PURPOSE Prostate cancer (PCa) is one of the most common cancers in elderly men worldwide. Systematic biopsy guided by transrectal ultrasound remains the standard for PCa diagnosis; however, the false negative rate is 10-20%. Multiparametric magnetic resonance imaging (mpMRI) allows PCa visualization with a more precise localization and a higher accuracy and specificity for the detection of PCa. The physician can mentally relocate the most appropriate area detected on the prebiopsy mpMRI, based on its zonal topography and anatomical landmarks, called cognitive fusion. Herein, we concentrated on the accuracy of PCa localization in cognitive fusion compared with MRI-TRUS fusion and explored the applied scope of cognitive fusion. METHODS Thirty-two eligible patients with 36 PCa lesions were recruited for our study. TRUS examinations and MRI-TRUS fusion procedures were performed by experienced operators. The cognitive fusion images were compared using the TRUS image in a MRI-TRUS fusion workstation. RESULTS Using cognitive fusion imaging, 86.1% of the lesions were accurately located by the senior sonographer and 69.4% of the lesions were accurately located by the junior sonographer. The maximum diameter and PI-RADS score of the lesions were important factors that affected the accuracy of cognitive fusion (P  less then  0.05). Furthermore, the lesions with high PI-RADS scores and the lesions with large diameters were more accurately located using cognitive fusion (P  less then  0.05). CONCLUSIONS Cognitive fusion is a reliable technique with dependency on working experience, and its accuracy of locating suspicious lesions is consistent with MRI-TRUS fusion in patients with high PI-RADS score and large lesions.BACKGROUND Deregulated microRNAs (miRNAs) in breast and gynecological cancer might contribute to improve early detection of female malignancies. OBJECTIVE Specification of miRNA types in serum and urine as minimally-invasive biomarkers for breast (BC), endometrial (EC) and ovarian cancer (OC). METHODS In a discovery phase, serum and urine samples from 17 BC, five EC and five OC patients vs. ten healthy controls (CTRL) were analyzed with Agilent human miRNA microarray chip. Selected miRNA types were further investigated by RT-qPCR in serum (31 BC, 13 EC, 15 OC patients, 32 CTRL) and urine (25 BC, 10 EC, 10 OC patients, 30 CTRL) applying two-sample t-tests. RESULTS Several miRNA biomarker candidates exhibited diagnostic features due to distinctive expression levels (serum 26; urine 22). Among these, miR-518b, -4719 and -6757-3p were found specifically deregulated in BC serum.  https://www.selleckchem.com/products/Gefitinib.html  Four, non-entity-specific, novel biomarker candidates with unknown functional roles were identified in urine (miR-3973; -4426; -5089-5p and -6841). RT-qPCR identified miR-484/-23a (all p⩽ 0.001) in serum as potential diagnostic markers for EC and OC while miR-23a may also serve as an endogenous control in BC diagnosis. CONCLUSIONS Promising miRNAs as liquid biopsy-based tools in the detection of BC, EC and OC qualified for external validation in larger cohorts.Cervical cancer (CC) is one kind of female cancer. With the development of bioinformatics, targeted specific biomarkers therapy has become much more valuable. GSE26511 was obtained from gene expression omnibus (GEO). We utilized a package called "WGCNA" to build co-expression network and choose the hub module. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein-protein interaction (PPI) information of those genes in the hub module. A Plug-in called MCODE was utilized to choose hub clusters of PPI network, which was visualized in Cytoscape. Clusterprofiler was used to do functional analysis. Univariate and multivariate cox proportional hazards regression analysis were both conducted to predict the risk score of CC patients. Kaplan-Meier curve analysis was done to show the overall survival. Receiver operating characteristic (ROC) curve analysis was utilized to evaluate the predictive value of the patient outcome. Validation of the hub gene in databases, Gene set enrichment analysis (GSEA) and GEPIA were completed. We built co-expression network based on GSE26511 and one CC-related module was identified. Functional analysis of this module showed that extracellular space and Signaling pathways regulating pluripotency of stem cells were most related pathways. PPI network screened GNG11 as the most valuable protein. Cox analysis showed that ACKR1 was negatively correlated with CC progression, which was validated in Gene Expression Profiling Interactive Analysis (GEPIA) and datasets. Survival analysis was performed and showed the consistent result. GSEA set enrichment analysis was also completed. This study showed hub functional terms and gene participated in CC and then speculated that ACKR1 might be tumor suppressor for CC.BACKGROUND Prognostic biomarkers are promising targets for cancer prevention and treatment. OBJECTIVE We try to filtrate survival-related genes for non-small cell lung cancer (NSCLC) via transcriptome analysis. METHODS Transcriptome data and clinical information of Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), mainly subtypes of NSCLC, were obtained from The Cancer Genome Atlas (TCGA) program. Differentially expressed genes (DEGs) analyzed by DESeq2 package were regarded as candidate genes. For survival analysis, univariate and multivariate Cox regression were applied to select biomarkers for overall survival (OS) and progression-free survival (PFS), where univariate analysis was for preliminary filtration and multivariate analysis considering age, gender, TNM parameters and clinical stage was for ultimate determination. Gene ontology (GO) analysis and pathway enrichment were used for biological annotation. RESULTS We ultimately acquired a series of genes closely related to prognosis. For LUAD, we determined 314 OS-related genes and 275 PFS-related genes, while 54 OS-related genes and 78 PFS-related genes were chosen for LUSC. The final biological analysis indicated important function of proliferative signaling in LUAD but for LUSC, only cornification process had statistical meaning. CONCLUSIONS We strictly determined prognostic genes of NSCLC, which would contribute to its carcinogenesis investigation and therapeutic methods improvement.