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Background Cellular senescence is considered to be an important correlate of tumorigenesis and progression, but the correlation between cellular senescence and immune infiltration of tumors remains unclear in glioma. The prognostic value of cellular senescence in gliomas with immune infiltration needs further investigation. Methods We obtained data from TCGA for GBM patients and LGG patients, followed by screening these genes by LASSO-COX based on genes associated with cellular senescence obtained from CellAge thereby obtaining survival-related signature genes, followed by KM analysis, ROC analysis, PCA analysis and immunostaining profiles to verify the risk score as a prognostic indicator of independence and plotting bar lines, and exploring the biological pathways associated with the high-risk group by GSEA analysis. The signature was also validated by combining the genetic information obtained from the China Glioma Genome Atlas (CGGA) database for GBM patients and LGG patients. Results We constructed a prognostic signature for five cellular senescence-related genes. They were CENPA, IGFBP-5, TNFSF13, PATZ1 & CDK6. The independence of the risk score as a prognostic indicator was validated by KM analysis, ROC analysis, PCA analysis, and immunohistochemical results. The prognosis of glioma patients was established from a plotted nomogram. We then found that the high-risk group was significantly enriched for pathways in the cell cycle, nuclear division regulation, CD40 signalling pathway and p53 signalling pathway by GSEA analysis. ssGSEA results indicated that the high-risk group was associated with tumor-infiltrating immune cells, including MDSCs, macrophages and Tregs. Conclusions We analyzed the clinical significance of different risk groups on glioma prognosis and the role in the immune landscape by constructing an independent prognostic signature based on cellular senescence correlation, which may help to develop personalized immunotherapy strategies for oncologists.

Objective: The aim of this study is to investigate the associations between phthalate exposure and UI in a nationally representative sample of US adults.Methods: Cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) database was used for analysis. In total, 2,818 participants with measurements for phthalate metabolites and complete UI questionnaire data were enrolled in our study. Further, seven phthalate metabolites were measured, which were obtained from urine samples and creatinine-standardized in the subsequent analyses. After divided these phthalate metabolites into three groups, multivariable regression models were performed to evaluate the association between phthalate metabolites and UI rates. Moreover, interaction analyses and subgroup analyses stratified by gender were performed.Results: In these seven phthalate metabolites, high level of mono-carboxynonyl phthalate (MCNP), mono-carboxyoctyl phthalate (MCOP), mono-isobutyl phthalate (MiBP), mono-n-butyl phthalate (MBP) and mono-3-carboxypropyl phthalate (MCPP) showed increased risk of UI [odds ratio (OR) = 1.52, 1.42, 1.43, 1.50, 1.51, respectively, all p value < 0.05]. Trend test showed that incidence of UI increased significantly with concentration. A higher incidence of UI among participants was observed in the maximal tertile of phthalate when comparing with the lowest tertile. Subgroup analysis found that different phthalates have varying influence for different types of UI. Moreover, the analyses stratified for sex indicated that the high concentrations of MCNP and median concentrations of MCCP were associated with increase of the odds of UI in women and in men, respectively.Conclusion: Overall, the exposure to phthalates was positively associated with UI among US adults. Notably, different phthalates have varying influence for different types of UI, and male and female exposure to phthalate could result in the different prevalence of UI.

Background: We aimed to develop a detailed individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, Gleason Score biopsy. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray’s proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and GS biopsy were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812-0.844), and the C-index was 0.838 (%95CI, 0.813-0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve postoperative management of patients

Background: Immune and stromal cells in the tumor microenvironment have exhibit critical relevance with tumorigenesis and progression. Therefore, our analysis was conducted to explore the potential prognostic factors and the therapeutic targets of bladder cancer (BC) that affiliate to immune and stromal infiltration signature.Methods: Immune and stromal score were calculated for BC patients from TCGA database using ESTIMATE algorithm to predict the level of infiltration. Kaplan-Meier curves were utilized to assess the correlation of immune/stromal infiltration with survival outcomes. Differential expression genes (DEGs) were identified. Enrichment analyses, Kaplan-Meier curves, protein-protein interaction (PPI) network construction were performed to describe and screen the core module genes, whose prognostic value was further validated by an independent GEO dataset. Transcriptional factors (TFs) and ncRNA. correlated with the core module were identified using RAID 2.0 and TRRUST 2.0 database, and drugs potentially regulative for BC were accordingly screened using DrugBank.Results: 393 and 93 BC patients were enrolled from TCGA and GEO respectively. Higher stromal infiltration indicated worse overall survival (P = 0.015), and higher immune infiltration indicated an improvement on overall survival (P = 0.042). 562 DEGs were identified and 123 of them associated with survival outcomes. PPI has identified the core module genes, in which EFNB2 was the only core prognostic gene that was validated by both TCGA (P = 0.042) and GEO dataset (P = 0.036). Four TFs and Five ncRNAs (e.g. HIF1A) were associated with the regulation of the core module genes, and six drugs were screened as potential candidates to regulate BC.Conclusion: Higher immune infiltration in bladder tumor was correlated with improved survival and higher stromal infiltration corelated with worse survival. Furthermore, a higher expression of EFNB2 was tied with poorer prognosis of BC, which was validated by two independent datasets.

