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.
Cancer is a mortal disease that can invade other parts of the body and cause severe complications. Despite their continuous progress, conventional cancer therapies including surgery, chemotherapy, and radiation therapy...
Purpose/Objective(s): The optimal management of men with high-risk prostate cancer (HRPC) is controversial. Currently, the National Comprehensive Cancer Network (NCCN) recommends external beam radiation therapy (EBRT) with androgen deprivation therapy (ADT) or radical prostatectomy (RP). Men with HRPC who undergo RP have an increased risk for positive margins and/or pT3 disease. This may lead to higher rates of adjuvant or salvage RT, which has been shown to have negative influences on urinary function, potency, and quality-of-life. We therefore sought to identify trends and predictors of positive margins in men with HRPC undergoing RP. Materials/Methods: We performed a retrospective analysis of the National Cancer Data Base from 2010 to 2014. We included men with non-metastatic HRPC (T3a or Gleason 8-10 on preoperative needle biopsy or PSA 20) who underwent RP. Information on preoperative Gleason score is only available from 2010 and onward. We performed univariable and multivariable logistic regression to evaluate temporal trends in rates of positive margins over the study period. Two-level hierarchical logistic regression was used to identify covariates associated with positive margins. Patients were clustered within treatment facilities to account for individual facility practice patterns. Results: We identified 58,084 men with HRPC who underwent RP between 2010 and 2014. During this study period, the use of robotic prostatectomy increased from 65% to 78% (P < 0.001). The rate of positive margins increased from 21% to 25% (P < 0.001). On hierarchical multivariable logistic regression, Gleason 9 vs. Gleason 6 (OR Z 2.97, P < 0.001) and Gleason 10 vs. Gleason 6 (OR Z 3.91, P < 0.001) were most strongly associated with positive margins. The proportion of men with Gleason 9 -10 disease who underwent RP increased from 16% to 25% over the study period. Other significant covariates associated with increased odds of positive margins included PSA >10-20 (OR Z 1.61, P < 0.001) or >20 (OR Z 2.04, P < 0.001) vs. PSA 10 and T3-T4 vs. T2 disease (OR Z 1.81, P < 0.001). Academic facility vs. community facility (OR Z 0.78, P Z 0.005) and robotic (OR Z 0.88, P Z 0.007) or laparoscopic (OR Z 0.67, P < 0.001) vs. open prostatectomy were associated with decreased offs of positive margins. Nineteen percent of the total variance was explained by inter-facility variation. Conclusion: From 2010 to 2014, the proportion of men with HRPC undergoing RP with positive margins increased by 19%, and the proportion of men with Gleason 9-10 disease undergoing RP increased by 56%. Significant practice variation was observed. Given that adjuvant EBRT is often recommended for positive margins, men with HRPC choosing RP upfront should be counseled on the potential need for additional treatment that may negatively impact quality-of-life.
The study aimed to identify an autophagy-related molecular subtype and characterize a novel defined autophagy-immune related genes score (AI-score) signature and prognosis model in bladder cancer (BLCA) patients using public databases. Methods: The transcriptome cohorts downloaded from TCGA and GEO database were carried out with genomic analysis and unsupervised methods to obtain autophagy-related molecular subtypes. The single-sample gene-set enrichment analysis (ssGSEA) was utilized to perform immune subtype clustering. We defined a novel autophagy subtype and evaluated the role in TME cell infiltration. Then, the principal-component analysis (PCA) was applied to construct an AI-score signature. Subsequently, two immunotherapeutic cohorts were used to evaluate the predictive value in immunotherapeutic benefits and immune response. Finally, univariate, Lasso and multivariate Cox regression algorithm were used to construct and evaluate an autophagy-immune-related genes prognosis model. Also, qRT-PCR and IHC was applied to validate the expression of the 6 genes in the model. Results: Three distinct autophagy clusters and immune-related clusters were identified, and a novel autophagy-related molecular subtypes were defined. Furthermore, the roles in TME cell infiltration and clinical traits for the autophagy subtypes were characterized. Meanwhile, we constructed an AI-score signature and demonstrated it could predict genetic mutation, clinicopathological traits, prognosis, and TME stromal activity. We found that it could accurately predict the clinicopathological characteristics and immune response of individual BLCA patients and provide guidance for selecting immunotherapy. Ultimately, we constructed and verified an autophagy-immune-related prognostic model of BLCA patients and established a prognostic nomogram with a good prediction accuracy. Conclusion:We constructed AI-score signatures and prognosis risk model to characterize their role in clinical features and TME immune cell infiltration. It revealed that the AI-score signature and prognosis model could be a valid predictive tool, which could accurately predict the prognosis of BLCA patients and contribute to choosing effective personalized immunotherapy strategies.
Aging is a major risk factor for prostate cancer (PCa), and prostatic stromal cells may also promote PCa progression. Accordingly, stromal cells do not equally promote PCa in older males and younger males. Therefore, it is also possible that the expression of androgen receptors (ARs) by prostatic stromal cells in older versus younger males plays different roles in PCa progression. Using a gene knockdown technique and coculture system, we found that the knockdown of the AR in prostatic stromal cells obtained from younger males could promote the invasiveness and metastasis of cocultured PC3/LNCaP cells in vitro. By contrast, the invasiveness and metastasis of LNCaP cells was inhibited when cocultured with prostatic stromal cells from older males that when AR expression was knocked down. Moreover, after targeting AR expression with small hairpin RNA (shRNA), matrix metalloproteinase (MMP) expression in stromal cells was observed to increase in the younger group, but decreased or remained unchanged in the older group. One exception, however, was observed with MMP9. In vivo, after knocking down AR expression in prostatic stromal cells, the incidence of metastatic lymph nodes was observed to increase in the younger age group, but decreased in the older age group. Together, these data suggest that the AR in prostatic stromal cells played opposite roles in PCa metastasis for older versus younger males. Therefore, collectively, the function of the AR in prostatic stromal cells appears to change with age, and this may account for the increased incidence of PCa in older males.
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