For patients with prostate cancer (PCa), pelvic lymph node (LN) metastasis remains a major poor prognostic factor associated with cancer-specific mortality. VEGF-C is a major lymphangiogenic ligand that plays a vital role in LN metastasis in PCa. However, in some PCa caseswith LN metastasis,VEGF-C is not upregulated, indicating that some VEGF-C-independent mechanisms are essential for lymphangiogenesis.Herein, we confirmed that extracellular vesicles (EVs) derived from PCa cells could promote LN metastasis in PCa independent of VEGF-C. We identified an EV circular RNA, circPDLIM5, that could promote lymphangiogenesis and lymphatic metastasis in both PCa cell lines and mouse models. Mechanistically, the packaging of circPDLIM5 into EVs was regulated by heterogeneous nuclear ribonucleoprotein A2B1. Subsequently, EVs were transmitted to human lymphatic endothelial cells, and EVs carrying circPDLIM5 could then directly interact with the transcription factor Yin Yang 1 to enhance the expression of Prospero homeobox 1, which is crucial for the formation, differentiation, and maturation of lymphatic vessels. Our findingshighlight the importance of a molecular mechanism mediated by EVs carrying circPDLIM5that is involved in lymphangiogenesis and LN metastasis in PCa; as a result, EVscarrying circPDLIM5 may be an attractive therapeutic target for LN-metastatic PCa.

Prostate cancer (PRAD) is one of the common malignant tumors of the urinary system. In order to predict the treatment results for PRAD patients, this study proposes to develop a risk profile based on endoplasmic reticulum stress (ERS). Based on the Memorial Sloan-Kettering Cancer Center (MSKCC) cohort and the Gene Expression Omnibus database (GSE70769), we verified the predictive signature. Using a random survival forest analysis, prognostically significant ERS-related genes were found. An ERS-related risk score (ERscore) was created using multivariable Cox analysis. In addition, the biological functions, genetic mutations and immune landscape related to ERscore are also studied to reveal the underlying mechanisms related to ERS in PRAD. We further explored the ERscore-related mechanisms by profiling a single-cell RNA sequencing (scRNA-seq) dataset (GSE137829) and explored the oncogenic role of ASNS in PRAD through in vitro experiments. The risk signature composed of eight ERS-related genes constructed in this study is an independent prognostic factor and validated in the MSKCC and GSE70769 data sets. The scRNA-seq data additionally revealed that several carcinogenic pathways were noticeably overactivated in the group with high ERS scores. As one of the prognostic genes, ASNS will significantly inhibit the proliferation, migration and invasion abilities of PRAD cells after its expression is interfered with. In conclusion, this study developed a novel risk-specific ERS-based clinical treatment strategy for patients with PRAD.

Background Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM. Methods An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort ( n = 413) as training set and validated in an independent multi-center prospective cohort ( n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score. Results An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85–1.01) and 0.93 (95% CI: 0.87–0.99) in the retrospective and prospective urine cohorts respectively. Kaplan–Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing. Conclusions The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

To construct a urine extracellular vesicle long non-coding RNA (lncRNA) classifier that can detect high-grade prostate cancer (PCa) of grade group 2 or greater and estimate the risk of progression during active surveillance, we identify high-grade PCa-specific lncRNAs by combined analyses of cohorts from TAHSY, TCGA, and the GEO database. We develop and validate a 3-lncRNA diagnostic model (Clnc, being made of AC015987.1, CTD-2589M5.4, RP11-363E6.3) that can detect high-grade PCa. Clnc shows higher accuracy than prostate cancer antigen 3 (PCA3), multiparametric magnetic resonance imaging (mpMRI), and two risk calculators (Prostate Cancer Prevention Trial [PCPT]-RC 2.0 and European Randomized Study of Screening for Prostate Cancer [ERSPC]-RC) in the training cohort (n = 350), two independent cohorts (n = 232; n = 251), and TCGA cohort (n = 499). In the prospective active surveillance cohort (n = 182), Clnc at diagnosis remains a powerful independent predictor for overall active surveillance progression. Thus, Clnc is a potential biomarker for high-grade PCa and can also serve as a biomarker for improved selection of candidates for active surveillance.

