Background Tumor angiogenesis, an essential process for cancer proliferation and metastasis, has a critical role in prognostic of kidney renal clear cell carcinoma (KIRC), as well as a target in guiding treatment with antiangiogenic agents. However, tumor angiogenesis subtypes and potential epigenetic regulation mechanisms in KIRC patient remains poorly characterized. System evaluation of angiogenesis subtypes in KIRC patient might help to reveal the mechanisms of KIRC and develop more target treatments for patients. Method Ten independent tumor angiogenesis signatures were obtained from molecular signatures database (MSigDB) and gene set variation analysis was performed to calculate the angiogenesis score in silico using the Cancer Genome Atlas (TCGA) KIRC dataset. Tumor angiogenesis subtypes in 539 TCGA-KIRC patients were identified using consensus clustering analysis. The potential regulation mechanisms was studied using gene mutation, copy number variation, and differential methylation analysis (DMA). The master transcription factors (MTF) that cause the difference in tumor angiogenesis signals were completed by transcription factor enrichment analysis. Results The angiogenesis score of a prognosis related angiogenesis signature including 189 genes was significantly correlated with immune score, stroma score, hypoxia score, and vascular endothelial growth factor (VEGF) signal score in 539 TCGA KIRC patients. MMRN2, CLEC14A, ACVRL1, EFNB2, and TEK in candidate gene set showed highest correlation coefficient with angiogenesis score in TCGA-KIRC patients. In addition, all of them were associated with overall survival in both TCGA-KIRC and E-MTAB-1980 KIRC data. Clustering analysis based on 183 genes in angiogenesis signature identified two prognosis related angiogenesis subtypes in TCGA KIRC patients. Two clusters also showed different angiogenesis score, immune score, stroma score, hypoxia score, VEGF signal score, and microenvironment score. DMA identified 59,654 differential methylation sites between two clusters and part of these sites were correlated with tumor angiogenesis genes including CDH13, COL4A3, and RHOB. In addition, RFX2, SOX13, and THRA were identified as top three MTF in regulating angiogenesis signature in KIRC patients. Conclusion Our study indicate that evaluation the angiogenesis subtypes of KIRC based on angiogenesis signature with 183 genes and potential epigenetic mechanisms may help to develop more target treatments for KIRC patients.

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Objective Immune cells residing in the testicular interstitial space form the immunological microenvironment of the testis. They are assumed to play a role in maintaining testicular homeostasis and immune privilege. However, the immune status and related cell polarization in patients with nonobstructive azoospermia (NOA) remains poorly characterized. System evaluation of the testis immunological microenvironment in NOA patients may help to reveal the mechanisms of idiopathic azoospermia. Study design The gene expression patterns of immune cells in normal human testes were systematically analyzed by single‐cell RNA sequencing (scRNA‐seq) and preliminarily verification by the human protein atlas (HPA) online database. The immune cell infiltration profiles and immune status of patients with NOA was analyzed by single‐sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) based on four independent public microarray datasets (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45885, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45887, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9210, and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE145467), obtained from Gene Expression Omnibus (GEO) online database. The relationship between immune cells and spermatogenesis score was further analyzed by Spearman correlation analysis. Finally, immunohistochemistry (IHC) staining was performed to identify the main immune cell types and their polarization status in patients with NOA. Results Both scRNA‐seq and HPA analysis showed that testicular macrophages represent the largest pool of immune cells in the normal testis, and also exhibit an attenuated inflammatory response by expressing high levels of tolerance proteins (CD163, IL‐10, TGF‐β, and VEGF) and reduced expression of TLR signaling pathway‐related genes. Correlation analysis revealed that the testicular immune score and macrophages including M1 and M2 macrophages were significantly negatively correlated with spermatogenesis score in patients with NOA (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45885 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45887). In addition, the number of M1 and M2 macrophages was significantly higher in patients with NOA (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9210 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE145467) than in normal testis. GSVA analysis indicated that the immunological microenvironment in NOA tissues was manifested by activated immune system and pro‐inflammatory status. IHC staining results showed that the number of M1 and M2 macrophages was significantly higher in NOA tissues than in normal testis and negatively correlated with the Johnson score. Conclusion Testicular macrophage polarization may play a vital role in NOA development and is a promising potential therapeutic target.

