Identification of serum prognostic marker miRNAs and construction of microRNA-mRNA networks of esophageal cancer.

Yue Jiang,Wenbin Shen,Jianjun Han,Fei Gao,Guangming Yi,Hongchun Dai,Mi Chen,Yun Wang,Tao Liu,Yiming Li,Jun He,Chengda Zhang,Xiaojun Liu,Li Jia,Suhwan Chang

doi:10.1371/journal.pone.0255479

Abstract

Esophageal cancer is a common tumor of the digestive system with poor prognosis. This study was to gain a better understanding of the mechanisms involved in esophageal cancer and to identify new prognostic markers. We downloaded the esophageal cancer miRNA expression profile microarray data (GSE113740, GSE112264, GSE122497, GSE113486, and GSE106817) from the GEO database, extracted the esophageal cancer miRNA sequencing data from The Cancer Genome Atlas (TCGA) database, and then used a bioinformatics approach to select common differentially expressed miRNAs (DEMs). Differentially expressed genes (DEGs) were selected by predicting DEM target genes using the miRWalk database and intersecting with differential genes obtained from TCGA database for esophageal cancer. The STRING database was used to obtain protein–protein interaction (PPI) relationships to construct the DEM-DEG network. Furthermore, we selected core genes and core miRNAs associated with esophageal cancer prognosis by performing survival and univariate/multivariate COX analysis on DEMs and DEGs in the network and performed GSEA analysis on core genes alone, and finally the expression of the markers was verified by qPCR in esophageal cancer cell lines Eca109, SKGT-4 and normal esophageal epithelial cells HEEC. Nine DEMs were obtained, of which three were upregulated and six were downregulated, and 326 DEGs were obtained, of which 105 were upregulated and 221 were downregulated. Survival univariate/multivariate COX analysis revealed that five genes, ZBTB16, AQP4, ADCYAP1R1, PDGFD, and VIPR2, and two microRNAs, miR-99a-5p, and miR-508-5p, were related to esophageal cancer prognosis. GSEA analysis showed that the following genes may be involved in esophageal cancer prognosis: ZBTB16 may through the MTOR signaling pathway, AQP4 through the GNRH signaling pathway, ADCYAP1R1 through the PPAR signaling pathway, VIPR2 through the P53 signaling pathway and PDGFD through the PENTOSE-PHOSPHATE signaling pathway.

Highlights

Esophageal cancer is a joint malignant tumor of the digestive system, primarily including two types of squamous carcinoma and adenocarcinoma [1, 2]
To explore the mechanism underlying esophageal cancer development and provide novel targets and pathways for esophageal cancer diagnosis and treatment, a bioinformatics approach was used to analyze the esophageal cancer data derived from the public databases The Cancer Genome Atlas (TCGA) [14] and Gene Expression Omnibus (GEO) [15]
The differential genes and differential miRNAs of esophageal cancer were integrated and analyzed to construct a miRNA-mRNA network for esophageal cancer, and survival analysis, univariate/multivariate COX analysis, and Gene set enrichment analysis (GSEA) analysis were conducted on the genes and miRNAs in the network to provide research directions for further basic experiments

Summary

Introduction

Esophageal cancer is a joint malignant tumor of the digestive system, primarily including two types of squamous carcinoma and adenocarcinoma [1, 2]. Approximately 500,000 patients are diagnosed with esophageal cancer each year, and their 5-year overall survival rate is generally

Methods

Results

Conclusion