Abstract Background There is an urgent need to accurately predict the risk of distant metastasis and metastatic castration-resistant prostate cancer (mCRPC) for treatment decision-making and reducing mortality. An artificial intelliegnce machine learning screening method in combination with liquid biopsy urine test was used to develop a novel gene expression-based algorithm for predicting prostate cancer distant metastasis and mCRPC in newly diagnosed patients. Methods Random forest machine learning algorithm screening was conducted to develop and validate a gene expression-based algorithm to predict the risk of metastasis and mCRPC using liquid biopsy urine samples from the patients with distant metastasis and mCRPC collected from multi-center retrospective (n = 505) and prospective (n = 243) studies with a median follow-up period of 8 and 6 years respectively. The prognostic performance of the algorithm test was assessed using univariate/multivariate Cox regression analyses, Kaplan-Meier disease-free survival plot, and univariate/multivariate discriminant analyses. Results A novel 18-Gene Algorithm urine test was developed and validated. The algorithm showed high accuracy to predict distant metastasis with an area under the curve (AUC) of 0.96 (95% CI 0.87–1.05) and 0.98 (95% CI 0.96–1.02) in the retrospective and prospective cohort respectively. In the prospective cohort, a hazard ratio (HR) to predict metastasis-free survival was 93.8 (95% CI 29.3-300.6) (p < 0.0001). In a prospective mCRPC cohort (n = 205), the algorithm predicted mCRPC-free survival with a HR of 154.4 (95% CI 36.8-647.5) (p < 0.0001) and predicted mCRPC with AUC of 0.98 (95% CI 0.95–1.01). In contrast, currently using clinicopathological parameters, such as Gleason grade and pre-operative PSA, had much lower prognostic power. Conclusions The novel 18-Gene Algorithm is the first highly accurate and non-invasive liquid biopsy urine test to predict distant metastasis and mCRPC in newly diagnosed prostate cancer patients with the potential to improve prostate cancer treatment decision-making and reduce mortality.

Background We aimed to develop and validate a plasma extracellular vesicle circular RNA (circRNA)-based signature that can predict overall survival (OS) in first-line abiraterone therapy for metastatic castration-resistant prostate cancer (mCRPC) patients. Methods In total, 582 mCRPC patients undergoing first-line abiraterone therapy from four institutions were sorted by three phases. In the discovery phase, 30 plasma samples from 30 case-matched patients with or without early progression were obtained to generate circRNA expression profiles using RNA sequencing. In the training phase, differentially expressed circRNAs were examined using digital droplet PCR in a training cohort ( n = 203). The circRNA signature was constructed using a least absolute shrinkage and selection operator Cox regression to predict OS. In the validation phase, the prognostic ability of this signature was prospectively validated in two external cohorts (Cohort I, n = 183; Cohort II, n = 166). Results We developed a five-circRNA signature, based on circCEP112, circFAM13A, circBRWD1, circVPS13C and circMACROD2 , which successfully stratified patients into high-risk and low-risk groups. The prognostic ability of this signature was prospectively validated in two external cohorts ( P < 0.0001, P < 0.0001). Patients with high-risk scores had shorter OS than patients with low-risk scores. Conclusion This five-circRNA signature is a reliable predictor of OS for mCRPC patients undergoing abiraterone.

Background:Bladder cancer is ranked the second most frequent tumor among urological malignancies. The research strived to establish a prognostic model based on endoplasmic reticulum stress (ERS)-related long non-coding RNA (lncRNA) in bladder cancer.Methods:We extracted the ERS-related genes from the published research and bladder cancer data from the Cancer Genome Atlas database. ERS-related lncRNAs with prognostic significance were screened by univariate Cox regression, least absolute shrinkage and selection operator regression analysis and Kaplan-Meier method. Multivariate Cox analysis was leveraged to establish the risk score model. Moreover, an independent dataset, GSE31684, was used to validate the model's efficacy. The nomogram was constructed based on the risk score and clinical variables. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the ERS-related signature. Finally, we employed external datasets (GSE55433 and GSE89006) and qRT-PCR to investigate the expression profile of these lncRNAs in bladder cancer tissues and cells.Results:Six ERS-related lncRNAs were identified to be closely coupled with patients' prognosis. On this foundation, a risk score model was created to generate the risk score for each patient. The ERS-related risk score was shown to be an independent prognostic factor. And the results of GSE31684 dataset also supported this conclusion. Then, a nomogram was constructed based on risk scores and clinical characteristics, and proven to have excellent predictive value. Moreover, the gene function analysis demonstrated that ERS-related lncRNAs were closely linked to fatty extracellular matrix, cytokines, cell adhesion, and tumor pathways. Further analysis revealed the association of the 6-lncRNAs signature with gene mutations and immunity in bladder cancer. Finally, the external datasets and qRT-PCR verified high expressions of the ERS-related lncRNAs in bladder cancer tissues and cells.Conclusions:Overall, our findings indicated that ERS-related lncRNAs, which may affect tumor pathogenesis in a number of ways, might be exploited to assess the prognosis of bladder cancer patients.

