Comprehensive Analysis of lncRNAs Related to the Prognosis of Esophageal Cancer Based on ceRNA Network and Cox Regression Model

Abstract

Background. Esophageal cancer is one of the most deadly malignant tumors. Among the common malignant tumors in the world, esophageal cancer is ranked seventh, which has a high mortality rate. Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of various tumors. lncRNAs can competitively bind microRNAs (miRNAs) with mRNA, which can regulate the expression level of the encoded gene at the posttranscriptional level. This regulatory mechanism is called the competitive endogenous RNA (ceRNA) hypothesis, and ceRNA has important research value in tumor-related research. However, the regulation of lncRNAs is less studied in the study of esophageal cancer. Methods. The Cancer Genome Atlas (TCGA) database was used to download transcriptome profiling data of esophageal cancer. Gene expression quantification data contains 160 cancer samples and 11 normal samples. These data were used to identify differentially expressed lncRNAs and mRNAs. miRNA expression data includes 185 cancer samples and 13 normal samples. The differentially expressed RNAs were identified using the edgeR package in R software. Then, the miRcode database was used to predict miRNAs that bind to lncRNAs. MiRTarBase, miRDB, and TargetScan databases were used to predict the target genes of miRNAs. Cytoscape software was used to draw ceRNA network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using DAVID 6.8. Finally, multifactor cox regression was used to screen lncRNAs related to prognosis. Results. We have screened 1331 DElncRNAs, 3193 DEmRNAs, and 162 DEmiRNAs. Among them, the ceRNA network contains 111 lncRNAs, 11 miRNAs, and 63 DEmRNAs. Finally, we established a prediction model containing three lncRNAs through multifactor Cox regression analysis. Conclusions. Our research screened out three independent prognostic lncRNAs from the ceRNA network and constructed a risk assessment model. This is helpful to understand the regulatory role of lncRNAs in esophageal cancer.

Identification of the miRNA-mRNA regulatory network associated with radiosensitivity in esophageal cancer based on integrative analysis of the TCGA and GEO data

“…Moreover, there is much to evidence to highlight that dysregulation of miRNAs is involved in the development and the radiosensitivity of esophageal cancer via regulation of their target mRNAs [ 12 , 13 , 24 ]. In addition, a risk assessment model based on lncRNAs, miRNAs, and mRNAs has also been constructed to screen three independent prognostic lncRNAs, thereby exploring a more effective lncRNA-miRNA-mRNA signature to predict the prognosis of esophageal cancer [ 27 ]. In our study, the results of GO and KEGG function enrichment analysis revealed that DEmRNAs in the miRNA-mRNA regulatory network may participate in the occurrence and development of esophageal cancer.…”

“…In our study, the results of GO and KEGG function enrichment analysis revealed that DEmRNAs in the miRNA-mRNA regulatory network may participate in the occurrence and development of esophageal cancer. The use of GO and KEGG analyses to explore the potential biological mechanisms of DEmRNAs is well-established [ 27 ]. Similarly, a prior study also reported that DEmRNAs are primarily enriched in BP, and the regulatory networks depending on DEmRNAs are constructed, which indicates potential target miRNAs and genes for future investigations of esophageal cancer [ 28 ].…”

Integrated Analysis of a ceRNA (lncRNA-miRNA-mRNA) Regulatory Network for Prostate Cancer

“…Network. We performed survival analyses of DE-miRNAs, DE-lncRNAs, and DE-mRNAs involved in the construction of the ceRNA regulatory complex, respectively, to evaluate the efectiveness of diferent RNA expression levels on the survival prognosis of PCa patients [22]. First, we sorted out the survival time and survival state of PCa patients, and then compared the survival diferences of patients with three types of diferent RNAs with diferent expression levels by using R package survival, and drew survival curves to visualize the results [23].…”

Prognostic Roles of ceRNA Network-Based Signatures in Gastrointestinal Cancers

“…For example, based on integrated analysis of lncRNA-mediated ceRNA network, Li et al. ( 47 ) and Zhang et al. ( 48 ) successfully developed a novel three-lncRNA and six-lncRNA panel with prognostic value for EC patients by employing multiple Cox regression analysis, respectively.…”

“…). For example, based on integrated analysis of lncRNA-mediated ceRNA network, Li et al(47) and Zhang et al(48) successfully developed a novel three-lncRNA and six-lncRNA panel with prognostic value for EC patients by employing multiple Cox regression analysis, respectively. Similarly, Wang et al(49) established a novel eight-gene signature as an independent prognostic factor for predicting the OS of patients with esophageal adenocarcinoma (EAC).…”