Journal of Shandong University (Health Sciences) ›› 2020, Vol. 1 ›› Issue (7): 47-52.doi: 10.6040/j.issn.1671-7554.0.2020.0663

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Construction and application of a miRNAs prognostic risk assessment model of gastric cancer

SHI Shuang1,2, LI Juan1,2, MI Qi1,2, WANG Yunshan1,2, DU Lutao1,2, WANG Chuanxin1,2   

  1. 1. Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, Shandong, China;
    2. Tumor Marker Detection Engineering Laboratory of Shandong Province, Jinan 250033, Shandong, China
  • Online:2020-07-20 Published:2020-07-10

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Abstract: Objective To identify microRNAs(miRNAs)biomarkers related to the prognosis of gastric cancer patients, and construct a miRNA risk assessment model for survival prediction. Methods The miRNA expression profile of gastric cancer patients and relevant clinical data were obtained from the Cancer Genome Atlas(TCGA)database. Differentially expressed miRNAs were identified with “DESeq2” package. The miRNAs related to prognosis were screened with univariate Cox regression and Kaplan-Meier analysis, which were analyzed with multivariate Cox regression to construct a prognostic risk assessment model. A receiver operating characteristic(ROC)curve was drawn with “time ROC” package to evaluate the effectiveness of the model. Finally, the messenger RNAs(mRNAs)that miRNAs might bind to were predicted with online database, and the possible functions were predicted with gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG). Results With Log2|Fold Change|>1 and P<0.05 as the standards, 248 differentially expressed miRNAs in gastric cancer tissues were identified. Univariate Cox regression and Kaplan-Meier analysis screened out 6 differentially expressed miRNAs which had significant correlation with prognosis to construct a prognostic risk assessment model. The risk score=0.048 35×miR-181b-1 +0.112 06×miR-548d-1+0.068 00×miR-675+0.075 87×miR-708+1.175 21×miR-4640+0.089 89×miR-4709. Kaplan-Meier analysis showed patients with high risks had a poor prognosis(P<0.001). The area under the ROC curve(AUC)of the 5-year overall survival rate was 0.776, indicating the model was able to predict the prognostic risk. GO and KEGG analysis showed miRNAs were involved in a few signaling pathways of gastric cancer. Conclusion A miRNAs prognostic risk assessment model was successfully constructed based on bioinformatics analysis, which was proved by Kaplan-Meier analysis and ROC curve to have good prediction effects on the survival of gastric cancer patients.

Key words: Gastric cancer, miRNAs, Prognosis, TCGA database, Cox hazards regression model

CLC Number: 

  • R574
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