Dear Editor, Currently no accurate prognostic test is available to predict prostate cancer (PCa) biochemical recurrence (BCR) after treatment or cancer metastasis.1-7 To address the unmet medical need, we developed a novel 23-Gene Classifier urine test as the first accurate and noninvasive tool for PCa prognosis with potential to improve cancer treatment. We used previously identified biomarkers with differential gene expression in PCa and benign prostate as candidates for BCR prediction and metastasis.8-10 Discriminant analysis was used to assess the ability of various combinations of mRNA expression quantities of the biomarker candidates in prostate tissue specimens collected before prostatectomy with BCR information during follow-up as classifiers to distinguish BCR and non-BCR patients. A 23-Gene Classifier consisting of PTEN, PIP5K1A, CDK1, TMPRSS2, ANXA3, HIF1A, FGFR1, BIRC5, AMACR, CRISP3, PMP22, GOLPH2, EZH2, GSTP1, PCA3, VEGFA, CST3, CCNA1, CCND1, FN1, MYO6, KLK3, and PSCA was found to predict BCR with the highest accuracy. We followed STARD guidelines for biomarker validation. Detailed patient cohorts and study methods are described in Supplementary Methods. The prostate epithelial cells are released into the urine so urine can be used as a noninvasive liquid biopsy source to detect prostate-specific biomarkers for PCa prognosis. The 23-Gene Classifier was developed as a urine test for BCR prognosis using urines collected without digital rectal examination (DRE). Using BCR Urine Prediction Algorithm, the mRNA levels of the 23 genes were used to generate a classification score to predict the patients as having BCR or Non-BCR (Supplementary Methods). A multicenter study was designed prospectively using retrospectively collected urine samples without DRE from 520 patients before prostatectomy or other treatments (IND-CHTN cohort). Forty-six patients developed BCR during the follow-up period averaging 8 years (Table 1). A total of 105 patients from the cohort were randomly selected as a training set to test the 23-Gene Classifier urine test for BCR prediction and the resulting area under the receiver operating characteristic curve (AUC) was 0.94 (95% CI 0.87-1.01). The prognostic performance of the 23-Gene Classifier urine test to predict BCR-free survival was validated in the remaining patients (n = 414). The patients were divided into two risk groups based on diagnosis by the 23-Gene Classifier and Kaplan-Meier survival analysis showed statistically significant association of the 23-Gene Classifier Negative group with shorter BCR-free survival (∼60% BCR-free survival at 48 months) as compared with the 23-Gene Classifier Positive group (100% BCR-free survival at 120 months) (Figure 1A) (log rank P = 0.000). In contrast, the two groups segregated by cancer stage or Gleason score had much smaller difference in BCR-free survival (Figures 1B and C). Univariate and multivariate Cox regression analysis was performed and the 23-Gene Classifier had a hazard ratio (HR) of 1730.90 (95% CI 4.52-6.63E+5) in the univariate analysis (Table 2), which indicated that the patients with a positive 23-Gene Classifier score was 1731 times more likely to have BCR than patients with a negative 23-Gene Classifier score and the BCR prediction was statistically significant (P = 0.014). Its predictive power remained large and significant in multivariate regression after adjusting for cancer stage and Gleason score with HR of 1795.01 (95% CI 4.30-7.49E+5) (P = 0.015). In contrast, cancer stage and Gleason score had much lower HR and were statistically insignificant (Table 2). In addition, univariate and multivariate logistic regression and discriminant analysis were performed to measure the predictive accuracy of the 23-Gene Classifier. The result showed high accuracy with sensitivity of 100% (95% CI 100-100%), specificity of 86.29% (95% CI 82.80-89.79%), and AUC of 0.93 (95% CI 0.90-0.96) (P < 0.0001) (Tables S1 and 3, Figure 1G). Cross-validation of the 23-Gene Classifier showed similarly high accuracy in BCR prediction (Table 3). In contrast, cancer stage and Gleason score had much lower specificity and AUC (Table 3, Figures 1H and I). 100% (100-100%) 7.28% (4.63-9.92%) 10.88% (7.77-13.99%) 100% (100-100%) 100% (100-100%) 2.43% (0.86-3.99%) 10.40% (7.42-13.37%) 100% (100-100%) 100% (100-100%) 86.29% (82.80-89.79%) 45.16% (35.05-55.28%) 100% (100-100%) 23G classifier Cross-validation 100% (100-100%) 86.17% (82.14-90.20%) 45.07% (33.50-56.64%) 100% (100-100%) 100% (100-100%) 88.11% (84.81-91.41%) 48.84% (38.27-59.40%) 100% (100-100%) 16.67% (4.49-28.84%) 99.04% (97.16-100.91%) 85.71% (59.79-111.64%) 77.44% (70.34-84.55%) 48.57% (32.01-65.13%) 96.12% (92.39-99.85%) 80.95% (64.16-97.75%) 84.62% (78.08-91.15%) 86.11% (74.81-97.41%) 100% (100-100%) 100% (100-100%) 95.41% (91.49-99.34%) 23G classifier Cross-validation 87.50% (64.58-110.42%) 100% (100-100%) 100% (100-100%) 96.97% (91.12-102.82%) 85.71% (74.12-97.31%) 100% (100-100%) 100% (100-100%) 95.37% (91.41-99.33%) In silico validation study was conducted to test if the 23-Gene Classifier can also be used in prostate tissue specimens for BCR prognosis using a tissue cohort MSKCC (Table 1). Its similarly high prognostic performance (Tables 2 and 3, Figures 1D-F, K-N) validated the results from the urine study and confirmed the 23-Gene Classifier as a more accurate prognostic tool for BCR prediction