Disulfidptosis-related genes serve as potential prognostic biomarkers and indicate tumor microenvironment characteristics and immunotherapy response in prostate cancer.

Disulfidptosis, a newly identified programmed cell death pathway in prostate cancer (PCa), is closely associated with intracellular disulfide stress and glycolysis. This study aims to elucidate the roles of disulfidptosis-related genes (DRGs) in the pathogenesis and progression of PCa, with the goal of improving diagnostic and therapeutic approaches. We analyzed PCa datasets and normal tissue transcriptome data from TCGA, GEO, and MSKCC. Using consensus clustering analysis and LASSO regression, we developed a risk scoring model, which was validated in an independent cohort. The model's predictive accuracy was confirmed through Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and nomograms. Additionally, we explored the relationship between the risk score and immune cell infiltration, and examined the tumor microenvironment and somatic mutations across different risk groups. We also investigated responses to immunotherapy and drug sensitivity. Our analysis identified two disulfidosis subtypes with significant differences in survival, immune environments, and treatment responses. According to our risk score, the high-risk group exhibited poorer progression-free survival (PFS) and higher tumor mutational burden (TMB), associated with increased immune suppression. Functional enrichment analysis linked high-risk features to key cancer pathways, including the IL-17 signaling pathway. Moreover, drug sensitivity analysis revealed varied responses to chemotherapy, suggesting the potential for disulfidosis-based personalized treatment strategies. Notably, we identified PROK1 as a crucial prognostic marker in PCa, with its reduced expression correlating with disease progression. In summary, our study comprehensively assessed the clinical implications of DRGs in PCa progression and prognosis, offering vital insights for tailored precision medicine approaches.

Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer.

PURPOSE: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML models to predict biochemical recurrence (BCR) of prostate cancer (PCa). MATERIALS AND METHODS: A total of 647 PCa patients were retrospectively evaluated. Clinical parameters were identified using LASSO regression. Then, cohort was split into training and validation datasets with a ratio of 0.75:0.25 and BCR-related features were included in Cox regression and five ML algorithm to construct BCR prediction models. The clinical utility of each model was evaluated by concordance index (C-index) values and decision curve analyses (DCA). Besides, Shapley Additive Explanation (SHAP) values were used to explain the features in the models. RESULTS: We identified 11 BCR-related features using LASSO regression, then establishing five ML-based models, including random survival forest (RSF), survival support vector machine (SSVM), survival Tree (sTree), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and a Cox regression model, C-index were 0.846 (95%CI 0.796-0.894), 0.774 (95%CI 0.712-0.834), 0.757 (95%CI 0.694-0.818), 0.820 (95%CI 0.765-0.869), 0.793 (95%CI 0.735-0.852), and 0.807 (95%CI 0.753-0.858), respectively. The DCA showed that RSF model had significant advantages over all models. In interpretability of ML models, the SHAP value demonstrated the tangible contribution of each feature in RSF model. CONCLUSIONS: Our score system provide reference for the identification for BCR, and the crafting of a framework for making therapeutic decisions for PCa on a personalized basis.

Spontaneous Fusion with Transformed Mesenchymal Stromal Cells Results in Complete Heterogeneity in Prostate Cancer Cells.

Tumor cells gain advantages in growth and survival by acquiring genotypic and phenotypic heterogeneity. Interactions with bystander cells in the tumor microenvironment contribute to the progression of heterogeneity. We have shown that fusion between tumor and bystander cells is one form of interaction, and that tumor-bystander cell fusion has contrasting effects. By trapping fusion hybrids in the heterokaryon or synkaryon state, tumor-bystander cell fusion prevents the progression of heterogeneity. However, if trapping fails, fusion hybrids will resume replication to form derivative clones with diverse genomic makeups and behavioral phenotypes. To determine the characteristics of bystander cells that influence the fate of fusion hybrids, we co-cultured prostate mesenchymal stromal cell lines and their spontaneously transformed sublines with LNCaP as well as HPE-15 prostate cancer cells. Subclones derived from cancer-stromal fusion hybrids were examined for genotypic and phenotypic diversifications. Both stromal cell lines were capable of fusing with cancer cells, but only fusion hybrids with the transformed stromal subline generated large numbers of derivative subclones. Each subclone had distinct cell morphologies and growth behaviors and was detected with complete genomic hybridization. The health conditions of the bystander cell compartment play a crucial role in the progression of tumor cell heterogeneity.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

Artificial intelligence-aided detection for prostate cancer with multimodal routine health check-up data: an Asian multi-center study.

BACKGROUND: The early detection of high-grade prostate cancer (HGPCa) is of great importance. However, the current detection strategies result in a high rate of negative biopsies and high medical costs. In this study, the authors aimed to establish an Asian Prostate Cancer Artificial intelligence (APCA) score with no extra cost other than routine health check-ups to predict the risk of HGPCa. PATIENTS AND METHODS: A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses. RESULTS: Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively. CONCLUSIONS: The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.

Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study.

BACKGROUND: The introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML). METHODS: This study included a multicenter retrospective cohort and two prospective cohorts with 4747 cases from 9 hospitals across China. The multimodal data, including demographics, clinical characteristics, laboratory tests, and ultrasound reports, of consecutive participants were retrieved using extract-transform-load tools. AutoML was applied to explore potential data processing patterns and the most suitable algorithm to build the Prostate Cancer Artificial Intelligence Diagnostic System (PCAIDS). The diagnostic performance was determined by the receiver operating characteristic curve (ROC) for discriminating csPCa from insignificant prostate cancer (PCa) and benign disease. The clinical utility was evaluated by decision curve analysis (DCA) and waterfall plots. RESULTS: The random forest algorithm was applied in the feature selection, and the AutoML algorithm was applied for model establishment. The area under the curve (AUC) value in identifying csPCa was 0.853 in the training cohort, 0.820 in the validation cohort, 0.807 in the Changhai prospective cohort, and 0.850 in the Zhongda prospective cohort. DCA showed that the PCAIDS was superior to PSA or fPSA/tPSA for diagnosing csPCa with a higher net benefit for all threshold probabilities in all cohorts. Setting a fixed sensitivity of 95%, a total of 32.2%, 17.6%, and 26.3% of unnecessary biopsies could be avoided with less than 5% of csPCa missed in the validation cohort, Changhai and Zhongda prospective cohorts, respectively. CONCLUSIONS: The PCAIDS was an effective tool to inform decision-making regarding the need for prostate biopsy and prebiopsy examinations such as mpMRI. Further prospective and international studies are warranted to validate the findings of this study. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2100048428. Registered on 06 July 2021.

Structural Change and Radical Generation of a Cadmium-Based Metal-Organic Framework Induced by an Electric Field.

Herein we report the structural change and radical generation of a cadmium-based metal-organic framework (Cd-MOF) induced by external electric fields. Under a weaker single electric field, different coordination modes of Cd-L lead to 3D → 2D structural change. Under stronger superposed electric fields, Cd-MOF was excited to produce a stable free radical. This study will provide a new avenue for the controlled assembly of MOFs.

Urine-derived exosomal PSMA is a promising diagnostic biomarker for the detection of prostate cancer on initial biopsy

