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Explore prognostic biomarker of bladder cancer based on competing endogenous network.

ABSTRACT: Bladder cancer (BC) is the most common tumor of the urinary tract. Increasing evidence showed that long non-coding RNA (lncRNA) is a critical regulator in cancer development and progression. However, the functions of lncRNAs in the development of BC remain mostly undefined. In the present study, based on RNA sequence profiles from The Cancer Genome Atlas database, we identified 723 lncRNAs, 157 miRNAs, and 1816 mRNAs aberrantly expressed in BC tissues. A competing endogenous RNA network, including 49 lncRNAs, 17 miRNAs, and 36 mRNAs, was then established. The functional enrichment analyses showed that the mRNAs in the ceRNA network mainly participated in 'regulation of transcription' and 'pathways in cancer'. Moreover, the Cox regression analyses demonstrated that three lncRNAs (AC112721.1, TMPRSS11GP, and ADAMTS9-AS1) could serve as independent risk factors. We established a risk prediction model with these lncRNAs. Kaplan-Meier curve analysis showed that high-risk patients' prognosis was lower than that of low-risk patients (P=0.001). The present study provides novel insights into the lncRNA-mediated ceRNA network and the potential of lncRNAs to be candidate prognostic biomarkers in BC, which could help better understand the pathological changes and pathogenesis of BC and be useful for clinical studies in the future.

SUBMITTER: Li F

PROVIDER: S-EPMC7711062 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Explore prognostic biomarker of bladder cancer based on competing endogenous network.

Li Faping F Guo Hui H Liu Bin B Liu Nian N Xu Zhixiang Z Wang Yishu Y Zhou Honglan H

Bioscience reports 20201201 12

Bladder cancer (BC) is the most common tumor of the urinary tract. Increasing evidence showed that long non-coding RNA (lncRNA) is a critical regulator in cancer development and progression. However, the functions of lncRNAs in the development of BC remain mostly undefined. In the present study, based on RNA sequence profiles from The Cancer Genome Atlas database, we identified 723 lncRNAs, 157 miRNAs, and 1816 mRNAs aberrantly expressed in BC tissues. A competing endogenous RNA network, includi ...[more]

PMID: 33169791

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Project description:BackgroundPancreatic cancer is an insidious and heterogeneous malignancy with poor prognosis that is often locally unresectable. Therefore, determining the underlying mechanisms and effective prognostic indicators of pancreatic cancer may help optimize clinical management. This study was conducted to develop a prognostic model for pancreatic cancer based on a competing endogenous RNA (ceRNA) network.MethodsWe obtained transcriptomic data and corresponding clinicopathological information of pancreatic cancer samples from The Cancer Genome Atlas (TCGA) database (training set). Based on the ceRNA interaction network, we screened candidate genes to build prediction models. Univariate Cox regression analysis was performed to screen for genes associated with prognosis, and least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive model. A receiver operating characteristic (ROC) curve was drawn, and the C-index was calculated to evaluate the accuracy of the prediction model. Furthermore, we downloaded transcriptomic data and related clinical information of pancreatic cancer samples from the Gene Expression Omnibus database (validation set) to evaluate the robustness of our prediction model.ResultsEight genes (ANLN, FHDC1, LY6D, SMAD6, ACKR4, RAB27B, AUNIP, and GPRIN3) were used to construct the prediction model, which was confirmed as an independent predictor for evaluating the prognosis of patients with pancreatic cancer through univariate and multivariate Cox regression analysis. By plotting the decision curve, we found that the risk score model is an independent predictor has the greatest impact on survival compared to pathological stage and targeted molecular therapy.ConclusionsAn eight-gene prediction model was constructed for effectively and independently predicting the prognosis of patients with pancreatic cancer. These eight genes identified show potential as diagnostic and therapeutic targets.

Project description:BackgroundIncreasing evidence has underscored the role of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the development and progression of tumors. Nevertheless, lncRNA biomarkers in lncRNA-related ceRNA network that can predict the prognosis of breast cancer (BC) are still lacking. The aim of our study was to identify potential lncRNA signatures capable of predicting overall survival (OS) of BC patients.MethodsThe RNA sequencing data and clinical characteristics of BC patients were obtained from the Cancer Genome Atlas database, and differentially expressed lncRNA (DElncRNAs), DEmRNAs, and DEmiRNAs were then identified between BC and normal breast tissue samples. Subsequently, the lncRNA-miRNA-mRNA ceRNA network of BC was established, and the gene oncology enrichment analyses for the DEmRNAs interacting with lncRNAs in the ceRNA network was implemented. Using univariate and multivariate Cox regression analyses, a four-lncRNA signature was developed and used for predicting the survival in BC patients. We applied receiver operating characteristic analysis to assess the performance of our model.ResultsA total of 1061 DElncRNAs, 2150 DEmRNAs, and 82 DEmiRNAs were identified between BC and normal breast tissue samples. A lncRNA-miRNA-mRNA ceRNA network of BC was established, which comprised of 8 DEmiRNAs, 48 DElncRNAs, and 10 DEmRNAs. Further gene oncology enrichment analyses revealed that the DEmRNAs interacting with lncRNAs in the ceRNA network participated in cell leading edge, protease binding, alpha-catenin binding, gamma-catenin binding, and adenylate cyclase binding. A univariate regression analysis of the DElncRNAs revealed 7 lncRNAs (ADAMTS9-AS1, AC061992.1, LINC00536, HOTAIR, AL391421.1, TLR8-AS1 and LINC00491) that were associated with OS of BC patients. A multivariate Cox regression analysis demonstrated that 4 of those lncRNAs (ADAMTS9-AS1, LINC00536, AL391421.1 and LINC00491) had significant prognostic value, and their cumulative risk score indicated that this 4-lncRNA signature independently predicted OS in BC patients. Furthermore, the area under the curve of the 4-lncRNA signature associated with 3-year survival was 0.696.ConclusionsThe current study provides novel insights into the lncRNA-related ceRNA network in BC and the 4 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of BC patients.

Project description:Background: Genetic characteristics remain underutilized for establishing prognostic models for colorectal cancer (CRC). We explored the underlying regulatory mechanisms of circular RNAs (circRNAs) that act as competing endogenous RNAs (ceRNAs) and constructed a gene-based nomogram to predict overall survival (OS) in patients with CRC. Methods: We obtained circRNA expression profiling data from the Gene Expression Omnibus (GEO) database. MicroRNA (miRNA) and mRNA expression profiles, with associated clinical data, were obtained from The Cancer Genome Atlas (TCGA). A ceRNA network was established using Cytoscape. Interactions between differential genes were analyzed, and hub genes were identified using the cytoHubba application. The R package "clusterProfiler" was used to evaluate the Gene Ontology (GO) annotations of the differentially expressed mRNAs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Database-extracted patients were randomized into a training and validation cohorts. A prognostic model was developed using the training set based on multivariate Cox analyses and was then assessed in the validation set. The accuracy of the model was evaluated using discrimination and calibration plots. Results: Thirteen circRNAs, 62 miRNAs, and 301 mRNAs were used to construct the ceRNA network; 10 hub genes were identified via the PPI network. Next, a circRNA- miRNA hub of gene-regulatory modules was established based on four differentially expressed circRNAs, eight differentially expressed miRNAs, and nine differentially expressed mRNAs (DEmRNAs). GO and KEGG pathway analyses indicated the possible association of DEmRNAs with CRC onset and progression. Multivariate analyses revealed that age, tumor stage, and CXCR5 expression were independent risk factors for OS. A CXCR5-based model was developed to predict the OS of patients with CRC in our training set. Our nomogram showed relatively good accuracy, with C-indices of 0.757 and 0.702 in the training and validation sets, respectively. The areas under the curve of the nomograms predicting 3- and 5-years OS were 0.749 and 0.805 in the training set and 0.706 and 0.779 in the validation set, respectively. Conclusions: Our data suggested that the hsa_circ_00001666/has-mir-1229/CXCR5 axis plays an important role in the pathogenesis of CRC, thereby identifying a potential therapeutic target. The proposed CXCR5-based nomogram may also assist surgeons in devising personalized treatments for patients with this disease.

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Explore prognostic biomarker of bladder cancer based on competing endogenous network.

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Explore prognostic biomarker of bladder cancer based on competing endogenous network.

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