than cancer stage and Gleason score. Accurate prediction of cancer metastasis at diagnosis is important for patients to be treated early with effective therapies to prevent development of castration-resistant metastatic cancer and reduce mortality. We tested if the 23-Gene Classifier urine test could be used for metastatic cancer prediction. We tested its performance in the multicenter, retrospective IND-CHTN Cohort (n = 520), a multicenter, prospective 7-HOSPITALS Cohort (n = 207), and a combination cohort combining the patients (n = 727) (Table 1). mRNA expression quantities of the 23 genes were used to classifier each sample as metastatic or nonmetastatic cancer using MET Urine Prediction Algorithm and such classification was compared with the metastatic cancer diagnosis by the imaging measurements to calculate the predictive performance (Supplementary Methods). The result showed that the 23-Gene Classifier urine test had similarly high accuracy in predicting metastatic cancer in the retrospective, prospective and combination cohorts (AUC of 0.92 [95% CI 0.79-1.05] for the retrospective cohort, 0.89 [95% CI 0.83-0.95] for the prospective cohort, and 0.98 [95% CI 0.96-1.01] for the combination cohort) (P < 0.0001). In contrast, Gleason score had much lower specificity and AUC (Table S2 and Figure 2). Development of accurate and actionable prognostic tests is important and urgently needed for PCa treatment. None of the clinicopathological parameters, nomograms, or biomarker panels used in clinic or reported in publications was capable of accurately predicting BCR or cancer metastasis with HR above 20 or AUC above 0.9.1-7 The 23-Gene Classifier had HR above 40 and AUC above 0.9 in all cohorts assessed, suggesting its higher accuracy and more robust performance for PCa prognosis. In addition, the 23-Gene Classifier can be used with prostate tissue specimens. In this study, we developed and validated a novel 23-Gene Classifier that can be used as a highly accurate and noninvasive urine test for prediction of BCR and cancer metastasis with great potential to improve PCa treatment and reduce mortality in clinical practice. The authors would like to thank C. Yun for excellent technical support and S. Liao for skillful assistance in urine collection. The retrospective urine study was approved by IRB at San Francisco General Hospital (IRB #: 15–15816) to use archived urine sediment samples acquired from Cooperative Human Tissue Network Southern Division and Indivumed GmbH. These organizations obtained ethical approval and patient consent prior to collection of patient urine samples. The prospective urine study was approved by IRB at Shenzhen People's Hospital (Study Number: P2014-006) to use urine samples collected from patients treated at the collaborating hospitals in the study with prior consent. All authors have agreed to publish the manuscript. The data supporting this study are available from the corresponding authors upon reasonable request or are publicly available in GEO. Heather Johnson is an employee of Olympia Diagnostics, Inc., and inventor of a pending patent application of prostate cancer diagnostic and prognostic biomarkers. No conflict of interest or financial interest was declared by the other authors. This study was supported by grants from Sanming Project of Medicine in Shenzhen (SZSM201412014), The Science and Technology Foundation of Shenzhen (JCYJ20170307095620828), The Science and Technology Foundation of Shenzhen (JCYJ20160422145718224), and The Shenzhen Urology Minimally Invasive Engineering Center (GCZX2015043016165448) (to Jinan Guo, and Kefeng Xiao); funds from Olympia Diagnostics, Inc. (to Heather Johnson); the Swedish Cancer Society (CAN2017/381), The Swedish Children Foundation (TJ2015-0097), H2020-MSCA-ITN-2018 GlycoImaging (721279), The Swedish National Research Council, the Malmö Cancer Foundation, the Government Health Innovation Grant, the Medical Faculty, Lund University, Kempestiftelserna, Umeå University, Medical Faculty Grants, the Norland Fund for Cancer Forskning, Insamlings Stiftelsen, Umeå University, Bioteknik medel, the Medical Faculty, Umeå University, Medical Faculty Grants, Umeå University, and grant from Umeå University Center for Microbiology Research (UCMR) and Biofilm Center at Malmö University (to Jenny Persson). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. HJ, CZ, LC, KX, and JLP contributed to study concept and design. HZ, JG, XF, CZ, KX, AHBW, and LC participated in study coordination and supervision. JG, TX, FL, and WT contributed to sample collection. XZ, JG, HJ, HZ, and XF contributed to sample processing and analysis. HZ, HJ, AJ, AS, ND, and JLP contributed to data collection and processing, and statistical analysis. HJ, PA, ND, LK, AS, and JLP contributed to data interpretation. XZ, HJ, and JLP contributed to literature search. JG, HJ, HZ, JLP, and CZ contributed to manuscript writing. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available. Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups. Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892–0.942) in the retrospective cohort ( n = 520), AUC of 0.959 (95% CI 0.935–0.983) in the prospective cohort ( n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort ( n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1–3 cancer subgroups. Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance.

