基于免疫相关lncRNA建立胰腺癌预后风险评估模型

摘要/Abstract

摘要：

目的鉴定和筛选胰腺癌中与免疫基因相关的长非编码RNA(lncRNA),并构建胰腺癌预后风险评估模型,探索预后相关因素。方法通过癌症和肿瘤基因组图谱(TCGA)数据库下载177例胰腺癌患者的测序数据和相应的临床病理和随访信息,采用随机数字表法将患者随机分为试验集(n=89)和验证集(n=88)。首先利用Pearson相关系数计算公式鉴定出与免疫基因显著相关的lncRNA,随后在试验集中利用单因素Cox和多因素Cox分析筛选出与预后相关的lncRNA用于构建预后风险评分公式,利用验证集数据对模型进行验证。结果Pearson相关系数计算公式筛选出788个与免疫相关的lncRNA,在试验集中利用单因素和多因素Cox分析鉴定出5个lncRNA(AC006237.1、AC025154.2、RASSF8-AS1、AL122010.1和AC073896.3)用于构建预后风险评分公式。基于预后风险评分公式将试验集患者分为高风险组(n=44)和低风险组(n=45),生存分析发现高风险组的中位生存期(1.09年)与低风险组(4.11年)相比显著缩短(χ²=26.016,P<0.001)。利用上述公式将验证集的患者分为高风险组(n=44)和低风险组(n=44),生存分析发现高风险组患者的中位生存期(1.28年)与低风险组(1.90年)相比差异也具有统计学意义(χ²=4.422,P=0.035)。单因素和多因素分析提示该预后风险评估模型可有效预测胰腺癌患者的预后情况,且可以作为一个独立的预后相关模型(HR=2.618,95%CI为1.285~5.332,P=0.008)。预后风险评估模型较常见的临床病理指标具有较好的预测效率[1年曲线下面积(AUC)=0.687,3年AUC=0.725,5年AUC=0.782],高于年龄、性别、肿瘤组织病理学分级等常见临床指标的预测能力。AC025154.2、AC073896.3、AL122010.1和RASSF8-AS1在不同临床特征胰腺癌患者中的表达差异均具有统计学意义(均P<0.05),可能是胰腺癌潜在的新型诊断和治疗靶点。干扰素α、哺乳动物雷帕霉素靶蛋白复合体1(mTORC1)、MYC相关调控基因、转化生长因子-β(TGF-β)信号通路在高风险组被显著激活,肌生成和胰腺β细胞信号通路在高风险组被显著抑制。上述信号通路可能是该预后风险模型的潜在分子机制。结论基于5个免疫相关lncRNA构建的预后风险评估模型可以有效地预测胰腺癌患者的预后情况,此外上述免疫相关lncRNA可能是胰腺癌诊断和治疗的新型生物标志物。

关键词:胰腺肿瘤,预后,RNA,长链非编码,ROC曲线

Abstract:

ObjectiveTo identify and screen out immune-related long non-coding RNAs (lncRNAs) in pancreatic cancer, and construct a prognostic risk assessment model to predict the prognostic factors of patients with pancreatic cancer.MethodsRNA-Seq data and corresponding clinicopathological and follow-up information of 177 pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The 177 pancreatic cancer samples were randomly divided into discovery cohort (n=89) and validation cohort (n=88) using random number table method. Immune-related lncRNAs were identified by Pearson correlation coefficient analysis. Univariate and multivariate Cox analysis were used to select prognosis-related lncRNAs and construct the risk score formula based on the data of discovery cohort. The conclusion generated from discovery cohort would be verified in the validation cohort.ResultsA total of 788 immune-related lncRNAs were screened out using Pearson correlation coefficient calculation formula, and 5 lncRNAs (AC006237.1, AC025154.2, RASSF8-AS1, AL122010.1 and AC073896.3) were selected by univariate and multivariate Cox analysis to build the risk score formula based on the discovery cohort. Based on the above risk score formula, pancreatic cancer patients from the discovery cohort were divided into high-risk group (n=44) and low-risk group (n=45). Survival analysis indicated that the median survival time of high-risk group (1.09 years) was significantly shorter than that of low-risk group (4.11 years;χ²=26.016,P<0.001). The validation cohort was also divided into high-risk group (n=44) and low-risk group (n=44) based on the above risk score formula. Survival analysis showed that the median survival time of high-risk group (1.28 years) was also significantly shorter than that of low-risk group (1.90 years;χ²=4.422,P=0.035). Besides, univariate and multivariate analyses suggested that the prognostic risk assessment model could effectively predict the prognosis of patients with pancreatic cancer, and could be used as an independent prognostic prediction model (HR=2.618, 95%CI: 1.285-5.332,P=0.008). The predictive efficiency of this model was better than that of common clinicopathological information such as age, gender and tumor histopathological grade [1-year area under curve (AUC)=0.687, 3-year AUC=0.725 and 5-year AUC=0.782]. The expression levels of AC025154.2, AC073896.3, AL122010.1 and RASSF8-AS1 were significantly different in various clinical characteristics of pancreatic cancer (allP<0.05), and might serve as potential new targets for diagnosis and treatment in pancreatic cancer. Interferon α, mammalian target of rapamycin complex 1 (mTORC1), MYC regulatory genes and transforming growth factor-β (TGF-β) signaling pathways were significantly activated, and myogenesis and pancreas β cells signaling pathways were significantly suppressed in the high-risk group, which may explain the underlying molecular mechanisms of the prognostic risk assessment model.ConclusionThe prognostic risk assessment model based on 5 immune-related lncRNAs can effectively predict the prognosis of pancreatic cancer patients. Besides, the above immune-related lncRNAs may serve as new biomarkers in the diagnosis and therapy in pancreatic cancer.

Key words:Pancreatic neoplasms,Prognosis,RNA,long noncoding,ROC curve

陈晓旭, 于洋, 张天雪. 基于免疫相关lncRNA建立胰腺癌预后风险评估模型[J]. 国际肿瘤学杂志, 2020, 47(8): 472-479.

Chen Xiaoxu, Yu Yang, Zhang Tianxue. A prognostic risk assessment model for pancreatic cancer established based on immune-related lncRNAs[J]. Journal of International Oncology, 2020, 47(8): 472-479.

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