肝癌预后miRNA风险评分模型的鉴定和分析

肝细胞癌(hepatocellular carcinoma,HCC)是世界上高发病率和高死亡率的恶性肿瘤之一.研究目的是寻找HCC相关的mi RNA预后生物学标志物,预测HCC患者的风险程度和生存时间,为他们提供有效的预后信息.使用4种方法从TCGA中识别差异表达的mi RNAs(DEMs).并用Kaplan-Meier生存曲线、单因素和多因素Cox回归分析从DEMs中筛选肝癌预后相关的mi RNA.最终4个HCC的预后mi RNA生物学标志物(hsa-mi R-132-3p、hsa-mi R-139-5p、hsa-mi R-3677-3p、hsa-mi R-500a-3p)被筛选出来组合成一个风险评分模型.目前还没有实验证据表明组合中的hsa-mir-3677-3p与HCC相关,是本研究新发现的mi RNA.生存曲线、ROC曲线、卡方检验等多种生物信息学方法的评价结果均表明,该模型计算出的风险分值能有效预测患者的风险程度(P<0.000,风险比=2.551,95%置信区间=1.751-3.717).低风险组HCC患者1-5年生存率比高风险组高20%-30%.通过与临床数据分析发现,组合的生物学标志物较其他临床指标相比具有更好的预后效果,也可以作为独立的预后因子.最后,预测了4种mi RNA的靶基因,包括AGO2、FOXO1、ROCK2、RAP1B、CYLD等,并在细胞增殖、迁移、凋亡、免疫应答等生物学过程中富集.

The Identification and Analysis of a miRNA Risk Score Model for Hepatocellular Carcinoma Prognosis

Hepatocellular carcinoma (HCC) is one of the malignancies with high morbidity and mortality in the world. The purpose of our study was to search for HCC related miRNA prognostic biomarkers to predict the risk degree and survival time of HCC patients and provide effective prognostic information for HCC patients. Four methods were used to identify differentially expressed miRNAs (DEMs) from The Cancer Genome Atlas(TCGA). Kaplan-Meier survival curve, univariate and multivariable Cox regression analysis were used to identify prognostic miRNAs of HCC from DEMs. Four prognostic miRNAs biomarkers (hsa-miR-132-3p, hsa-miR-139-5p, hsa-miR-3677-3p, hsa-miR-500a-3p) of HCC were identified at last, and combined into a risk score model. There was no experimental evidence that hsa-mir-3677-3p is related to HCC, and it was a newly discovered miRNA in this study. The evaluation results of various bioinformatics methods, including survival curve, ROC curve, chi-square test, et al.. All indicated that the risk score calculated by the model can effectively predict the risk degree of patients(P<0.000, hazard ratio=2.551, 95% confidence interval=1.751-3.717). 1-5 year survival rates of HCC patients in the low risk group had 20%-30% higher than in the high risk group. Through the clinical data analysis, it was found that the combined biomarkers have a better prognostic effect than other clinical indicators, and can also be used as an independent prognostic factor. Target genes of four miRNAs were predicted, including AGO2, FOXO1, ROCK2, RAP1B, CYLD, et al., and enriched in biological processes such as cell proliferation, migration, apoptosis and immune response.