Recently, studies on competing endogenous RNA (ceRNA) networks have become prevalent, and circular RNAs (circRNAs) have crucial implications for the development and progression of carcinoma. However, studies relevant to metastatic prostate cancer (mPCa) are scant. This study aims to discover potential ceRNAs that may be related to the prognosis of mPCa. RNA-Seq data were obtained from the MiOncoCirc database and Gene Expression Omnibus (GEO). Differential expression patterns of RNAs were examined using R packages. Circular RNA Interactome, miRTarBase, miRDB and TargetScan were applied to predict the corresponding relation between circRNAs, miRNAs and mRNAs. The Gene Ontology (GO) annotations were performed to present related GO terms, and Gene Set Enrichment Analysis (GSEA) tools were applied for pathway annotations. Moreover, survival analysis was conducted for the hub genes. We found 820 circRNAs, 81 miRNAs and 179 mRNAs that were distinguishingly expressed between primary prostate cancer (PCa) and mPCa samples. A ceRNA network including 45 circRNAs, 24 miRNAs and 56 mRNAs was constructed. In addition, the protein-protein interaction (PPI) network was built, and 10 hub genes were selected by using the CytoHubba application. Among the 10 hub genes, survival analysis showed that ITGA1, LMOD1, MYH11, MYLK, SORBS1 and TGFBR3 were significantly connected with disease-free survival (DFS). The circRNA-mediated ceRNA network provides potential prognostic biomarkers for metastatic prostate cancer.

