1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Identification of a three-miroRNA-based prognostic gene signature in hepatocellular carcinoma using the random survival forest method
Paper ID : 1075-ICB10
Authors:
صدرا صالحی-مازندرانی1, Mohammad Hossein Donyavi2, Parvaneh Nikpour *1
1Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
2Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
Abstract:
Background: Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide. HCC has poor prognosis and monitoring of HCC patients with high risk is necessary [1]. Many studies have revealed miroRNAs (miRNAs) as potential prognostic biomarkers in cancer [2-3]. For this aim, we performed a bioinformatic-based analysis and random survival forest method to identify a prognostic gene signature in HCC.

Materials and Methods: miRNA-seq data and clinical information of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) database using the TCGAbiolinks R package. Differentially-expressed miRNAs (DEMis) among cancerous and paracancerous samples were identified by the DESeq2 package based on |logFC| > 2 and adjusted p-value < 0.01. Univariate survival analysis was performed on the DEMis data of cancerous samples to identify prognosis-related miRNAs (hazard ratio (HR) ≠ 1 and p-value < 0.01) by the survival package. Subsequently, randomForestSRC package was utilized to rank survival-related miRNAs. Then, multivariate cox regression analysis was performed to establish a risk scoring model based on the regression coefficient and gene expression. Survival and time dependent ROC (receiver operating characteristic) curve plots were generated by the survival and survivalROC packages, respectively.

Results: 131 DEMis (126 upregulated and 5 downregulated) were identified between 372 cancerous and 50 paracancerous HCC samples. Univariate survival analysis revealed 8 prognostic-associated miRNAs. Based on the random forest analysis, 3 miRNAs (hsa-miR-9-5p, hsa-miR-137-3p and hsa-miR-105-5p) which had the relative importance > 0.6, were selected to construct a prognostic gene signature. The HR and p-value of the model were 2.6 and 0.00003, respectively. Additionally, AUC of the model for 5, 3, and 1 year were 0.66, 0.61, and 0.64, respectively.

Conclusion: This study provides a reliable gene signature for prediction of prognosis in HCC patients using miRNAs. Further studies are needed to evaluate the strength of this model in HCC.
Keywords:
Carcinoma, Hepatocellular; MicroRNAs; prognostic Biomarker
Status : Paper Accepted (Poster Presentation)