Purpose It is well-established that the lack of accurate diagnostic modalities for prostate cancer (PCa) leads to overdiagnosis and overtreatments. Accordingly, this study aimed to assess the value of urine-derived exosomal prostate-specific membrane antigen (PSMA) as a biomarker for the diagnosis of PCa and clinically significant prostate cancer (csPCa). Methods A total of 284 urine samples were collected from patients after the digital rectal examination (DRE). Urinary exosomes were extracted using commercial kits, and urine-derived exosomal PSMA was determined via enzyme-linked immunosorbent assay (ELISA). Evaluation of diagnostic accuracy of PSMA was performed via receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and waterfall plots. Results We found that urine-derived exosomal PSMA was significantly higher in PCa and csPCa than in benign prostatic hyperplasia (BPH) and BPH + non-aggressive prostate cancer (naPCa) groups (P < 0.001). Furthermore, the urine-derived exosome PSMA yielded area under the ROC curve (AUC) values of 0.876 and 0.826 for detecting PCa and csPCa, respectively, suggesting better performance than traditional clinical biomarkers. Besides, when the cutoff value used corresponded to a sensitivity of 95%, urine-derived exosomal PSMA could avoid unnecessary biopsies in 41.2% of cases and missed only 0.7% of csPCa cases. Conclusions Urine-derived exosomal PSMA exhibits a good diagnostic yield for detecting PCa and csPCa. Findings of the present study provide the foothold for future studies on cancer management and research in this patient population (AU)

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration.

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database. Cox regression analysis and least absolute shrinkage and selection operator analysis were applied to assess prognostic genes and build a risk score model. The estimate and cibersort algorithms were used to explore the immune microenvironment, immune infiltration, and checkpoints in BLCA patients. Furthermore, we used the Human Protein Atlas database and our single-cell RNA-seq datasets of BLCA patients to verify the expression of 13 EMRGs at the protein and single-cell levels. We constructed a risk score model; the area under the curve of the model at 5 years was 0.792. The risk score was significantly correlated with the immune markers M0 macrophages, M2 macrophages, CD8 T cells, follicular helper T cells, regulatory T cells, and dendritic activating cells. Furthermore, eight immune checkpoint genes were significantly upregulated in the high-risk group. The risk score model can accurately predict the prognosis of BLCA patients and has clinical application value. In addition, according to the differences in immune infiltration and checkpoints, BLCA patients with the most significant benefit can be selected for immune checkpoint inhibitor therapy.

Procoxacin bidirectionally inhibits osteoblastic and osteoclastic activity in bone and suppresses bone metastasis of prostate cancer.

BACKGROUND: Bone is the most common site of metastasis of prostate cancer (PCa). PCa invasion leads to a disruption of osteogenic-osteolytic balance and causes abnormal bone formation. The interaction between PCa and bone stromal cells, especially osteoblasts (OB), is considered essential for the disease progression. However, drugs that effectively block the cancer-bone interaction and regulate the osteogenic-osteolytic balance remain undiscovered. METHODS: A reporter gene system was constructed to screen compounds that could inhibit PCa-induced OB activation from 631 compounds. Then, the pharmacological effects of a candidate drug, Procoxacin (Pro), on OBs, osteoclasts (OCs) and cancer-bone interaction were studied in cellular models. Intratibial inoculation, micro-CT and histological analysis were used to explore the effect of Pro on osteogenic and osteolytic metastatic lesions. Bioinformatic analysis and experiments including qPCR, western blotting and ELISA assay were used to identify the effector molecules of Pro in the cancer-bone microenvironment. Virtual screening, molecular docking, surface plasmon resonance assay and RNA knockdown were utilized to identify the drug target of Pro. Experiments including co-IP, western blotting and immunofluorescence were performed to reveal the role of Pro binding to its target. Intracardiac inoculation metastasis model and survival analysis were used to investigate the therapeutic effect of Pro on metastatic cancer. RESULTS: Luciferase reporter gene consisted of Runx2 binding sequence, OSE2, and Alp promotor could sensitively reflect the intensity of PCa-OB interaction. Pro best matched the screening criteria among 631 compounds in drug screening. Further study demonstrated that Pro effectively inhibited the PCa-induced osteoblastic changes without killing OBs or PCa cells and directly killed OCs or suppressed osteoclastic functions at very low concentrations. Mechanism study revealed that Pro broke the feedback loop of TGF-ß/C-Raf/MAPK pathway by sandwiching into 14-3-3ζ/C-Raf complex and prevented its disassociation. Pro treatment alleviated both osteogenic and osteolytic lesions in PCa-involved bones and reduced the number of metastases of PCa in vivo. CONCLUSIONS: In summary, our study provides a drug screening strategy based on the cancer-host microenvironment and demonstrates that Pro effectively inhibits both osteoblastic and osteoclastic lesions in PCa-involved bones, which makes it a promising therapeutic agent for PCa bone metastasis.