Background Heterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies. Methods Using a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy. Results The 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963–0.929) in the retrospective cohort ( n = 614), AUC of 0.901 (0.929–0.873) in the prospective cohort ( n = 396), and AUC of 0.936 (0.956–0.916) in the large combination cohort ( n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors ( p < 0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980–0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947–0.909) in the combination cohort ( n = 727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy. Conclusions The 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.

目的 探讨尿液Dachshund同源基因2(DACH2)测定对膀胱尿路上皮癌的诊断价值.方法 收集因可疑膀胱肿瘤需行经尿道膀胱病变切除术患者的尿液,采用放射免疫法检测尿液DACH2水平,采用受试者工作特征(ROC)曲线分析尿液DACH2水平对膀胱尿路上皮癌的诊断效能.结果 共纳入72例患者,按照术后病理结果 分为膀胱尿路上皮癌组(肿瘤组)49例、膀胱良性疾病(包括慢性膀胱炎和尿路上皮增生)组(良性组)23例.肿瘤组患者术前尿液DACH2水平高于良性组(P<0.05).肿瘤组中,高级别者术前尿液DACH2水平高于低级别者(P<0.05).尿液DACH2水平诊断膀胱尿路上皮癌的ROC曲线下面积为0.796,最佳临界值为4.05 ng/ml,此时的灵敏度和特异度分别为86.7％和83.4％.结论 尿液DACH2测定可作为早期诊断膀胱尿路上皮癌的肿瘤标志物.

目的 探讨未阻断肾蒂血管后腹腔镜下肾部分切除术治疗T1a期肾癌的临床疗效.方法 回顾性分析2016年1月至2018年12月佛山市第一人民医院行后腹腔镜下肾部分切除术的56例(24例无肾蒂阻断,32例阻断肾蒂血管)T1a期肾癌的临床资料.结果 两组患者在术前平均血肌酐水平(无肾蒂阻断组72μmol/L;肾蒂阻断组75μmol/L)、平均手术时间(无肾蒂阻断组64 min;肾蒂阻断组60 min)、术后1个月平均血肌酐水平(无肾蒂阻断组75μmol/L;肾蒂阻断组82μmol/L)差异均无统计学意义(P>0.05).两组患者在术中平均出血量(无肾蒂阻断组为100 ml;肾蒂阻断组为44 ml)、术中热缺血时间(无肾蒂阻断组为0 min;肾蒂阻断组为25 min)差异均有统计学意义(P<0.05).无肾蒂阻断组术侧放射性核素断层扫描术前平均51 ml/min,术后1个月平均49 ml/min,差异无统计学意义(P>0.05).肾蒂阻断组术侧放射性核素断层扫描术前平均52 ml/min,术后1个月平均45 ml/min,差异有统计学意义(P<0.05).两组肾癌患者术后病理报告均为肾透明细胞癌,术后随访3～36个月,平均17个月,肿瘤无复发转移.结论 零热缺血后腹腔镜下肾部分切除术治疗T1a肾癌安全可行,有利于术侧肾单位及功能的保留.

Objective To compare the expression of T-cad protein in superficial bladder carcinoma and study the evaluation in diagnosis and follow-up. Methods Urine were collected from 50 cases with superficial bladder transitional cell carcinoma (TCC) and other 50 controls. T-cad protein levels in the urine of these cases were measured. The 50 superficial bladder TCC cases were received regular bladder perfusion chemotherapy and cystoscopy examination after transurethral resection of bladder tumor. T-cad expression levels in the tissues were examined by immunohistochemical staining, and their correlation with clinicopathologic parameters (e.g. G grade and recrudescence) were analyzed. Results The average urine T-cad concentration was (2.12±1.16) ng/ml in bladder TCC group, and was (3.03±1.97) ng/ml in the control group. There was no significant difference between them (P=0.08). Five cases (29.4%) had low or no expression of T-cad protein in the tissue of 17 patients with G1 superficial bladder TCC. Seventeen cases (51.5%) had low or no expression of T-cad protein in the tissue of 33 patients with G2-G3 superficial bladder TCC. The expression of T-cad in G1 was higher than that in G2-G3, and the difference was significant (P < 0.05). Recurrence rate was 17.9% in 28 bladder cancer patients with high T-cad expression, and was 54.5% in 22 bladder cancer patients with low T-cad expression. There was a significant difference between the two groups (P < 0.05). Conclusions Decreased T-cad expression in the tissue of bladder TCC was negatively correlated with high grade and tumor relapse, and may be associated with malignancy of bladder TCC. Key words: Superficial bladder cancer; T-cadherin