Background: Few studies on predicting survival in patients with histology of mucinous adenocarcinoma of the prostate have been done.Objective: Nomogram is a mathematical model in which various important factors are combined to predict a specific end point. The purpose of this study was to evaluate the prognostic value of clinical factors in patients with mucinous adenocarcinoma and to construct nomogram to predict the survival rate. Design,Setting,and Participants: A nomogram was designed to predict the survival rate of 356 patients with invasive ductal carcinoma selected from the surveillance, epidemiology and final results (SEER) database. Univariate and multivariate models of the cohort were done firstly.And then a model of nomogram that included age, race, grade, stage M and chemotherapy were constructed to predict the 3-year and 5-year survival of prostate cancer patients with histology of mucinous adenocarcinoma. Based on different time, we designed two nomograms.Results and limitations: After the discrimination and calibration, C-index was 0.8138. The specificity was 86.12%, sensitivity was 55.89% for 3-year nomogram . Its survival was 91.49% and AUC was 0.7467. The specificity was 82.42%, sensitivity was 54.83% for 5-year nomogram. Its Survival was 86.73% and AUC was 0.7555. Respectively, which indicated relative good discrimination of the nomogram. Conclusions: This clinical model shows a high ability to predict the survival rate of mucinous adenocarcinoma patients with histologically prostate cancer, making this model a novel and attractive model for predicting the survival rate of patients.

Background: Papillary renal carcinoma (PRCC) is one of the important subtypes of kidney cancer, with a high degree of heterogeneity. At present, there is still a lack of robust and accurate biomarkers for the diagnosis, prognosis and treatment selection of PRCC. Considering the important role of tumor immunity in PRCC, we aim to construct a signature based on immune-related gene pairs (IRGPs) to estimate the prognostic of patients with PRCC.Methods: We obtained gene expression profiling and clinical information of patients with PRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which were divided into discovery and validation cohorts, respectively. The immune-related genes in the samples were used to construct gene pairs, and the immune-related genes pairs (IRGPs) with robust impact for overall survival (OS) were screened out to construct the signature by univariate analysis, multivariate Cox analysis, and least absolute shrinkage and selection operator (Lasso) analysis. Then we verified the prognostic role of the signature, and assessed the relationship between this signature with tumor immune infiltration and functional pathways.Results: A total of 315 patients were included in our study, and divided to discovery (n=287) and validation (n=28) cohorts. Finally, we selected 14 IRGPs with a panel of 22 unique genes to construct the prognostic signature. According to the signature, we stratified patients into high-risk group and low-risk group. In both discovery and validation cohorts, the results of Kaplan-Meier analysis showed that there were significant differences in OS between the two groups (p+ T cells, Tregs, and T follicular cell helper were significantly higher in the high-risk group. Functional analysis showed that multiple immune-related signaling pathways were enriched in the high-risk group.Conclusions: We successfully established an individualized prognostic immune-related gene pairs signature, which can accurately and independently predict the OS of patients with PRCC.

Background: Previous evidence directly evaluating the efficacy and safety of the treatment for castration-resistant prostate cancer (CRPC) by androgen receptor (AR) signaling-axis inhibitors was limited by the quantity of randomized controlled trials (RCT) and biased by non-RCTs. We aim to comprehensively assess the efficacy and safety of abiraterone and enzalutamide using only RCTs. Patients and methods: We systematically searched Pubmed, Embase, MEDLINE and ClinicalTrial.gov for RCTs providing data of treatment outcomes by AR inhibitors (abiraterone and enzalutamide). Pooled hazard ratios (HR) with 95%CI for survival benefits were calculated using STATA 12.0. Comparison of prostate-specific antigen (PSA) response rate, aggregated adverse event (AE) statistics, any grade AE, high-grade AE (grade ≥3), AE of special interest between the treatment and control groups were performed by RevMan 5.3 and STATA 12.0. Results: Eight eligible RCTs with 6296 patients were selected. Pooled HR were 0.72 for overall survival, 0.45 for radiographic progression-free survival and 0.36 for PSA PFS. AR inhibitors could significantly increase the rate of PSA response (OR=8.67, 95%CI 4.42-17.04) and AE occurrence (OR=1.98, 95%CI 1.46-2.68). The treatment group had a higher occurrence of any-grade fatigue (OR=1.34, 95%CI 1.20-1.49), back pain (OR=1.15, 95%CI 1.01-1.15), hot flush (OR=1.76, 95%CI 1.50-2.06), diarrhea (OR=1.22, 95%CI 1.07-2.40) and arthralgia (OR=1.34, 95%CI 1.16-1.54). Particularly, the outcomes for AEs of special interest including any grade hypertension (OR=2.06, 95%CI 1.71-2.47), hypokalemia (OR=1.80, 95%CI 1.42-2.30) and fluid retention or edema (OR=1.38, 95%CI 1.17-1.63) also favored the control group. In terms of high-grade AEs, a higher occurrence of hypertension (OR=2.60, 95%CI 1.79-3.79) and pain in extremity (OR=4.46, 95%CI 2.81-7.07) was observed in the treatment group. There was no significant difference regarding other AEs analyzed in this study. Conclusion: The survival benefits by AR inhibitors for CRPC were evident and promising at the cost of acceptably higher risk of AEs occurrence.