Background Prostate cancer (PCa) remains the second leading cause of deaths due to cancer in the United States in men. The aim of this study was to perform an integrative epigenetic analysis of prostate adenocarcinoma to explore the epigenetic abnormalities involved in the development and progression of prostate adenocarcinoma. The key DNA methylation-driven genes were also identified. Methods Methylation and RNA-seq data were downloaded for The Cancer Genome Atlas (TCGA). Methylation and gene expression data from TCGA were incorporated and analyzed using MethylMix package. Methylation data from the Gene Expression Omnibus (GEO) were assessed by R package limma to obtain differentially methylated genes. Pathway analysis was performed on genes identified by MethylMix criteria using ConsensusPathDB. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also applied for the identification of pathways in which DNA methylation-driven genes significantly enriched. The protein–protein interaction (PPI) network and module analysis in Cytoscape software were used to find the hub genes. Two methylation profile (GSE112047 and GSE76938) datasets were utilized to validate screened hub genes. Immunohistochemistry of these hub genes were evaluated by the Human Protein Atlas. Results A total of 553 samples in TCGA database, 32 samples in GSE112047 and 136 samples in GSE76938 were included in this study. There were a total of 266 differentially methylated genes were identified by MethylMix. Plus, a total of 369 differentially methylated genes and 594 differentially methylated genes were identified by the R package limma in GSE112047 and GSE76938, respectively. GO term enrichment analysis suggested that DNA methylation-driven genes significantly enriched in oxidation–reduction process, extracellular exosome, electron carrier activity, response to reactive oxygen species, and aldehyde dehydrogenase [NAD(P)+] activity. KEGG pathway analysis found DNA methylation-driven genes significantly enriched in five pathways including drug metabolism—cytochrome P450, phenylalanine metabolism, histidine metabolism, glutathione metabolism, and tyrosine metabolism. The validated hub genes were MAOB and RTP4. Conclusions Methylated hub genes, including MAOB and RTP4, can be regarded as novel biomarkers for accurate PCa diagnosis and treatment. Further studies are needed to draw more attention to the roles of these hub genes in the occurrence and development of PCa.

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Testicular cancer is the most common solid malignancy among young men. We downloaded data of testicular cancer patients from The Cancer Genome Atlas database to find novel genes in the testicular cancer microenviroment based on ESTIMATE algorithm-derived immune scores. A total of 156 cases of testicular cancer were included in this study and 165 cases of normal testicular tissues were used. We divided the testicular cancer patients into high-and low-score groups based on their immune scores. We identified 1,226 differentially expressed genes (fold change > 2, false discovery rate < 0.05), including 688 downregulated genes and 538 upregulated genes, between these two groups. The top Gene Ontology terms were involved in the immune response-regulating cell surface receptor signaling pathway, immune response-activating cell surface receptor signaling pathway, external side of the plasma membrane, and receptor ligand activity. By performing the Kyoto Encyclopedia of Genes and Genomes analysis, we demonstrated that cAMP signaling pathway was highly enriched among these differentially expressed genes. High expression of LINC01564, LINC02208, ODAM, RNA5SP111, and RNU6-196P were found to be associated with poor overall survival. The expression of genes was further validated by the Human Protein Atlas and only ALB and IFNG were demonstrated to be differentially expressed between testis tissue and testicular cancer tissue.

PI-RADS v2 could be used to reduce unnecessary prostate biopsies in patients with PSA levels of 4-10 ng/ml.

Benign prostatic hyperplasia (BPH) is one of the most common causes of lower urinary tract symptoms (LUTS) in elderly man. However, the underlying molecular mechanisms of BPH have not been completely elucidated. We identified the key genes and pathways by using analysis of Gene Expression Omnibus (GEO) database.Differentially expressed genes (DEGs) were identified using edgeR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the DEGs by Database for Annotation, Visualization and Integrated Discovery (DAVID) database and ConsensusPathDB, respectively. Then, proteinprotein interaction (PPI) networks were established by the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Finally, we identified 660 DEGs ultimately including 268 upregulated genes and 392 downregulated genes. GO analysis revealed that DEGs were mainly enriched in extracellular exosome, identical protein binding, mitochondrial adenosine triphosphate (ATP) synthesis coupled proton transport, extracelluar matrix, focal adhesion, cytosol, Golgi apparatus, cytoplasm, protein binding, and Golgi membrane. Focal adhesion pathway, FoxO signaling pathway, and autophagy pathway were selected.

