Dataset Information

A potential prognostic lncRNA signature for predicting survival in patients with bladder urothelial carcinoma.

ABSTRACT: Increasing evidence has highlighted the critical roles of long non-coding RNA (lncRNA) in cancer development and progression. However, the prognostic power of expression-based lncRNA signature for predicting overall survival in patients with Bladder Urothelial Carcinoma (BLCA) has not been investigated. Here, we performed a comprehensive analysis for lncRNA expression profiles and corresponding clinical information of 234 BLCA patients from The Cancer Genome Atlas (TCGA). We established a set of four-lncRNAs that were significantly associated with BLCA patients' survival. Using the prognostic four-lncRNA signature, we successfully classified the BLCA patients into high-risk and low-risk groups, and the prognostic power of the four-lncRNA signature was further validated in the testing dataset and entire dataset. Multivariate Cox regression and stratified analyses demonstrated that the prognostic power of the four-lncRNA signature was independent of other clinical variables. Functional enrichment analyses suggested the four prognostic lncRNAs may be involved in known BLCA-related biological processes and pathways. Our results demonstrated that the four-lncRNA signature could be novel independent biomarkers for predicting survival in patients with BLCA.

SUBMITTER: Bao Z

PROVIDER: S-EPMC5354674 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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A potential prognostic lncRNA signature for predicting survival in patients with bladder urothelial carcinoma.

Bao Zhenyu Z Zhang Weitao W Dong Dong D

Oncotarget 20170201 6

Increasing evidence has highlighted the critical roles of long non-coding RNA (lncRNA) in cancer development and progression. However, the prognostic power of expression-based lncRNA signature for predicting overall survival in patients with Bladder Urothelial Carcinoma (BLCA) has not been investigated. Here, we performed a comprehensive analysis for lncRNA expression profiles and corresponding clinical information of 234 BLCA patients from The Cancer Genome Atlas (TCGA). We established a set of ...[more]

PMID: 28060759

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Project description:BackgroundThe tumor microenvironment (TME) regulates the proliferation and metastasis of solid tumors and the effectiveness of immunotherapy against them. We investigated the prognostic role of TME-related genes based on transcriptomic data of bladder urothelial carcinoma (BLCA) and formulated a prediction model of TME-related signatures.MethodsMolecular subtypes were identified using the non-negative matrix factorization (NMF) algorithm based on TME-related genes from the TCGA database. TME-related genes with prognostic significance were screened with univariate Cox regression analysis and lasso regression. Nomogram was developed based on risk genes. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used for inner and outer validation of the model. Risk scores (RS) of patients were calculated and divided into high-risk group (HRG) and low-risk group (LRG) to compare the differences in clinical characteristics and PD-L1 treatment responsiveness between HRG and LRG.ResultsWe identified two molecular subtypes (C1 and C2) according to the NMF algorithm. There were significant differences in overall survival (OS) (p<0.05), progression-free survival (PFS) (p<0.05), and immune cell infiltration between the two subtypes. A total of eight TME-associated genes (CABP4, ZNF432, BLOC1S3, CXCL11, ANO9, OAS1, FBN2, CEMIP) with independent prognostic significance were screened to build prognostic risk models. Age (p<0.001), grade (p<0.001), and RS (p<0.001) were independent predictors of survival in BLCA patients. The developed RS nomogram was able to predict the prognosis of BLCA patients at 1, 3, and 5 years more potentially than the models of other investigators according to ROC and DCA. RS showed significantly higher values (p = 0.047) in patients with stable disease (SD)/progressive disease (PD) compared to patients with complete response (CR)/partial response (PR).ConclusionsWe successfully clustered and constructed predictive models for TME-associated genes and helped guide immunotherapy strategies.

Project description:BackgroundBoth lncRNAs and glycolysis are considered to be key influencing factors in the progression of bladder cancer (BCa). Studies have shown that glycolysis-related lncRNAs are an important factor affecting the overall survival and prognosis of patients with bladder cancer. In this study, a prognostic model of BCa patients was constructed based on glycolysis-related lncRNAs to provide a point of reference for clinical diagnosis and treatment decisions.MethodsThe transcriptome, clinical data, and glycolysis-related pathway gene sets of BCa patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Set Enrichment Analysis (GSEA) official website. Next, differentially expressed glycolysis-related lncRNAs were screened out, glycolysis-related lncRNAs with prognostic significance were identified through LASSO regression analysis, and a risk scoring model was constructed through multivariate Cox regression analysis. Then, based on the median of the risk scores, all BCa patients were divided into either a high-risk or low-risk group. Kaplan-Meier (KM) survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive power of the model. A nomogram prognostic model was then constructed based on clinical indicators and risk scores. A calibration chart, clinical decision curve, and ROC curve analysis were used to evaluate the predictive performance of the model, and the risk score of the prognostic model was verified using the TCGA data set. Finally, Gene Set Enrichment Analysis (GSEA) was performed on glycolysis-related lncRNAs.ResultsA total of 59 differentially expressed glycolysis-related lncRNAs were obtained from 411 bladder tumor tissues and 19 pericarcinomatous tissues, and 9 of those glycolysis-related lncRNAs (AC099850.3, AL589843.1, MAFG-DT, AC011503.2, NR2F1-AS1, AC078778.1, ZNF667-AS1, MNX1-AS1, and AC105942.1) were found to have prognostic significance. A signature was then constructed for predicting survival in BCa based on those 9 glycolysis-related lncRNAs. ROC curve analysis and a nomogram verified the accuracy of the signature.ConclusionThrough this study, a novel prognostic prediction model for BCa was established based on 9 glycolysis-related lncRNAs that could effectively distinguish high-risk and low-risk BCa patients, and also provide a new point of reference for clinicians to make individualized treatment and review plans for patients with different levels of risk.

Dataset Information

A potential prognostic lncRNA signature for predicting survival in patients with bladder urothelial carcinoma.

Publications

A potential prognostic lncRNA signature for predicting survival in patients with bladder urothelial carcinoma.

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