Urine-derived exosomal PSMA is a promising diagnostic biomarker for the detection of prostate cancer on initial biopsy.

PURPOSE: It is well-established that the lack of accurate diagnostic modalities for prostate cancer (PCa) leads to overdiagnosis and overtreatments. Accordingly, this study aimed to assess the value of urine-derived exosomal prostate-specific membrane antigen (PSMA) as a biomarker for the diagnosis of PCa and clinically significant prostate cancer (csPCa). METHODS: A total of 284 urine samples were collected from patients after the digital rectal examination (DRE). Urinary exosomes were extracted using commercial kits, and urine-derived exosomal PSMA was determined via enzyme-linked immunosorbent assay (ELISA). Evaluation of diagnostic accuracy of PSMA was performed via receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and waterfall plots. RESULTS: We found that urine-derived exosomal PSMA was significantly higher in PCa and csPCa than in benign prostatic hyperplasia (BPH) and BPH + non-aggressive prostate cancer (naPCa) groups (P < 0.001). Furthermore, the urine-derived exosome PSMA yielded area under the ROC curve (AUC) values of 0.876 and 0.826 for detecting PCa and csPCa, respectively, suggesting better performance than traditional clinical biomarkers. Besides, when the cutoff value used corresponded to a sensitivity of 95%, urine-derived exosomal PSMA could avoid unnecessary biopsies in 41.2% of cases and missed only 0.7% of csPCa cases. CONCLUSIONS: Urine-derived exosomal PSMA exhibits a good diagnostic yield for detecting PCa and csPCa. Findings of the present study provide the foothold for future studies on cancer management and research in this patient population.

Urinary exosomal prostate-specific antigen is a noninvasive biomarker to detect prostate cancer: Not only old wine in new bottles.

The study aimed at evaluating the performance of urinary exosomal prostate-specific antigen (UE-PSA) to predict the results of initial prostate biopsies and discriminate clinically significant prostate cancer (Gleason score ≥ 7, csPCa) from nonsignificant PCa (Gleason score < 7, nsPCa) plus benign patients. Two hundred seventy-two consecutive participants were admitted who underwent a prostate biopsy. The UE-PSA expression was detected by enzyme-linked immunosorbent assay (ELISA). The predictive power and clinical value of UE-PSA was assessed by receiver operating characteristic (ROC), decision curve analysis (DCA) and waterfall plots. UE-PSA was upregulated in PCa compared to benign patients (P < .001) and csPCa compared to nsPCa plus benign patients (P < .001). UE-PSA achieved an AUC of 0.953 (0.905-0.989) in distinguishing PCa from benign patients and an AUC of 0.879 (0.808-0.941) in predicting csPCa from nsPCa plus benign patients. These results were validated in an additional multicenter cohort. In addition, DCA showed that UE-PSA achieved the highest net benefit at almost any threshold probability compared to tPSA and %fPSA. As the waterfall plot showed, the UE-PSA assay could avoid 57.6% (155 cases) and 34.6% (93 cases) unnecessary biopsies while only missing 2.6% (7 cases) and 1.5% (4 cases) of the cases of csPCa at the cutoff value of 90% and 95% sensitivity, respectively. We validated that UE-PSA presented great diagnostic power and clinical utility to diagnose PCa and csPCa. UE-PSA could be a promising noninvasive biomarker to improve PCa detection.

Long noncoding RNA as a potential diagnostic tool for prostate cancer: a systematic review and meta-analysis.