ObjectiveTo explore risk factors of infectious complications following transrectal ultrasound-guided prostate biopsy (TRUSPB).MethodsWe retrospectively analyzed 1,203 patients with suspected prostate cancer who underwent TRUSPB at our center between December 2012 and December 2016. Demographics, clinical characteristics, and data regarding complications were collected, and then univariate and multivariate logistic regression analyses were used to identify independent risk factors for infectious complications after prostate biopsy.ResultsMultivariate logistic analysis demonstrated that body mass index (BMI) (OR=2.339, 95% CI 2.029–2.697, P<0.001), history of diabetes (OR=2.203, 95% CI 1.090–4.455, P=0.028), and preoperative catheterization (OR=2.303, 95% CI 1.119–4.737, P=0.023) were risk factors for infection after prostate biopsy. The area under the receiver operating characteristics curve for infectious complications was 0.930 (95% CI 0.907–0.953, P<0.001). BMI=28.196 kg/m2 was the best cut-off threshold for predicting infection after TRUSPB.ConclusionBMI >28.196 kg/m2, history of diabetes, and preoperative catheterization are independent risk factors for infection after prostate biopsy.

Background: Bladder cancer (BCa) is one of the important tumors that have been proven to be treatable with immunotherapy. This study aims to identify and validate a molecular prognostic index of BCa based on immunogenomic landscape analysis. Methods: The cancer genome atlas (TCGA) database and immunology database and analysis portal (ImmPort) database were used to identified differentially expressed immune-related genes (IRGs). Prognostic IRGs were screened and protein-protein interaction (PPI) network was constructed. Multivariate Cox analysis was performed to develop a molecular prognostic index of BCa. Internal and external validation were then performed in TCGA cohort and GEO cohort, respectively. Besides, we also explore the relationship between this index and clinical characteristics, immune cell infiltration and tumor microenvironment. Results: A total of 61 prognostic IRGs were identified and a molecular prognostic index was developed. The top four hub genes included MMP9, IGF1, CXCL12 and PGF. The difference in overall survival between high-risk group and low-risk group was statistically significant. The area under curve of the receiver operating characteristic (ROC) curve was 0.757, suggesting the potential for this index. Besides, Internal validation using TCGA cohort and external validation using GEO cohort indicated that this index was of great performance in predicting outcome. T cells CD8, T cells CD4 memory activated, T cells follicular helper, macrophages M0, macrophages M2 and neutrophils were significantly associated with prognosis of BCa patients. Female, high grade, stage III&IV, N1-3 and T3-4 were associated significantly with higher risk score compared with male, low grade, stage I&II, N0 and T1-2, respectively. High risk score had a positive association with higher stromal score and ESTIMATE score while high risk score had a negative association with tumor purity. Conclusions: This study identified several prognostic immune-related genes of clinical value. Besides, we developed and validated a molecular index based on immunogenomic landscape analysis, which performed well in predicting prognosis of BCa. Further researches are needed to verify the effectiveness of this index and these vital genes.

| INTRODUC TI ONProstate cancer (PCa) is the fifth principal cause of death and also the second most frequent cancer in males all over the world. 1 More than 15% of PCa patients harbour lymph node metastasis at radical prostatectomy. 2 Lymph node metastasis is a complicated process in which cancer cells leave the primary tumour site through the lymphatic system and then establish a secondary tumour site in lymph nodes. 3 These individuals have a higher risk of recurrence after primary treatment and usually suffer a poor prognosis. 4 Therefore, predicting the occurrence of lymph node metastasis is of vital clinical significance. At the present day, several nomograms and indexes AbstractLymph node metastasis is one of the most important independent risk factors that can negatively affect the prognosis of prostate cancer (PCa); however, the exact mechanisms have not been well studied. This study aims to better understand the underlying mechanism of lymph node metastasis in PCa by bioinformatics analysis.We analysed a total of 367 PCa cases from the cancer genome atlas database and performed weighted gene co-expression network analysis to explore some modules related to lymph node metastasis. Gene Ontology analysis and pathway enrichment analysis were conducted for functional annotation, and a protein-protein interaction network was built. Samples from the International Cancer Genomics Consortium database were used as a validation set. The turquoise module showed the most relevance with lymph node metastasis. Functional annotation showed that biological processes and pathways were mainly related to activation of the processes of cell cycle and mitosis. Four hub genes were selected: CKAP2L, CDCA8, ERCC6L and ARPC1A. Further validation showed that the four hub genes well-distinguished tumour and normal tissues, and they were good biomarkers for lymph node metastasis of PCa. In conclusion, the identified hub genes facilitate our knowledge of the underlying molecular mechanism for lymph node metastasis of PCa. K E Y W O R D Shub genes, lymph node metastasis, prostate cancer, weighted gene co-expression network analysis