To identify prostate cancer (PCa) patients with a high risk of recurrence is critical before delivering adjuvant treatment. We developed a classifier based on the Enzalutamide treatment resistance-related genes to assist the currently available staging system in predicting the recurrence-free survival (RFS) prognosis of PCa patients. We overlapped the DEGs from two datasets to obtain a more convincing Enzalutamide-resistance-related-gene (ERRG) cluster. The five-ERRG-based classifier obtained good predictive values in both the training and validation cohorts. The classifier precisely predicted RFS of patients in four cohorts, independent of patient age, pathological tumour stage, Gleason score and PSA levels. The classifier and the clinicopathological factors were combined to construct a nomogram, which had an increased predictive accuracy than that of each variable alone. Besides, we also compared the differences between high- and low-risk subgroups and found their differences were enriched in cancer progression-related pathways. The five-ERRG-based classifier is a practical and reliable predictor, which adds value to the existing staging system for predicting the RFS prognosis of PCa after radical prostatectomy, enabling physicians to make more informed treatment decisions concerning adjuvant therapy.

Prostate cancer (PCa) is the most frequent malignancy in male urogenital system around worldwide. We performed molecular subtyping and prognostic assessment based on consensus genes in patients with PCa. Five cohorts containing 1,046 PCa patients with RNA expression profiles and recorded clinical follow-up information were included. Univariate, multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were used to select prognostic genes and establish the signature. Immunohistochemistry staining, cell proliferation, migration and invasion assays were used to assess the biological functions of key genes. Thirty-nine intersecting consensus prognostic genes from five independent cohorts were identified. Subsequently, an eleven-consensus-gene classifier was established. In addition, multivariate Cox regression analyses showed that the classifier served as an independent indicator of recurrence-free survival in three of the five cohorts. Combined receiver operating characteristic (ROC) analysis achieved synthesized effects by combining the classifier with clinicopathological features in four of five cohorts. SRD5A2 inhibits cell proliferation, while ITGA11 promotes cell migration and invasion, possibly through the PI3K/AKT signaling pathway. To conclude, we established and validated an eleven-consensus-gene classifier, which may add prognostic value to the currently available staging system.

BACKGROUND Both meta-analyses and systematic reviews were used to assess the relationship between purinergic receptor P2X ligand-gated ion channel 7 (P2RX7)rs3751143 polymorphism and the risk of cancer. MATERIALS AND METHODS The dataused in this research were collected from Google Scholar, Web of Science, CNKI, and Wan Fang Data databases. The final retrieval ended on 22 February 2019.The strength of correlation was assessed using odds ratios and 95% confidence intervals.Based on the heterogeneity test results,fixed-effect (Mantel-Haenszel) or random-effects(DerSimonian-Laird) models were selected to summarise the collective effects. RESULTS Eightseparate studies containing 1,462 cancer cases and 3,037controls were enrolled. Overall, there was no significant association between P2RX7rs3751143 polymorphism and the risk of cancer in the allelic, homozygous, heterozygous, dominant, or recessive models. CONCLUSIONS Our meta-analysis indicates that there is no significant association between P2RX7 rs3751143 polymorphism and the risk of cancer in the allelic, homozygous, heterozygous, dominant,and recessive models.

Cumulative evidence suggests that abnormal differentiation of T lymphocytes influences the pathogenesis of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). Thus, understanding the immune activation landscape of CP/CPPS would be helpful for improving therapeutic strategies. Here, we utilized BD™ AbSeq to digitally quantify both the protein and mRNA expression levels in single peripheral blood T cells from two CP/CPPS patients and two healthy controls. We utilized an integrated strategy based on canonical correlation analysis of 10 000+ AbSeq profiles and identified fifteen unique T‐cell subpopulations. Notably, we found that the proportion of cluster 0 in the CP/CPPS group (30.35%) was significantly increased compared with the proportion in the healthy control group (9.38%); cluster 0 was defined as effector T cells based on differentially expressed genes/proteins. Flow cytometry assays confirmed that the proportions of effector T‐cell subpopulations, particularly central memory T cells, T helper (Th)1, Th17 and Th22 cells, in the peripheral blood mononuclear cell populations of patients with CP/CPPS were significantly increased compared with those of healthy controls (P < 0.05), further confirming that aberration of effector T cells possibly leads to or intensifies CP/CPPS. Our results provide novel insights into the underlying mechanisms of CP/CPPS, which will be beneficial for its treatment.