PURPOSE: To identify consistently expressed lncRNAs and suitable lncRNAs with high sensitivity and specificity from multiple independent studies as potential biomarkers for PCa diagnostics. METHODS: We searched multiple electronic databases including PubMed, Web of Science, EMBASE, Cochrane Library, CNKI, CQVIP, Wanfang, and CBMdisc for studies published up to July 2022. The quality of the included studies was assessed by two independent reviewers based on the QUADAS-2 tool using Review Manager 5.3. A vote-counting method was used based on the ranking of potential molecular biomarkers. The top-ranked lncRNAs were further assessed for diagnostic value using Meta-disc version 1.4 software. RESULTS: Among the 26 included studies, 2 circulating lncRNAs (PCA3 and MALAT-1) were reported 3 or more times in PCa patients versus non-PCa patients. In further analysis, the areas under the curve of the summary receiver operating characteristic curves for PCA3 and MALAT-1 distinguishing PCa patients were 0.775 and 0.771, respectively. CONCLUSIONS: Based on the current evidence, PCA3 and MALAT-1 are reliable lncRNAs for the diagnosis of PCa.

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis.

Objective: The aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy. Methods: A total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P < 0.05) were included in five machine learning algorithm models. The performance of the models was evaluated by receiver operating characteristic curves. Decision curve analysis (DCA) was performed to estimate the clinical utility of the models. Ten-fold cross-validation was applied in the training process. Results: Prostate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined. Conclusion: The RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

[Fe2S2-Agx]-Hydrogenase Active-Site-Containing Coordination Polymers and Their Photocatalytic H2 Evolution Reaction Properties.

Three [Fe2S2-Agx]-hydrogenase active-site-containing coordination polymers (CPs), {[Fe2S2-Ag1](4-cpmt)2(CO)6(ClO4-)}n (1), {[Fe2S2-Ag2](4-cpmt)2(CO)6(OTf-)2(benzene)}n (2), and {[Fe2S2-Ag2](3-cpmt)2(CO)6(ClO4-)2}n (3), were obtained by a direct synthesis method from ligands [FeFe](4-cpmt)2(CO)6 [L1; 4-cpmt = (4-cyanophenyl)methanethiolate] and [FeFe](3-cpmt)2(CO)6 [L2; 3-cpmt = (3-cyanophenyl)methanethiolate] with silver salts. 1-3 represent the first examples of [FeFe]-hydrogenase-based CPs. It was worth noting that the Ag-S bonding between the Ag centers and S atoms of a [Fe2S2] cluster produced a novel [Fe2S2-Agx] (x = 1 or 2) catalytic site in all three polymers. The results of photochemical H2 generation experiments indicated that 2 and 3 containing [Fe2S2-Ag2] active sites showed obviously improved catalytic performances compared with ligands L1 and L2 and [Fe2S2-Ag1]-containing 1. This work provides a pioneering strategy for the direct synthesis of [Fe2S2]-based CPs or metal-organic frameworks.

Circulating exosomal mRNA signatures for the early diagnosis of clear cell renal cell carcinoma.

BACKGROUND: There are no proven tumor biomarkers for the early diagnosis of clear cell renal cell carcinoma (ccRCC) thus far. This study aimed to identify novel biomarkers of ccRCC based on exosomal mRNA (emRNA) profiling and develop emRNA-based signatures for the early detection of ccRCC. METHODS: Four hundred eighty-eight participants, including 226 localized ccRCCs, 73 patients with benign renal masses, and 189 healthy controls, were recruited. Circulating emRNA sequencing was performed in 12 ccRCCs and 22 healthy controls in the discovery phase. The candidate emRNAs were evaluated with 108 ccRCCs and 70 healthy controls in the test and training phases. The emRNA-based signatures were developed by logistic regression analysis and validated with additional cohorts of 106 ccRCCs, 97 healthy controls, and 73 benign individuals. RESULTS: Five emRNAs, CUL9, KMT2D, PBRM1, PREX2, and SETD2, were identified as novel potential biomarkers of ccRCC. We further developed an early diagnostic signature that comprised KMT2D and PREX2 and a differential diagnostic signature that comprised CUL9, KMT2D, and PREX2 for RCC detection. The early diagnostic signature displayed high accuracy in distinguishing ccRCCs from healthy controls, with areas under the receiver operating characteristic curve (AUCs) of 0.836 and 0.830 in the training and validation cohorts, respectively. The differential diagnostic signature also showed great performance in distinguishing ccRCCs from benign renal masses (AUC = 0.816), including solid masses (AUC = 0.810) and cystic masses (AUC = 0.832). CONCLUSIONS: We established and validated novel emRNA-based signatures for the early detection of ccRCC and differential diagnosis of uncertain renal masses. These signatures could be promising and noninvasive biomarkers for ccRCC detection and thus improve the prognosis of ccRCC patients.

