AUTHOR=Zhang Xiaobin , He Yi , Gu Haiyong , Liu Zhichao , Li Bin , Yang Yang , Hao Jie , Hua Rong 

TITLE=Construction of a Nine-MicroRNA-Based Signature to Predict the Overall Survival of Esophageal Cancer Patients

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.670405

DOI=10.3389/fgene.2021.670405

ISSN=1664-8021

ABSTRACT=Background: Esophageal cancer (EC) is a common malignant tumor. MiRNAs had a key role in the occurrence and metastasis, and are closely related the prognosis of esophageal cancer. Therefore, it will provide a powerful tool to predict OS of EC patients based on miRNAs expression in EC tissues and blood samples.
Methods: Five independent databases, TCGA, GSE106817, GSE113486, GSE122497 and GSE112264, were used to construct 9-miRna signature and nomograms for prognosis. The bioinformatics Analysis were used to predict the enrichment pathways of targets.
Results: A total of 132 overexpressed miRNAs and 23 suppressed miRNAs showed significant differential expression in both EC serum and tissue samples compared to normal samples. We also showed that 9 miRNAs were related to the prognosis of EC. Higher levels of miR-15a-5p, miR-92a-3p, miR-92a-1-5p, miR-590-5p, miR-324-5p, miR-25-3p, miR-181b-5p, miR-421, and miR-93-5p were correlated to the shorter survival time in patients with EC. In addition, we constructed a risk prediction model based on the levels of 9 DEMs and found the overall survival time of EC patients with high-risk score is shorter than that of EC patients with low-risk score. In addition, our results showed that the risk prediction scores of EC samples were higher than those of normal samples. Finally, the area under the curve was used to analyze the risk characteristics of EC and normal controls. By calculating the area under the curve and the calibration curve, the RNA signature shows a good performance. Bioinformatics analysis showed 9 DEMs are associated with several crucial signaling, including p53, FoxO, PI3K-Akt, HIF-1 and TORC1 signaling. Finally, 14 mRNAs were identified as hub targets of 9 miRNAs, including BTRC, SIAH1, RNF138, CDC27, NEDD4L, MKRN1, RLIM, FBXO11, RNF34, MYLIP, FBXW7, RNF4, UBE3C, RNF111. TCGA dataset validation showed these hub targets were significantly differently expressed in EC tissues compared to normal samples.
Conclusion: We have constructed maps and nomograms of 9-miRna risk signals associated with EC prognosis. Bioinformatics analysis revealed 9 DEMs are associated with several crucial signaling, including p53, FoxO, PI3K-Akt, HIF-1 and TORC1 signaling in EC. We thought this study will provide clinicians with an effective decision-making tool.