Background: LncRNA ROR, a tumor oncogene associated with various human cancers, has been reported to be involved in regulating various cellular processes, such as proliferation, apoptosis and invasion through targeting multiple genes. However, the molecular biological function in bladder cancer has not been clearly elucidated. The aim of this study is to explore ROR expression levels and evaluated its function in bladder cancer. Methods: LncRNA ROR expression levels in the 36 pairs of bladder cancer tissues (and corresponding non-tumor tissues) and bladder cancer cells were assessed by qRT-PCR. MTT assay, colony formation assay, flow cytometric analysis, wound healing assay, cell transwell assays, attachment/detachment and western blotting were performed to assess the effects of ROR on proliferation, apoptosis, migration/invasion and epithelial-to-mesenchymal (EMT) phenotypes in BC cells in vitro. ZEB1 is target of ROR. Rescue assays were performed to further confirm that ROR contributes to the progression of BC cells through targeting ZEB1. Results: LncRNA ROR was up-regulated in bladder cancer tissues (compared to adjacent non-tumor tissues) and was almost overexpression in bladder cancer cells (compared with normal urothelial cell line SVHUC-1 cells). Increased lncRNA ROR expression significantly promoted tumor cells proliferation, inhibited cells apoptosis, facilitated cells metastasis and contributed to the formation of EMT phenotype. While down-regulated ROR could obviously inhibit cells proliferation, promote cells apoptosis, inhibit metastasis and reverse EMT to MET. ZEB1 was a target gene of ROR and was positive correlation with the level of ROR in cancer tissues. Conclusion: These results indicated that lncRNA ROR was associated with tumor progression in bladder cancer cells.

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Background Bladder cancer is a multifactorial disease with increasing incidence and mortality. Genetic alterations and altered expressions of mRNAs, long non-coding RNAs (lncRNAs), and miRNAs have been shown to play important roles in the tumorigenesis of bladder cancer. However, the functions of key RNAs and their regulatory network in bladder cancer are still to be elucidated. Material/Methods RNA profiles were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed mRNAs, lncRNAs, and miRNAs in bladder cancer were acquired through analyses of data from 414 bladder cancer tissues and 19 normal bladder tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed by using “DAVID6.8” and the R package “ClusterProfile”. Protein–protein interaction and competing endogenous RNA (ceRNA) networks were constructed by using “STRING” database and Cytoscape 3.6.2. Based on the clinical data and Cox regression, a prognosis model was established, and survival analysis was performed. Results A total of 1819 mRNAs, 659 lncRNAs, and 160 miRNAs were identified as significantly differentially expressed in bladder cancer of which 52 mRNAs, 58 lncRNAs, and 22 miRNAs were incorporated in the ceRNA network. CFL2 and TPM2 were found to be downregulated and showed significant correlation to each other in bladder cancer. HOXB5 and 6 lncRNAs ( ADAMTS9-AS1, AC112721.1, LINC00460, AC110491.1, LINC00163, and HCG22 ) were strongly associated with high-grade, disease stages, and overall survival. Conclusions In this study, we have identified differentially expressed mRNAs, lncRNAs, and miRNAs in bladder cancer which were strongly associated with oncogenesis and prognosis. Further experimental studies are necessary to validate these results.

Background: Prostate cancer (PCa) is one of the most common cancers and the fifth leading cause of cancer-related death in men. Immune responses in the tumor microenvironment are hypothesized to be related to the prognosis of PCa patients; however, no studies are available to confirm the same. In this study, we aimed to explore the potential link between these two factors and identify new biomarkers to estimate the survival rate of PCa patients.Methods: A total of 490 cases were obtained from The Cancer Genome Atlas (TCGA) database. The gene expression data were analyzed by the ESTIMATE algorithm to evaluate the immune and stromal scores. The survival rate was calculated according to the case-specific clinical data. Enrichment analysis was performed to discover the main biological processes and signaling pathways of immune responses. We further identified and analyzed hub genes in the protein-protein interaction (PPI) network and evaluated their prognostic values.Results: Immune score significantly correlated with immune cell infiltration and overall survival of PCa patients. The genes CXCR4 and GPR183, identified as hub genes in the PPI network, correlated with immune cell infiltration and prognosis of PCa patients. Conclusion:CXCR4 and GPR183 participate in immune cell infiltration and function in PCa patients. The immune score, as well as the expression of CXCR4 and GPR183 in prostate cancer tissues, could be potential indexes for the prognosis of prostate cancer.

Background: To construct a prognostic risk model of bladder cancer (BC) from the perspective of long non-coding RNAs (lncRNAs) and ferroptosis, in order to guide clinical prognosis and identify potential therapeutic targets. Methods: In-hours BC samples were collected from 4 patients diagnosed with BC, who underwent radical cystectomy. Single cell transcriptome sequencing was performed and Seurat package were used for quality control and secondary analysis. LncRNAs expression profiles of BC samples were extracted from The Cancer Genome Atlas database. And sex, age, tumor, node, metastasis stage and other clinical data was downloaded at the same time. Ferroptosis-related lncRNAs were identified by co-expression analysis. We constructed a risk model by Cox regression and least absolute shrinkage and selection operator regression analyses. The predictive strength of the risk model for overall survival (OS) of patients with BC was evaluated by the log-rank test and Kaplan–Meier method. Finally, the enrichment analysis was performed and visualized. Results: We identified and included 15 prognostic ferroptosis-related lncRNAs (AL356740.1, FOXC2AS1, ZNF528AS1, LINC02535, PSMB8AS1, AL590428.1, AP000347.2, OCIAD1-AS1, AP001347.1, AC104986.2, AC018926.2, LINC00867, AC099518.4, USP30-AS1, and ARHGAP5-AS1), to build our ferroptosis-related lncRNAs risk model. Using this risk model, BC patients were divided into high and low-risk groups, and their respective survival lengths were calculated. The results showed that the OS of the low-risk group was significantly longer than that of the high-risk group. A nomogram was utilized to predict the survival rate of BC patients. As indicated in the nomogram, risk score was the most important indicator of OS in patients with BC. The ferroptosis-related lncRNAs risk model is an independent tool for prognostic risk assessment in patients with BC. Single cell transcriptome sequencing suggests that ferroptosis-related lncRNAs express specifically in BC tumor microenvironment. AL356740.1, LINC02535 and LINC00867 were mainly expressed in tumor cells. Conclusion: The risk model based on the ferroptosis-related lncRNAs and the genomic clinico-pathological nomogram could be used to accurately predict the prognosis of patients with BC. The lncRNAs used to build this model might become potential therapeutic targets in the future.