Construction of long non-coding RNA- and microRNA-mediated competing endogenous RNA networks in alcohol-related esophageal cancer

Abstract

The current study aimed to explore the lncRNA-miRNA-mRNA networks associated with alcohol-related esophageal cancer (EC). RNA-sequencing and clinical data were downloaded from The Cancer Genome Atlas and the differentially expressed genes (DEGs), long non-coding RNAs (lncRNAs, DELs), and miRNAs (DEMs) in patients with alcohol-related and non-alcohol-related EC were identified. Prognostic RNAs were identified by performing Kaplan-Meier survival analyses. Weighted gene co-expression network analysis was employed to build the gene modules. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed based on our in silico analyses using data from miRcode, starBase, and miRTarBase databases. Functional enrichment analysis was performed for the genes in the identified ceRNA networks. A total of 906 DEGs, 40 DELs, and 52 DEMs were identified. There were eight lncRNAs and miRNAs each, including ST7-AS2 and miR-1269, which were significantly associated with the survival rate of patients with EC. Of the seven gene modules, the blue and turquoise modules were closely related to disease progression; the genes in this module were selected to construct the ceRNA networks. SNHG12-miR-1-ST6GAL1, SNHG3-miR-1-ST6GAL1, SPAG5-AS1-miR-133a-ST6GAL1, and SNHG12-hsa-miR-33a-ST6GA interactions, associated with the N-glycan biosynthesis pathway, may have key roles in alcohol-related EC. Thus, the identified biomarkers provide a novel insight into the molecular mechanism of alcohol-related EC.

Fig 1. The flow chart of this study.

Fig 2. Cluster analysis of the selected RNAs.

A. The distribution of expression density of mRNAs, miRNAs, and lncRNAs. The peak expression density increased after filtering the RNAs with expression level < 1. B. Clustering map of samples based on differential expression of genes, miRNAs, and lncRNAs. In which, the samples in one group tended to cluster together.

Fig 3. Prognosis related lncRNAs and miRNAs.

Univariate Cox regression for differentially expressed lncRNAs and miRNAs identified eight lncRNAs and eight miRNAs were associated with overall survival of patients with alcohol-related esophageal cancer. Kaplan–Meier survival curves showed the survival differences between high and low expression (grouping by median expression value) of the eight prognostic lncRNAs (A) and eight prognostic miRNAs (B).

Fig 4. Identification of the gene modules by weighted gene correlation network analysis (WGCNA).

A. Determination of parameter β of the adjacency function in the WGCNA algorithm. The higher the square value of the correlation coefficient is, the closer the network is to the distribution without network scale. B. Hierarchical cluster analysis dendrogram of gene modules along with corresponding color assignments. Each color represents a certain gene module C. The correlation between trait values of each module and disease phenotype. Y-axis represent gene significance value, and the gene modules with higher gene significance value were significantly associated with disease phenotype. D. The hierarchical cluster analysis of different modules showed the correlations among different modules. E. The principal component analysis (PCA) analysis of different modules.

Fig 5. DEL–DEM regulatory network in alcohol-related EC progression.

All the screened DELs were uploaded to both miRcode and starBase databases to predict lncRNA–miRNA interactions, of which 44 lncRNA–miRNA interactions involving DEMs were screened. The network was visualized based on the 44 lncRNA–miRNA interactions. Diamond and square nodes represent miRNAs and lncRNAs, respectively. Red and green colors represent upregulation and downregulation, respectively. DEL, differentially expressed lncRNA; DEM, differentially expressed miRNA.

Fig 6. DEM–DEG regulatory network in alcohol-related EC progression.

The 12 DEMs in DEL–DEM regulatory network were uploaded to miRTarBase database to predict their targeted genes, and the miRNA-target gene interactions involving DEGs in blue (A) and turquoise (B) modules were screened to construct miRNA-target gene networks, respectively. Diamond and round nodes represent miRNAs and target genes, respectively. Red and green colors represent upregulation and downregulation, respectively. DEM, differentially expressed miRNAs; DEG, differentially expressed genes.

Fig 7. CeRNA regulatory network in alcohol-related EC progression.

The lncRNA–miRNA interactions, and miRNA-target gene interactions were integrated as lncRNA-miRNA-target genes interactions, and ceRNA network was visualized based on lncRNA-miRNA-target genes interactions. A. The ceRNA network constructed by the DEGs in the blue module. B. The ceRNA network constructed by the DEGs in the turquoise module. Diamond, square, and round nodes represent miRNAs, lncRNAs, and mRNA, respectively. Red and green colors represent upregulation and downregulation, respectively.

Fig 8. Gene Ontology enrichment analysis of the genes in the ceRNA networks associated with alcohol-related EC.

A. The analysis of genes in the blue module. B. The analysis of genes in the turquoise module. The Gene Ontology annotation terms consist of molecular function (MF) annotation terms, cellular component (CC) annotation terms and biological process (BP) annotation terms. The length of column represent gene numbers enriched in this annotation term.