Dataset Information

Cancer-related long noncoding RNAs show aberrant expression profiles and competing endogenous RNA potential in esophageal adenocarcinoma.

ABSTRACT: Long non-coding RNAs (lncRNAs) govern gene expression by competitively binding to microRNA response elements (MREs). Although they were initially considered as transcriptional noise, lncRNAs have attracted increased attention in oncology. Dysregulation of lncRNAs occurs in various types of human tumor, including esophageal adenocarcinoma (EAC). However, the functions of these cancer-associated lncRNAs and of their related competitive endogenous RNA (ceRNA) network in EAC remains unknown. To determine the relevant potential mechanisms, the present study analyzed the transcriptome sequencing data and clinical information of 79 patients with EAC, including 79 tumor samples and 11 normal samples, which were obtained from The Cancer Genome Atlas esophageal cancer project. The edgeR v3.25.0 software was used for differential gene expression analysis. The results exhibited 561 cancer-associated lncRNAs with a >2.0-fold change and a false discovery rate-adjusted P<0.01. Among these lncRNAs, 26 were significantly associated with patient overall survival. According to data from bioinformatics databases and differentially expressed RNAs, an lncRNA-regulated ceRNA network for EAC was constructed. The results demonstrated that the aberrantly expressed lncRNA-associated ceRNA network included 37 EAC cancer-associated lncRNAs, five miRNAs and 13 mRNAs. In conclusion, the present study identified novel lncRNAs as candidate prognostic biomarkers and revealed a potential regulatory network of gene expression in EAC.

SUBMITTER: Yu Y

PROVIDER: S-EPMC6781732 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Cancer-related long noncoding RNAs show aberrant expression profiles and competing endogenous RNA potential in esophageal adenocarcinoma.

Yu Yang Y Chen Xingxing X Cang Shundong S

Oncology letters 20190905 5

Long non-coding RNAs (lncRNAs) govern gene expression by competitively binding to microRNA response elements (MREs). Although they were initially considered as transcriptional noise, lncRNAs have attracted increased attention in oncology. Dysregulation of lncRNAs occurs in various types of human tumor, including esophageal adenocarcinoma (EAC). However, the functions of these cancer-associated lncRNAs and of their related competitive endogenous RNA (ceRNA) network in EAC remains unknown. To dete ...[more]

PMID: 31611990

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Project description:Liver cancer is the most common cancer and is the epitome of a recalcitrant cancer. Increasing evidence shown that long noncoding RNAs (lncRNA) were associated with cancer-related death and could function as a competing endogenous RNA (ceRNA). To explore regulatory roles and potential prognostic biomarkers of lncRNA for liver cancer, RNA-sequencing expression data were downloaded from the TCGA database and GEO database. A total of 357 patients were randomly divided into a discovery group and a validation group, of which 313 patients can obtain clinical data. In discovery phrase, 58 lncRNAs, 16 miRNAs, and 34 mRNAs were screened to construct the ceRNA network based on 252 patients employed from discovery group. Univariate and multivariate Cox hazard regression analysis model revealed that five lncRNAs (AATK-AS1, C10orf91, LINC00162, LINC00200, and LINC00501) from 58 lncRNAs were formulated to predict the overall survival (OS). We used the value of gene expression and regression coefficients to construct a risk score based on the five lncRNAs. Next, we validated our model in the GSE116174 dataset (n = 64) and the validation group (n = 94) from TCGA database. Subgroup analysis suggest that the five lncRNAs played critical parts in early stage in cancer from both discovery and validation groups. The five lncRNAs were also found to be associated with immune cells infiltration including CD4+ memory activated, NK cells activated and mast cells activated, then the results were also validated according to the validation group. Furthermore, KEGG pathway enrichment analysis showed that these nine coexpressed modules using the method of WGCNA, and many of these pathways are associated with the development and progression of disease. At last, the transcription factor binding sites (TFBS) of the five lncRNAs were predicted, which help us to understand the potential mechanism that the TFBS adjusted the ceRNA network. In summary, the ceRNA regulatory network may contribute to a better understanding of liver cancer mechanism and provide potential prognostic biomarkers and therapeutic targets.

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.

Dataset Information

Cancer-related long noncoding RNAs show aberrant expression profiles and competing endogenous RNA potential in esophageal adenocarcinoma.

Publications

Cancer-related long noncoding RNAs show aberrant expression profiles and competing endogenous RNA potential in esophageal adenocarcinoma.

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