ObjectiveProstate cancer is a malignant tumour that poses a serious risk to human health. Epidemiological studies suggest that it may be associated with vitamin D receptor gene (VDR) polymorphisms. Previous work investigated potential risks between Taq I (rs731236) and Bsm I (rs1544410) VDR polymorphisms with prostate cancer in humans; however, results are inconsistent.MethodsWe conducted a meta-analysis to retrieve genetic association analyses of rs731236 and rs1544410 polymorphisms with prostate cancer from studies published between 2006–2016. Pooled odds ratios with 95% confidence intervals were used to assess genetic associations, and heterogeneity was assessed by Q and I2statistics.ResultsOur findings suggest a significant association between rs731236 and prostate cancer risk in Asians and African Americans, but rs1544410 was not associated with prostate cancer under three genetic models.ConclusionFuture studies including larger sample sizes and the analysis of gene functions are needed to help develop prostate cancer treatment.

Background: Growing findings have demonstrated that interferon regulatory transcription factor (IRF) family members are linked to the progression of various cancers. However, the roles of IRFs in clear cell renal cell carcinoma (ccRCC) remain undefined. Herein, we conducted a comprehensive analysis using the bioinformatics method to evaluate the expression patterns, clinical significance, and regulation of IRFs-related mechanisms in patients with ccRCC. Methods: Data from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGA), and Gene Expression Omnibus (GEO) databases were used for investigation comprehensively. Specifically, we carried out a series of analyses to identify the candidate IRF and to explore its potential action mechanisms using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. What is more, we emphatically investigate the association of candidate IRF with tumor immunity in ccRCC through the CIBERSORT algorithm, TIMER and GEPIA databases. Results: Herein, IRF3 was identified as candidate IRF, which was highly expressed in ccRCC, and its overexpression was significantly associated with worse clinical outcomes and adverse overall survival. Uni-and multi-variate Cox regression analysis demonstrated that IRF3 overexpression was an independent predictor of worse prognosis. Functional enrichment analysis showed that IRF3 might participate in several cancer-related biological processes and signaling pathways, thereby promoting the progression of ccRCC. Additionally, we found that IRF3 was remarkably associated with tumor-infiltrating immune cells (TIICs) and various immune-related genes. Conclusion: Herein, we identified IRF3 from the IRF gene family members, which could serve as promising prognostic marker and therapeutic target in ccRCC.

Purpose The overall survival of clear cell renal cell carcinoma (ccRCC) is poor. Markers for early detection and progression could improve disease outcomes. This study aims to reveal the potential pathogenesis of ccRCC by integrative bioinformatics analysis and to further develop new therapeutic strategies. Patients and Methods RNA-seq data of 530 ccRCC cases in TCGA were downloaded, and a comprehensive analysis was carried out using bioinformatics tools. Another 14 tissue samples were included to verify the expression of selected lncRNAs by qRT-PCR. DGIdb database was used to screen out potential drugs, and molecular docking was used to explore the interaction and mechanism between candidate drugs and targets. Results A total of 58 differentially expressed lncRNAs (DElncRNAs) and 660 differentially expressed mRNAs (DEmRNAs) were identified in ccRCC. LINC02038, FAM242C, LINC01762, and PVT1 were identified as the optimal diagnostic lncRNAs, of which PVT1 was significantly correlated with the survival rate of ccRCC. GO analysis of cell components showed that DEmRNAs co-expressed with 4 DElncRNAs were mainly distributed in the extracellular area and the plasma membrane, involved in the transport of metal ions, the transport of proteins across membranes, and the binding of immunoglobulins. Immune infiltration analysis showed that MDSC was the most correlated immune cells with PVT1 and key mRNA SIGLEC8. Validation analysis showed that GABRD, SIGLEC8 and CDKN2A were significantly overexpressed, while ESRRB, ELF5 and UMOD were significantly down-regulated, which was consistent with the expression in our analysis. Furthermore, 84 potential drugs were screened by 6 key mRNAs, of which ABEMACICLIB and RIBOCICLIB were selected for molecular docking with CDKN2A, with stable binding affinity. Conclusion In summary, 4 key lncRNAs and key mRNAs of ccRCC were identified by integrative bioinformatics analysis. Potential drugs were screened for the treatment of ccRCC, providing a new perspective for disease diagnosis and treatment.