETS oncogene family fusions, yet the clinical relevance is not well established. While paneled genetic tests of DNA repair genes are recommended in advanced PCa, conventional genomic or cytogenetic tools are not ideal for genome-wide screening of structural variations (SVs) such as balanced translocation due to cost and/or resolution issues. Methods In this study, we tested the feasibility of whole-genome optical genomic mapping (OGM), a newly developed platform for genome-wide SV analysis to detect complex genomic rearrangements in consecutive unselected PCa samples from MRI/US-fusion targeted biopsy. Results We tested ten samples, and nine (90%) passed quality check. Average mapping rate and coverage depth were 58.1 ± 23.7% and 157.3 ± 97.7×, respectively (mean ± SD). OGM detected copy number alterations such as chr6q13 loss and chr8q12-24 gain. Two adjacent tumor samples were distinguished by inter/intra-chromosomal translocations, revealing that they’re from the same ancestor. Furthermore, OGM detected large deletion of chr13q13.1 accompanied by inter-chromosomal translocation t(13;20)(q13.1;p13) occurring within BRCA2 gene, suggesting complete loss of function. Conclusion In conclusion, clinically relevant genomic SVs were successfully detected in PCa samples by OGM. We suggest that OGM can complement panel sequencing of DNA repair genes BRCA1/2 or ATM in high-risk PCa.

Background and Objectives: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN. Methods and Materials: This retrospective study included 785 patients (training set 706, test set 79) from six tertiary referral centers who underwent open or robotic PN. Forty-four perioperative features were used as inputs to train the AI prediction model. XG-Boost and genetic algorithms were used for the final model selection and to determine feature importance. The primary outcome measure was immediate postoperative serum creatinine (Cr) level. The secondary outcome was the incidence of AKI (estimated glomerular filtration rate (eGFR) < 60 mL/h). The average difference between the true and predicted serum Cr levels was considered the mean absolute error (MAE) and was used as a model evaluation parameter. Results: An AI model for predicting immediate postoperative serum Cr levels was selected from 2000 candidates by providing the lowest MAE (0.03 mg/dL). The model-predicted immediate postoperative serum Cr levels correlated closely with the measured values (R2 = 0.9669). The sensitivity and specificity of the model for predicting AKI were 85.5% and 99.7% in the training set, and 100.0% and 100.0% in the test set, respectively. The limitations of this study included its retrospective design. Conclusions: Our AI model successfully predicted accurate serum Cr levels and the likelihood of AKI. The accuracy of our model suggests that personalized guidelines to optimize multidisciplinary plans involving pre- and postoperative care need to be developed.

Summary: Bioinformatic analysis of 94 patient-derived xenografts (PDXs), cell lines, and organoids (PCOs) identifies three intrinsic transcriptional subtypes of metastatic castration-resistant prostate cancer: androgen receptor (AR) pathway + prostate cancer (PC) (ARPC), mesenchymal and stem-like PC (MSPC), and neuroendocrine PC (NEPC). A sizable proportion of castration-resistant and metastatic stage PC (M-CRPC) cases are admixtures of ARPC and MSPC. Analysis of clinical datasets and mechanistic studies indicates that MSPC arises from ARPC as a consequence of therapy-induced lineage plasticity. AR blockade with enzalutamide induces (1) transcriptional silencing of TP53 and hence dedifferentiation to a hybrid epithelial and mesenchymal and stem-like state and (2) inhibition of BMP signaling, which promotes resistance to AR inhibition. Enzalutamide-tolerant LNCaP cells re-enter the cell cycle in response to neuregulin and generate metastasis in mice. Combined inhibition of HER2/3 and AR or mTORC1 exhibits efficacy in models of ARPC and MSPC or MSPC, respectively. These results define MSPC, trace its origin to therapy-induced lineage plasticity, and reveal its sensitivity to HER2/3 inhibition.

Dilated cardiomyopathy (DCM) is a heart muscle disease that causes heart failure and is the leading cause for heart transplantation. It is a heart muscle disease resulted from a variety of genetics, toxic, metabolic, and infectious causes. One of the most prevalent genetic causes of DCM is a protein-truncating variant in the Titin gene (TTNtv). We have generated a human-induced pluripotent stem cell (hiPSC) line from patients who underwent heart transplantation due to DCM carrying a TTNtv mutation (c.70051C > T, p.Arg23351Ter) at the age of 20. The generated hiPSCs showed normal karyotype (46, XY) and expression of pluripotency markers, and were differentiated towards cardiomyocytes successfully.

Recent advances in convolutional neural network (CNN)-based object detection have a trade-off between accuracy and computational cost in various industrial tasks and essential consideration. However, the fully convolutional one-stage detector (FCOS) demonstrates low accuracy compared with its computational costs owing to the loss of low-level information. Therefore, we propose a module called a dual-path lightweight module (DLM) that efficiently utilizes low-level information. In addition, we propose a DLMFCOS based on DLM to achieve an optimal trade-off between computational cost and detection accuracy. Our network minimizes feature loss by extracting spatial and channel information in parallel and implementing a bottom-up feature pyramid network that improves low-level information detection. Additionally, the structure of the detection head is improved to minimize the computational cost. The proposed method was trained and evaluated by fine-tuning parameters through experiments and using public datasets PASCAL VOC 07 and MS COCO 2017 datasets. The average precision (AP) metric is used for our quantitative evaluation matrix for detection performance, and our model achieves an average 1.5% accuracy improvement at about 33.85% lower computational cost on each dataset than the conventional method. Finally, the efficiency of the proposed method is verified by comparing the proposed method with the conventional method through an ablation study.

