Five miRNAs Considered as Molecular Targets for Predicting Esophageal Cancer

Abstract

Background: Esophageal cancer (EC) is one of the most aggressive malignant gastrointestinal tumors; however the traditional therapies for EC are not effective enough. Great improvements are needed to explore new and valid treatments for EC. We aimed to screen the differentially expressed miRNAs (DEMs) in esophageal cancer and explore the pathogenesis of esophageal cancer along with functions and pathways of the target genes.

Material and methods: miRNA high-throughput sequencing data were downloaded from The Cancer Genome Atlas (TCGA), then the DEMs underwent principal component analysis (PCA) based on their expression value. Following that, TargetScan software was used to predict the target genes, and enrichment analysis and pathway annotation of these target genes were done by DAVID and KEGG, respectively. Finally, survival analysis between the DEMs and patient survival time was done, and the miRNAs with prediction potential were identified.

Results: A total of 140 DEMs were obtained, 113 miRNAs were up-regulated including hsa-mir-153-2, hsa-mir-92a-1 and hsa-mir-182; while 27 miRNAs were down-regulated including hsa-mir comprising 29a, hsa-mir-100 and hsa-mir-139 and so on. Five miRNAs (hsa-mir-103-1, hsa-mir-18a, hsa-mir-324, hsa-mir-369 and hsa-mir-320b-2) with diagnostic and preventive potential were significantly correlated with survival time.

Conclusions: The crucial molecular targets such as p53 may provide great clinical value in treatment, as well to provide new ideas for esophageal cancer therapy. The target genes of miRNA were found to play key roles in protein phosphorylation, and the functions of the target genes during protein phosphorylation should be further studied to explore novel treatment of EC.

Figure 1

Principal component analysis of the miRNA expression values. Principal component analysis was complicated according to the miRNA expression differences between normal and cancer samples. The horizontal axis shows that the first principal component accounted for 25%, while the vertical axis shows that the second principal component accounted for 11%. The red dots and black spots indicate cancer samples and normal samples, respectively. The figure clearly shows that normal samples gathered on the right, while cancer samples gathered on the left, indicating that there were significant differences between the 2 samples.

Figure 2

Overall survival status changes of patients. The survival curves were drawn according to patient survival time. The vertical axis shows survival time and the horizontal axis shows survival rates, the red crosses on the curve are cut-off values. As seen from the figure, patient viability decreased gradually over time.

Figure 3

Changes in patient survival status in different classifications. (A–G) Represent the relationships between age, gender, T stage, R stage, M stage, N stage, tumor stage, and survival time, respectively. P-value of log rank test is showed in the lower left corner of each figure.

Figure 4

Predictive capabilities of miRNAs under different tumor statuses. The abscissa and the ordinate show different tumor statuses and different miRNAs, respectively. This heat map shows that the capacity of the 140 differentially expressed miRNAs of tumors affected the survival under various statuses. Red represents strong positive correlations between miRNA expression and survival time, while green represents strong negative correlations.

Figure 5

Determination of patient survival risk by Kaplan-Meier analysis based on expression levels of 5 miRNAs. The abscissa shows the survival time, while the ordinate shows the survival rate. Red and blue represent high-risk and low-risk patients, respectively. P-value at the lower left corner indicates differences between the 2 risk groups.

Figure 6

Analysis of the risk coefficients predicted by the 5 miRNAs. (A) Risk coefficients analysis of training set data; (B) Heat map for expression levels of 5 miRNAs in training set; (C, D) Relationship between patient survival time and risk coefficients; (E, F) Distribution of risk coefficients.

Figure 7

GO and KEGG analysis of the target genes of differentially expressed miRNAs. (A) GO analysis of the target gene; (B) KEGG analysis of the target gene. The abscissa represents degree of significance, while the vertical axis represents functional annotation; the greater the degree of significance, the greater the correlation between the target gene and annotation.