目的 探讨单平面超声和双平面超声介导下前列腺穿刺活检在前列腺癌诊断中的价值及其安全性,为前列腺癌术前诊断提供可靠的检查方法.方法 将194例行前列腺穿刺活检的患者随机分为单平面组和双平面组,在经直肠超声引导下行系统性前列腺穿刺活检术,观察两组在不同PSA水平,其前列腺癌的阳性检出率及并发症的发生率.结果 单平面组和双平面组前列腺癌阳性检出率分别为42.2％和34％,两者间差异无统计学意义(P>0.05);在PSA≤10、10<PSA≤30、PSA>30 ng/mL时,单平面组前列腺癌阳性检出率分别为12.5％、24.4％、72.5％,双平面组阳性检出率分别为4.5％、20.5％、66.7％,两组差异均无统计学意义(均P>0.05);结论 单平面和双平面超声介导下前列腺穿刺活检术均安全有效,两种穿刺方法阳性率无统计学差异.

Objective To observe the clinical effect ofureteral stent in coordinating extracorporeal shock wave lithotripsy for the treatment of upper urinary tract radioparent calculus.Methods From 2013 to 2016,182 cases of upper urinary tract radioparent calculus were collected.The ureteral stent (including common stent and double J tube) was used before the treatment.Results 165 cases were effective,and 17 cases were not effective,with the total effective rate of 90.65％.No severe complications were found in all cases.Conclusion Ureteral stent in coordinating extracorporeal shock wave lithotripsy for the treatment of upper urinary tract radioparent calculus is safe,effective,and easy and simple to handle.

Objective To evaluate the value of diffusion-weighted MR imaging in diagnosis of urothelial cancer. Methods From June 2015 to December 2016, 72 consecutive patients with suspicious bladder lesions were accessed in our hospital. According to the results of pathological examination, the patients were divided into two groups: benign lesions group (n=23) and urothelial cancer group (n=49). The apparent diffusion coefficient (ADC) values of all patients was analyzed. The receiver operating characteristic (ROC) curves were generated by plotting the sensitivity versus specificity. Area under the cures was calculated for ADC assay. Results There was significant difference in ADC values between benign lesions group and urothelial cancer group [(1.86±0.40)×10-3mm2/s vs (0.93±0.11) ×10-3mm2/s, P<0.01]. Area under the ROC curves of ADC values were 0.705. The specificity and sensitivity of ADC values were 82.9% and 87.2% respectively at the optimum cutoff value (1.32×10-3 ) mm2/s. Conclusions ADC value could provide an objective and reliable biomarker for the early diagnosis of bladder urothelial cancer.

目的:探讨BR-TRG-Ⅰ型体腔热灌注治疗仪实施膀胱内温热灌注化疗治疗高复发表浅性膀胱移行细胞癌的效果.方法:选取2011年3月～2014年10月收治的高复发表浅性膀胱移行细胞癌患者90例,随机分为研究组(n=45)和对照组(n=45).对照组患者术后24小时内行膀胱灌注,研究组患者术后3天行丝裂霉素膀胱腔内热灌注化疗.结果:研究组患者膀胱局部复发率为33.3％(15/45),显著低于对照组的77.8％ (35/45) (P＜0.05);复发时间和复发间期均显著长于对照组(P＜0.05);复发肿瘤个数显著少于对照组(P＜0.05).结论:BR-TRG-Ⅰ型体腔热灌注治疗仪实施膀胱内温热灌注化疗治疗高复发表浅性膀胱移行细胞癌患者效果显著,值得在临床推广.