Prostate cancer is a common form of cancer in men, and androgen-deprivation therapy (ADT) is often used as a first-line treatment. However, some patients develop resistance to ADT, and their disease is called castration-resistant prostate cancer (CRPC). Identifying potential therapeutic targets for this aggressive subtype of prostate cancer is crucial. In this study, we show that statins can selectively inhibit the growth of these CRPC tumors that have lost their androgen receptor (AR) and have overexpressed the RNA-binding protein QKI. We found that the repression of microRNA-200 by QKI overexpression promotes the rise of AR-low mesenchymal-like CRPC cells. Using in silico drug/gene perturbation combined screening, we discovered that QKI-overexpressing cancer cells are selectively vulnerable to CDC42-PAK7 inhibition by statins. We also confirmed that PAK7 overexpression is present in prostate cancer that coexists with hyperlipidemia. Our results demonstrate a previously unseen mechanism of action for statins in these QKI-expressing AR-lost CRPCs. This may explain the clinical benefits of the drug and support the development of a biology-driven drug-repurposing clinical trial. This is an important finding that could help improve treatment options for patients with this aggressive form of prostate cancer.

OBJECTIVES This study examined the association between high risk of Internet gaming disorder (IGD) and online game genres used by adolescents. METHODS The data derived from the baseline data of the Internet user Cohort for Unbiased Recognition of gaming disorder in Early Adolescence. A total 1,532 middle school students who use online games included. The participants reported the names of the online games they used during the past year. Game genres were categorized into role playing games (RPGs), shooting, multiplayer online battle arena (MOBA), simulation, arcade, sports and action games. The risk of IGD was measured using the Internet Gaming Use-Elicited symptom Screen. The relationship between the experience of online game genre and high risk of IGD was analyzed using multiple logistic regression model. RESULTS The game time of a student was longer if he or she had an experience of RPGs, shooting games, MOBA games, simulation games, and action games. The direct and independent association between high risk of IGD in adolescents and the genres of RPGs, simulation games and MOBA were found to be odds ratios 1.52 (95% confidence interval [CI], 1.03 to 2.26); 1.59 (95% CI, 1.03 to 2.45); and 1.51 (95% CI, 1.03 to 2.21), respectively after adjusted the potential confounding variables and the use of other online game genres. CONCLUSIONS The present cross-sectional study has found an association between online game genres and the risk of IGD in adolescents attending a school. A cohort study should verify the causal association in future.

High-grade serous carcinoma (HGSCa) of the ovary is featured by TP53 gene mutation. Missense or nonsense mutation types accompany most cases of HGSCa that correlate well with immunohistochemical (IHC) staining results—an all (missense) or none (nonsense) pattern. However, some IHCs produce subclonal or mosaic patterns from which TP53 mutation types, including the wild type of the gene, cannot be clearly deduced. We analyzed a total of 236 cases of ovarian HGSCa and tumors of other histology by matching the results of p53 IHC staining and targeted next-generation sequencing (TruSight Tumor 170 panel). Ambiguous IHCs that do not belong to the conventional “all or none” groups were reviewed to distinguish the true wild type (WT) from potentially pathogenic subclonal or mosaic patterns. There were about 9% of sequencing-IHC mismatching cases, which were enriched by the p53 c-terminal encoding nuclear localization signal and oligomerization domain, in which the subcellular locations of p53 protein were affected. Indeed, mutations in the oligomerization domain of the p53 protein frequently revealed an unmatched signal or cytosolic staining (L289Ffs*57 (Ins), and R342*). We conclude that both mutation types and IHC patterns of p53 are important sources of information to provide a precise diagnosis of HGSCa.

Convolutional-neural-network (CNN)-based methods are continuously used in various industries with the rapid development of deep learning technologies. However, an inference efficiency problem was reported in applications that require real-time performance, such as a mobile device. It is important to design a lightweight network that can be used in general-purpose environments such as mobile environments and GPU environments. In this study, we propose a lightweight network efficient shot detector (ESDet) based on deep training with small parameters. The feature extraction process was performed using depthwise and pointwise convolution to minimize the computational complexity of the proposed network. The subsequent layer was formed in a feature pyramid structure to ensure that the extracted features were robust to multiscale objects. The network was trained by defining a prior box optimized for the data set of each feature scale. We defined an ESDet-baseline with optimal parameters through experiments and expanded it by gradually increasing the input resolution for detection accuracy. ESDet training and evaluation was performed using the PASCAL VOC and MS COCO2017 Dataset. Moreover, the average precision (AP) evaluation index was used for quantitative evaluation of detection performance. Finally, superior detection efficiency was demonstrated through the experiment compared to the conventional detection method.

Segmentation of street scenes is a key technology in the field of autonomous vehicles. However, conventional segmentation methods achieve low accuracy because of the complexity of street landscapes. Therefore, we propose an efficient atrous residual network (EAR-Net) to improve accuracy while maintaining computation costs. First, we performed feature extraction and restoration, utilizing depthwise separable convolution (DSConv) and interpolation. Compared with conventional methods, DSConv and interpolation significantly reduce computation costs while minimizing performance degradation. Second, we utilized residual learning and atrous spatial pyramid pooling (ASPP) to achieve high accuracy. Residual learning increases the ability to extract context information by preventing the problem of feature and gradient losses. In addition, ASPP extracts additional context information while maintaining the resolution of the feature map. Finally, to alleviate the class imbalance between the image background and objects and to improve learning efficiency, we utilized focal loss. We evaluated EAR-Net on the Cityscapes dataset, which is commonly used for street scene segmentation studies. Experimental results showed that the EAR-Net had better segmentation results and similar computation costs as the conventional methods. We also conducted an ablation study to analyze the contributions of the ASPP and DSConv in the EAR-Net.