Dataset Information

A Novel Three-miRNA Signature Identified Using Bioinformatics Predicts Survival in Esophageal Carcinoma.

ABSTRACT: Objective:We identified differentially expressed microRNAs (DEMs) between esophageal carcinoma (ESCA) tissues and normal esophageal tissues. We then constructed a novel three-miRNA signature to predict the prognosis of ESCA patients using bioinformatics analysis. Materials and Methods. We combined two microarray profiling datasets from the Gene Expression Omnibus (GEO) database and RNA-seq datasets from the Cancer Genome Atlas (TCGA) database to analyze DEMs in ESCA. The clinical data from 168 ESCA patients were selected from the TCGA database to assess the prognostic role of the DEMs. The TargetScan, miRDB, miRWalk, and DIANA websites were used to predict the miRNA target genes. Functional enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery (David), and protein-protein interaction (PPI) networks were obtained using the Search Tool for the Retrieval of Interacting Genes database (STRING). Results:With cut-off criteria of P < 0.05 and |log2FC|?>?1.0, 33 overlapping DEMs, including 27 upregulated and 6 downregulated miRNAs, were identified from GEO microarray datasets and TCGA RNA-seq count datasets. The Kaplan-Meier survival analysis indicated that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) was significantly associated with the overall survival of ESCA patients. The results of univariate and multivariate Cox regression analysis showed that the three-miRNA signature was a potential prognostic factor in ESCA. Furthermore, the gene functional enrichment analysis revealed that the target genes of the three miRNAs participate in various cancer-related pathways, including viral carcinogenesis, forkhead box O (FoxO), vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (ErbB2), and mammalian target of rapamycin (mTOR) signaling pathways. In the PPI network, three target genes (MAPK1, RB1, and CLTC) with a high degree of connectivity were selected as hub genes. Conclusions:Our results revealed that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) is a potential novel prognostic biomarker for ESCA.

SUBMITTER: Wu K

PROVIDER: S-EPMC7035545 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Novel Three-miRNA Signature Identified Using Bioinformatics Predicts Survival in Esophageal Carcinoma.

Wu KunZhe K Zhang ChunDong C Zhang Cheng C Dai DongQiu D

BioMed research international 20200210

<h4>Objective</h4>We identified differentially expressed microRNAs (DEMs) between esophageal carcinoma (ESCA) tissues and normal esophageal tissues. We then constructed a novel three-miRNA signature to predict the prognosis of ESCA patients using bioinformatics analysis. <i>Materials and Methods</i>. We combined two microarray profiling datasets from the Gene Expression Omnibus (GEO) database and RNA-seq datasets from the <i>Cancer</i> Genome Atlas (TCGA) database to analyze DEMs in ESCA. The cl ...[more]

PMID: 32104700

Similar Datasets

Project description:BackgroundIncreasing evidence shows that dysregulated long non-coding RNAs (lncRNAs) can serve as potential biomarkers for cancer prognosis. However, lncRNA signatures, as potential prognostic biomarkers for esophageal squamous cell carcinoma (ESCC), have been seldom reported.MethodsBased on our previous transcriptome RNA sequencing analysis from 15 paired ESCC tissues and adjacent normal tissues, we selected 10 lncRNAs with high score rank and characterized the expression of those lncRNAs, by qRT-PCR, in 138 ESCC and paired adjacent normal samples. These 138 patients were divided randomly into training (n = 77) and test (n = 59) groups. A prognostic signature of lncRNAs was identified in the training group and validated in the test group and in an independent cohort (n = 119). Multivariable Cox regression analysis evaluated the independence of the signature in overall survival (OS) and disease-free survival (DFS) prediction. GO and KEGG pathway analysis, combined with cell transwell and proliferation assays, are applied to explore the function of the three lncRNAs.ResultsA novel three-lncRNA signature, comprised of RP11-366H4.1.1 (ENSG00000248370), LINC00460 (ENSG00000233532) and AC093850.2 (ENSG00000230838), was identified. The signature classified patients into high-risk and low-risk groups with different overall survival (OS) and disease-free survival (DFS). For the training group, median OS: 23.1 months vs. 39.1 months, P < 0.001; median DFS: 15.2 months vs. 33.3 months, P < 0.001. For the test group, median OS: 23 months vs. 59 months, P < 0.001; median DFS: 16.4 months vs. 50.8 months, P < 0.001. For the independent cohort, median OS: 22.4 months vs. 60.4 months, P < 0.001). The signature indicates that patients in the high-risk group show poor OS and DFS, whereas patients with a low-risk group show significantly better outcome. The independence of the signature was validated by multivariable Cox regression analysis. GO and KEGG pathway analysis for 588 protein-coding genes-associated with the three lncRNAs indicated that the three lncRNAs were involved in tumorigenesis. In vitro assays further demonstrated that the three lncRNAs promoted the migration and proliferation of ESCC cells.ConclusionsThe three-lncRNA signature is a novel and potential predictor of OS and DFS for patients with ESCC.

Project description:The aim of the present study was to identify microRNAs (miRNAs) that predict the prognosis of patients with nasopharyngeal carcinoma by integrated bioinformatics analysis. First, the original microarray dataset GSE32960, including 312 nasopharyngeal carcinomas and 18 normal samples, was downloaded from the Gene Expression Omnibus database. In addition, 46 differentially expressed miRNAs (DEMs) were screened. Then, four miRNAs, including hsa‑miR‑142‑3p, hsa‑miR‑150, hsa‑miR‑29b, and hsa‑miR‑29c, were obtained as prognostic markers by combining univariate Cox regression analysis with weighted gene coexpression network analysis (WGCNA). Subsequently, the risk score of 312 NPC patients from the signature of miRNAs was calculated, and patients were divided into high‑risk or low‑risk groups. Notably, compared with patients with low‑risk scores, high‑risk groups had shorter disease‑free survival (DFS), overall survival (OS), and distant metastasis‑free survival (DMFS). Receiver operating characteristic curve (ROC) analysis indicated that the risk score was a very effective prognostic factor. Moreover, the Search Tool for the Database for Annotation, Visualization, and Integrated Discovery (DAVID), Cytoscape, starBase, and Retrieval of Interacting Genes database (STRING) were used to establish the miRNA‑mRNA correlation network and the protein‑protein interaction (PPI) network. In addition, the shared genes superimposing 888 protein‑coding genes targeted by four hub miRNAs and 1,601 upregulated differentially expressed mRNAs accounted for 127 and were used for subsequent gene functional enrichment analysis. In particular, biological pathway analysis indicated that these genes mainly participate in some vital pathways related to cancer pathogenesis, such as the focal adhesion, PI3K/Akt, p53, and mTOR signalling pathways. In summary, the identification of NPC patients with a four‑miRNA signature may increase the prognostic value and provide reference information for precision medicine.

Dataset Information

A Novel Three-miRNA Signature Identified Using Bioinformatics Predicts Survival in Esophageal Carcinoma.

Publications

A Novel Three-miRNA Signature Identified Using Bioinformatics Predicts Survival in Esophageal Carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure