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.

Survival estimation in patients with stomach and esophageal carcinoma using miRNA expression profiles

“…Over-expression of hsa-miR-1301-3p induces cell proliferation and tumorigenesis in gastric cancer tissues [59] . Wu et al reported the differential expression of hsa-miR-1301-3p in ESCA, suggesting that this miRNA could be used as a prognostic biomarker for ESCA [60] . Zhang and colleagues reported the downregulation of hsa-miR-532-5p in gastric cancer cells, and its expression is associated with poorer survival in patients with gastric cancer [61] .…”

A Prognostic Signature Based on Immunogenomic Profiling Offers Guidance for Esophageal Squamous Cell Cancer Treatment

“…A few previous studies have stratified subtypes of ESCC based on genomic profiling ( 7 – 9 , 39 ). However, the stratification of ESCC by immune signatures is poorly studied.…”

“…Recent research has aimed to uncover biomarkers with early diagnostic and prognostic value against EC. A 3-miRNA signature (miR-1301-3p, miR-769-5p, and miR-431-5p) has been suggested as a novel prognostic biomarker for EC ( 7 ). Li et al.…”

“…Recent research has aimed to uncover biomarkers with early diagnostic and prognostic value against EC. A 3-miRNA signature (miR-1301-3p, miR-769-5p, and miR-431-5p) has been suggested as a novel prognostic biomarker for EC (7). Li et al developed a prognostic tool for ESCC based on 8 lncRNA and used weighted gene co-expression network analysis (WGCNA) to evaluate internal interaction between gene expressions (8).…”

Construction and Investigation of MicroRNA-mRNA Regulatory Network of Gastric Cancer with Helicobacter pylori Infection

“…Evidence has been increasing that abnormal miRNAs expression is involved in the pathogenesis and development of many cancers, which suggests the promising biomarkers for early diagnosis and therapy of tumors [5,10]. Now, a high-throughput platform combined with bioinformatics analysis has become a new way to identify biomarkers of disease [11,12].…”