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Identification of a six-lncRNA signature based on a competing endogenous RNA network for predicting the risk of tumour recurrence in bladder cancer patients.

ABSTRACT: Bladder cancer (BC) is the most common malignancy involving the urinary system, and is characterized by a high recurrence rate. It is important to identify potential lncRNA signatures capable of predicting tumour recurrence risk and assessing recurrence prognosis in BC patients. We extracted data from The Cancer Genome Atlas and identified 381 differentially expressed lncRNAs, 855 mRNAs and 70 miRNAs between non-recurrent and recurrent BC tissues. Subsequently, a competing endogenous RNA (ceRNA) network composed of 29 lncRNAs, 13 miRNAs and 4 mRNAs was established. We used univariate and multivariate Cox regression to analyse the relationship between the 29 lncRNAs and recurrence-free survival (RFS) in BC patients. Six lncRNAs had significant prognostic values, and their cumulative risk score indicated that this 6-lncRNA signature independently predicted RFS in BC patients. We applied a receiver operating characteristic (ROC) analysis to assess the efficiency of our prognostic models. High-risk patients exhibited a poorer prognosis than low-risk patients did. Additionally, the 6-lncRNA signature showed a significant correlation with BC clinicopathological characteristics, which indicates that it could be used for effective risk stratification. The current study provides novel insights into the lncRNA-related ceRNA network and this 6-lncRNA signature may be an independent prognostic factor in predicting the recurrence of BC patients.

SUBMITTER: Zhao D

PROVIDER: S-EPMC6930402 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of a six-lncRNA signature based on a competing endogenous RNA network for predicting the risk of tumour recurrence in bladder cancer patients.

Zhao Danfeng D Peng Qiang Q Wang Lu L Li Cong C Lv Yulin Y Liu Yong Y Wang Zhichao Z Fang Ruizhe R Wang Jiaqi J Liu Zhongqing Z Xu Wanhai W

Journal of Cancer 20200101 1

Bladder cancer (BC) is the most common malignancy involving the urinary system, and is characterized by a high recurrence rate. It is important to identify potential lncRNA signatures capable of predicting tumour recurrence risk and assessing recurrence prognosis in BC patients. We extracted data from The Cancer Genome Atlas and identified 381 differentially expressed lncRNAs, 855 mRNAs and 70 miRNAs between non-recurrent and recurrent BC tissues. Subsequently, a competing endogenous RNA (ceRNA) ...[more]

PMID: 31892978

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Project description:The aim of the present study was to identify novel microRNA (miRNA) or long noncoding RNA (lncRNA) signatures of laryngeal cancer recurrence and to investigate the regulatory mechanisms associated with this malignancy. Datasets of recurrent and nonrecurrent laryngeal cancer samples were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GSE27020 and GSE25727) to examine differentially expressed miRNAs (DE‑miRs), lncRNAs (DE‑lncRs) and mRNAs (DEGs). miRNA‑mRNA and lncRNA‑miRNA networks were constructed by investigating the associations among these RNAs in various databases. Subsequently, the interactions identified were combined into a competing endogenous RNA (ceRNA) regulatory network. Feature genes in the miRNA‑mRNA network were identified via topological analysis and a recursive feature elimination algorithm. A support vector machine (SVM) classifier was established using the betweenness centrality values in the miRNA‑mRNA network, consisting of 32 optimal feature‑coding genes. The classification effect was tested using two validation datasets. Furthermore, coding genes in the ceRNA network were examined via pathway enrichment analyses. In total, 21 DE‑lncRs, 507 DEGs and 55 DE‑miRs were selected. The SVM classifier exhibited an accuracy of 94.05% (79/84) for sample classification prediction in the TCGA dataset, and 92.66 and 91.07% in the two validation datasets. The ceRNA regulatory network comprised 203 nodes, corresponding to mRNAs, miRNAs and lncRNAs, and 346 lines, corresponding to the interactions among RNAs. In particular, the interactions with the highest scores were HLA complex group 4 (HCG4)‑miR‑33b, HOX transcript antisense RNA (HOTAIR)‑miR‑1‑MAGE family member A2 (MAGEA2), EMX2 opposite strand/antisense RNA (EMX2OS)‑miR‑124‑calcitonin related polypeptide α (CALCA) and EMX2OS‑miR‑124‑γ‑aminobutyric acid type A receptor γ2 subunit (GABRG2). Gene enrichment analysis of the genes in the ceRNA network identified that 11 pathway terms and 16 molecular function terms were significantly enriched. The SVM classifier based on 32 feature coding genes exhibited high accuracy in the classification of laryngeal cancer samples. miR‑1, miR‑33b, miR‑124, HOTAIR, HCG4 and EMX2OS may be novel biomarkers of recurrent laryngeal cancer, and HCG4‑miR‑33b, HOTAIR‑miR‑1‑MAGEA2 and EMX2OS‑miR‑124‑CALCA/GABRG2 may be associated with the molecular mechanisms regulating recurrent laryngeal cancer.

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.

Dataset Information

Identification of a six-lncRNA signature based on a competing endogenous RNA network for predicting the risk of tumour recurrence in bladder cancer patients.

Publications

Identification of a six-lncRNA signature based on a competing endogenous RNA network for predicting the risk of tumour recurrence in bladder cancer patients.

Similar Datasets

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