ObjectiveTo reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).MethodsMRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).ResultsThe positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).ConclusionsWe found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

Chronic inflammation of the male genital tract is thought to be a primary etiological factor of male infertility. The abundance and activation of macrophages and dendritic cells in patients with chronic inflammation of genital tract were closely associated with oligozoospermia and asthenospermia. Chronic epididymitis appears to be more important than seminal vesiculitis or prostatitis due to the direct interaction between spermatozoa and epididymal inflammatory cells. In this study, we present a case report of a 41-year-old male with oligoasthenospermia and chronic epididymitis. Hematoxylin-eosin staining and immunofluorescence analyses showed that antigen presenting cells including macrophages and dendritic cells were found capturing spermatozoa in the lumen of cauda epididymis. To our knowledge, this is the first case report that directly observed dendritic cells capturing spermatozoa in the lumen of an inflamed epididymis. This finding directly explains chronic epididymitis as the possible cause of oligospermia in patients.

PBRM1 is a tumor suppressor frequently mutated in clear cell renal cell carcinoma. However, no effective targeted therapies exist for ccRCC with PBRM1 loss. To identify novel therapeutic approaches to targeting PBRM1-deficient renal cancers, we employed a synthetic lethality compound screening in isogenic PBRM1+/+ and PBRM1-/- 786-O renal tumor cells and found that a DNMT inhibitor 5-Fluoro-2’-deoxycytidine (Fdcyd) selectively inhibit PBRM1-deficient tumor growth. RCC cells lacking PBRM1 show enhanced DNA damage response, which leads to sensitivity to DNA toxic drugs. Fdcyd treatment not only induces DNA damage, but also re-activated a pro-apoptotic factor XAF1 and further promotes the genotoxic stress-induced PBRM1-deficient cell death. This study shows a novel synthetic lethality interaction between PBRM1 loss and Fdcyd treatment and indicates that DNMT inhibitor represents a novel strategy for treating ccRCC with PBRM1 loss-of-function mutations.

Background: Prostate cancer (PCa) is one of the most common tumors of the urinary system. Cuproptosis is a novel mode of controlled cell death that is related to the development of various tumor types. However, the functions of cuproptosis-related long noncoding RNAs (CRLs) in PCa have not yet been well studied.Methods: In this study, data of PCa patients were obtained from The Cancer Genome Atlas (TCGA) and from the Changhai Hospital. Univariate and multivariate Cox regression analyses and LASSO regression analysis were conducted to screen CRLs linked to the prognosis of PCa patients. A risk score model was constructed on the basis of CRLs to predict prognosis. PCa patients were categorized into high- and low-risk cohorts. The predictive value of the risk score was evaluated by Kaplan–Meier survival analysis, receiver operating characteristic curves, and nomograms. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore possible pathways involving CRLs in PCa. Immune function analysis confirmed the correlation between CRLs and immunity in PCa. Finally, we explored the tumor mutational burden and drug response in the high- and low-risk cohorts.Results: First, we identified seven CRLs (C1orf229, C9orf139, LIPE-AS1, MCPH1-AS1, PRR26, SGMS1-AS1, and SNHG1) that were closely related to prognosis in PCa. The risk score model based on the selected CRLs could accurately predict the prognosis of PCa patients. The high-risk cohort was associated with worse disease-free survival (DFS) time in PCa patients (p < 0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 1 year: 0.703). Nomograms were constructed based on the risk score and clinicopathological features and also exhibited great predictive efficiency for PCa. GO and KEGG analyses showed that the CRLs were mainly enriched in metabolism-related biological pathways in PCa. In addition, immune function analysis showed that patients in the high-risk cohort had higher TMB and were more sensitive to conventional chemotherapy and targeted drugs including doxorubicin, epothilone B, etoposide, and mitomycin C.Conclusion: We constructed a novel CRL-related risk score model to accurately predict the prognosis of PCa patients. Our results indicate that CRLs are potential targets for drug therapy in PCa and provide a possible new direction for personalized treatment of PCa patients.