PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan–Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.
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BackgroundBAP1 is an important tumor suppressor involved in various biological processes and is commonly lost or inactivated in clear-cell renal cell carcinoma (ccRCC). However, the role of the BAP1-deficient tumor competing endogenous RNA (ceRNA) network involved in ccRCC remains unclear. Thus, this study aims to investigate the prognostic BAP1-related ceRNA in ccRCC.MethodsRaw data was obtained from the TCGA and the differentially expressed genes were screened to establish a BAP1-related ceRNA network. Subsequently, the role of the ceRNA axis was validated using phenotypic experiments. Dual-luciferase reporter assays and fluorescence in situ hybridization (FISH) assays were used to confirm the ceRNA network.ResultsNuclear enriched abundant transcript 1 (NEAT1) expression was significantly increased in kidney cancer cell lines. NEAT1 knockdown significantly inhibited cell proliferation and migration, which could be reversed by miR-10a-5p inhibitor. Dual-luciferase reporter assay confirmed miR-10a-5p as a common target of NEAT1 and Serine protease inhibitor family E member 1 (SERPINE1). FISH assays revealed the co-localization of NEAT1 and miR-10a-5p in the cytoplasm. Additionally, the methylation level of SERPINE1 in ccRCC was significantly lower than that in normal tissues. Furthermore, SERPINE1 expression was positively correlated with multiple immune cell infiltration levels.ConclusionsIn BAP1-deficient ccRCC, NEAT1 competitively binds to miR-10a-5p, indirectly upregulating SERPINE1 expression to promote kidney cancer cell proliferation. Furthermore, NEAT1/miR-10a-5p/SERPINE1 were found to be independent prognostic factors of ccRCC.
This study aimed to identify enzalutamide resistant-related (EnzR-related) circRNAs and to characterize and validate circRNA-miRNA-mRNA ceRNA regulatory network and corresponding prognostic signature of prostate cancer patients. Methods: We obtained circRNA expression microarray from the Gene Expression Omnibus (GEO) database and performed differential expression analysis to identify EnzR-related circRNAs using the limma package. The miRNA and mRNA expression profiling were downloaded and performed differential expression analysis, then overlapped with predicted candidates. Next, we established circRNA-miRNA-mRNA ceRNA network and PPI network utilized Cytoscape software and STRING database, respectively. In addition, univariate and Lasso Cox regression analyses were applied to generate a prognostic signature. Receiver operating characteristic (ROC) curves and Kaplan-Meier analysis were used to evaluate the reliability and sensitivity of the signature. Ultimately, we chose hsa_circ_0047641 to validate the feasibility of the EnzR-related ceRNA regulatory pathway using qRT-PCR, CCK8 and Transwell assays. Results: We identified 13 EnzR-related circRNAs and constructed a ceRNA regulatory network that contained two downregulated circRNAs (has-circ-00000919 and has-circ -0000036) and two upregulated circRNAs (has-circ-0047641 and has-circ-0068697), and their sponged 6 miRNAs and 167 targeted mRNAs. Subsequently, these targeted mRNAs were performed to implement PPI analysis and to identify 10 Hub genes. Functional enrichment analysis provided new ways to seek potential biological functions. Besides, we established a prognostic signature of PCa patients based on 8 prognostic-associated mRNAs. We confirmed that the survival rates of PCa patients with high-risk subgroup were slightly lower than those with low-risk subgroup in the TCGA dataset (p<0.001), and ROC curves revealed that the AUC value for prognostic signature was 0.816. Finally, the functional analysis suggested that knockdown of hsa_circ_0047641 could inhibit the progression of PCa and could reverse Enz-resistance in vitro. Conclusion:We identified 13 EnzR-related circRNAs, and constructed and confirmed that EnzR-related circRNA-miRNA-mRNA ceRNA network and corresponding prognostic signature could be a useful prognostic biomarker and therapeutic target.
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