Glycolytic potential enhanced by blockade of pyruvate influx into mitochondria sensitizes prostate cancer to detection and radiotherapy.

OBJECTIVE: This study aimed to evaluate the effects of mitochondrial pyruvate carrier (MPC) blockade on the sensitivity of detection and radiotherapy of prostate cancer (PCa). METHODS: We investigated glycolysis reprogramming and MPC changes in patients with PCa by using metabolic profiling, RNA-Seq, and tissue microarrays. Transient blockade of pyruvate influx into mitochondria was observed in cellular studies to detect its different effects on prostate carcinoma cells and benign prostate cells. Xenograft mouse models were injected with an MPC inhibitor to evaluate the sensitivity of 18F-fluorodeoxyglucose positron emission tomography with computed tomography and radiotherapy of PCa. Furthermore, the molecular mechanism of this different effect of transient blockage towards benign prostate cells and prostate cancer cells was studied in vitro. RESULTS: MPC was elevated in PCa tissue compared with benign prostate tissue, but decreased during cancer progression. The transient blockade increased PCa cell proliferation while decreasing benign prostate cell proliferation, thus increasing the sensitivity of PCa cells to 18F-PET/CT (SUVavg, P = 0.016; SUVmax, P = 0.03) and radiotherapy (P < 0.01). This differential effect of MPC on PCa and benign prostate cells was dependent on regulation by a VDAC1-MPC-mitochondrial homeostasis-glycolysis pathway. CONCLUSIONS: Blockade of pyruvate influx into mitochondria increased glycolysis levels in PCa but not in non-carcinoma prostate tissue. This transient blockage sensitized PCa to both detection and radiotherapy, thus indicating that glycolytic potential is a novel mechanism underlying PCa progression. The change in the mitochondrial pyruvate influx caused by transient MPC blockade provides a critical target for PCa diagnosis and treatment.

Comparing pretreatment strategies to increase the yield and purity of human urinary extracellular vesicles.

Extracellular vesicles (EVs) are membranous vesicles released by various cells, and are involved in intercellular communication and disease progression. EVs that are isolated from urine are good indicators of urinary system diseases and help certain urological studies. During isolation of urine EVs, Dithiothreitol (DTT) is widely used to reduce the contamination of the major contaminant Tamm-Horsefall protein (THP),which is the most abundant protein in the human urine and the most difficult contaminant to remove in the isolation of urine EVs. Unfortunately, DTT can interfere with subsequent analysis due to its strong reducing ability and cannot completely remove THP. To optimize the urine EV isolation strategy, we compared two pretreatment protocols: incubating urine with NaCl and DTT before centrifugation. After a series of analyses by nanoparticle tracking analysis (NTA), western blotting (WB), and transmission electron microscopy (TEM), we found that NaCl removed more THP than DTT in a low-speed centrifugation step and that the residual EVs also had lower THP contamination post NaCl treatment. Remarkably, the yield of EVs obtained via the salting-out method was significantly higher than those obtained by the other methods (P = 0.001). Our study is the first to demonstrate that the salting-out method is better than the traditional DTT method in terms of efficiency in removing THP and EV yields.

Urine Exosomal AMACR Is a Novel Biomarker for Prostate Cancer Detection at Initial Biopsy.

