Background: The diagnostic value and suitability of prostate cancer antigen 3 (PCA3) for the detection of prostate cancer (PCa) have been inconsistent in previous studies. Thus, the aim of the present meta-analysis was performed to systematically evaluate the diagnostic value of PCA3 for PCa. Materials and Methods: A meta-analysis was performed to search relevant studies using online databases EMBASE, PubMed and Web of Science published until February 1st, 2019. Ultimately, 65 studies met the inclusion criteria for this meta-analysis with 8.139 cases and 14.116 controls. The sensitivity, specificity, positive likelihood ratios (LR+), negative likelihood ratios (LR−), and other measures of PCA3 were pooled and determined to evaluate the diagnostic rate of PCa by the random-effect model. Results: With PCA3, the pooled overall diagnostic sensitivity, specificity, LR+, LR−, and 95% confidence intervals (CIs) for predicting significant PCa were 0.68 (0.64-0.72), 0.72 (0.68-0.75), 2.41 (2.16-2.69), 0.44 (0.40-0.49), respectively. Besides, the summary diagnostic odds ratio (DOR) and 95% CIs for PCA3 was 5.44 (4.53-6.53). In addition, the area under summary receiver operating characteristic (sROC) curves and 95% CIs was 0.76 (0.72-0.79). The major design deficiencies of included studies were differential verification bias, and a lack of clear inclusion and exclusion criteria. Conclusions: The results of this meta-analysis suggested that PCA3 was a non-invasive method with the acceptable sensitivity and specificity in the diagnosis of PCa, to distinguish between patients and healthy individuals. To validate the potential applicability of PCA3 in the diagnosis of PCa, more rigorous studies were needed to confirm these conclusions.

Objective: To explore fatty acid metabolism-related genes and signature, which could predict survival outcomes of clear cell renal carcinoma (ccRCC) patients. Materials and Methods: Transcriptional and survival data of fatty acid genes in ccRCC patients were retrieved from UCSC Xena and Geo DataSets. We first performed Lasso Cox regression analysis to identify survival-related genes. These genes were then used to construct metabolic-related gene signature and risk score. Enrichment analysis and immune component and chemotherapy response prediction were also performed. Results: In total, five survival-related genes were identified: AGR2, HAO2, IGF2BP1, MCCD1 and OLFM4 (p < 0.05). A series of survival value analyses revealed survival-related signature and risk score, including KM analysis (training set: p < 0.001; test set: p = 0.008). Four clinical indexes (T stage, N stage, M stage, and pathology) were positively correlated with risk score. Time-dependent ROC analysis yielded AUC value of 0.813. Immune landscape analysis revealed that risk score was strongly correlated with TAM score and cytotoxic score. Patients with high risk score and TAM score or cytotoxic score had the shortest survival time. Finally, inhibition of fatty acid metabolism in human ccRCC cell line produced corresponding changes in five genes, consistent with our preliminary results. Conclusion: We identified five survival-related genes (AGR2, HAO2, IGF2BP1, MCCD1 and OLFM4) in ccRCC patients. Our results also indicated that survival-related signature based on these genes is a potential robust prognostic biomarker for ccRCC in patients.

Immune-related genes are important factors in tumor progression. The main aim of this study was to identify the immune-related genes in kidney papillary cell carcinoma (pRCC) patients. We downloaded RNAseq data and clinical information of pRCC patients from the TCGA database and retrieved the immune-related genes list from Immport. From the data, we mined out 2,468 differential expression genes (DEGs) and 183 immune-related DEGs. Four hub DEGs (NTS, BIRC5, ELN, and CHGA) were identified after conducting Cox analysis and LASSO analysis. Moreover, the prognostic value of the signature based on four hub DEGs was verified using Kaplan–Meier analysis (P = 0.0041 in the training set and p = 0.021 in the test set) and ROC analysis (AUC: 0.957 in 1 year, 0.965 in 2 years, and 0.901 in 3 years in the training set, and 0.963 in 1 year, 0.898 in 2 years, and 0.742 in 3 years in the test set). Furthermore, we found that the high-risk score group had a higher percentage of B cells in the immune component, a higher expression of immune-related genes (CTLA4, LAG3, PDCD1LG2, and TIGIT), and a better immunotherapy response.

Objective: To determine survival rates and the underlying mechanism of genes in the TRIM family in Kidney Clear Cell Carcinoma (KIRC). Methods: Transcriptional and survival data of TRIM genes in KIRC patients were retrieved from the UCSC Xena, and GEPIA databases. The function of TRIM genes in KIRC was investigated, focusing on potential ubiquitination, miRNAs regulation, and enrichment analysis. Next, TRIM gene survival values were determined, followed by the development of a survival-related signature. Results: Only TRIM26 was down expressed in the carcinoma tissue and had a survival value in KIRC relative to control tissues, which was supplied by vitro experiment. The patients with lower expression of TRIM26 would have the chance to live a shorter time. SNRPB, which also plays a role in ubiquitination, directly interacted with TRIM26. Moreover, two miRNAs (hsa-let-7i-5p, and hsa-miR-1228-5p) that regulated levels of TRIM26 expression were also identified. Next, we constructed a signature (TRIM4/7/27/58/65/72) and found that high-risk scores of the signature were associated with poor survival rates in KIRC patients. while its resultant risk scores were correlated with immune cell components and markers. Conclusions: TRIM26 was differentially expressed between KIRC and normal tissues and had a survival value in the KIRC. hsa-let-7i-5p/hsa-miR-1228-5p-TRIM26-SNRPB was a potential mechanism axis that may play a role on the KIRC cells. A survival signature (TRIM4/7/27/58/65/72) was successfully established to predict the survival of KIRC patients.

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