Objectives: The aim of this study is to identify and validate urine exosomal AMACR (UE-A) as a novel biomarker to improve the detection of prostate cancer (PCa) and clinically significant PCa (Gleason score ≥ 7) at initial prostate biopsy. Methods: A total of 289 first-catch urine samples after the digital rectal exam (DRE) were collected from patients who underwent prostatic biopsy, and 17 patients were excluded due to incomplete clinical information. Urine exosomes were purified, and urinary exosomal AMACR (UE-A) was measured by enzyme-linked immunosorbent assay (ELISA). The diagnostic performance of UE-A was evaluated by receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and waterfall plots. Results: The expression of AMACR in PCa and csPCa was significantly higher than that in BPH and non-aggressive (p < 0.001). The UE-A presented good performance in distinguishing PCa from BPH or BPH plus non-significant PCa (nsPCa) from csPCa with an area under the ROC curve (AUC) of 0.832 and 0.78, respectively. The performance of UE-A was further validated in a multi-center cohort of patients with an AUC of 0.800 for detecting PCa and 0.749 for detecting csPCa. The clinical utility assessed by DCA showed that the benefit of patients using UE-A was superior to PSA, f/t PSA, and PSAD in both the training cohort and the validation cohort in terms of all threshold probabilities. Setting 95% sensitivity as the cutoff value, UE-A could avoid 27.57% of unnecessary biopsies, with only 4 (1.47%) csPCa patients missed. Conclusions: We demonstrated the great performance of UE-A for the early diagnosis of PCa and csPCa. UE-A could be a novel non-invasive diagnostic biomarker to improve the detection of PCa and csPCa.

Effect of APOBEC3A functional polymorphism on renal cell carcinoma is influenced by tumor necrosis factor-α and transcriptional repressor ETS1.

Human apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) 3 cytidine deaminases are the prominent drivers of somatic mutations in cancers. However, the effect of APOBEC3s functional polymorphisms on the development of renal cell carcinoma (RCC) remains unknown. Five genetic polymorphisms affecting the expression of APOBEC3A (A3A), APOBEC3B, and APOBEC4 and uracil DNA glycosylase (UNG) were genotyped in 728 RCC patients and 1500 healthy controls. The effects of tumor necrosis factor-α (TNFα) and interleukin-6 on the activity of the A3A promoter with rs12157810-A or -C in four RCC cell lines (786-O, A498, Caki2, ACHN) and two colorectal cancer cell lines (HCT116, SW620) were evaluated using dual-luciferase assays. Transcriptional repressors to the A3A promoter were identified by chromatin immunoprecipitation-quantitative PCR. The proapoptotic effect of A3A on RCC cells was evaluated using cytometry. The prognostic values of A3A and ETS1 were evaluated by the Cox regression analysis. The expressions of A3A and ETS1 were evaluated in clear cell RCC (ccRCC) specimens with different polymorphic genotypes using quantitative RT-PCR and immunohistochemistry. Of those functional polymorphisms, CC genotype at rs12157810 in the A3A promoter was significantly associated with a decreased risk of ccRCC, compared to the AA genotype (odds ratio adjusted for age and gender, 0.41, 95% confidence interval [CI], 0.28-0.57). Other polymorphic genotypes were not associated with the risk of RCC. The activity of the A3A promoter with rs12157810-C was significantly higher than that with rs12157810-A in the four RCC cell lines and two colorectal cancer cell lines. The activity of the A3A promoter with rs12157810-C was greatly up-regulated by TNFα and predominantly inhibited by a transcriptional repressor ETS1. The binding of ETS1 to the A3A promoter with rs12157810-C was looser than that with rs12157810-A. Ectopic expression of A3A significantly promoted apoptosis in ccRCC cells, rather than in colorectal cancer cells. Higher ETS1 expression predicted a favorable prognosis in ccRCC, with a hazard ratio of 0.58 (95% CI, 0.43-0.78). Rs121567810-C up-regulates the A3A promoter activity, possibly due to higher response to TNFα and looser transcriptional repression by ETS1. Up-regulation of A3A increases apoptosis, thus decreasing ccRCC risk in those carrying rs121567810-C.