基于缺氧相关长链非编码RNA基因集的食管鳞癌预后模型

A Prognosis Model for Esophageal Squamous Cell Carcinoma Based on Hypoxia-Related Long Non-coding RNAs

目的利用GEO数据库(gene expression omnibus)中食管鳞癌患者的转录组数据和临床数据,构建基于缺氧相关长链非编码RNA(lncRNA)的食管鳞癌预后模型,并分析模型的预测效能,为食管鳞癌的个性化治疗提供依据。方法下载358例食管鳞癌患者的转录组数据和对应的临床资料,利用R语言分析包鉴定肿瘤组织和癌旁正常组织数据间差异表达的基因,并计算缺氧相关基因集与表达差异的lncRNA基因集间的相关性,随后使用单因素Cox回归分析和LASSO(least absolute shrinkage and selection operation)回归分析对相关的基因进行筛选,最后利用多因素Cox回归构建基于基因表达量和回归系数的风险打分模型,并分析模型的预后效能和肿瘤免疫微环境等。结果获得由6个基因组成的风险打分模型,高风险组的生存时间明显低于低风险组(P<0.0001),打分模型1、3、5 a的AUC(area under the curve)值均>0.65,证明模型有较好的预后预测能力;Cox回归分析显示打分模型有独立的预后预测能力;肿瘤免疫微环境分析显示,高低风险组在免疫细胞组成、通路等方面差异显著。结论食管鳞癌6基因风险打分模型能较好地预测患者的预后风险,这些基因可作为食管鳞癌缺氧相关调控通路研究的潜在靶点,免疫微环境可能导致了不同风险组的生存差异。

Objective To analyze the predictive efficacy of a prognosis model of ESCC based on hypoxia-associated lncRNA,and provide basis for personalized treatment of ESCC.Methods Transcriptome data and clinical data of 358 patients with ESCC were downloaded.The R language analysis package was used to identify differentially expressed genes between tumor tissue and normal adjacent tissue,the correlation between hypoxia-related gene sets and differentially expressed lncRNA gene sets was calculated;univariate Cox regression analysis and LASSO regression analysis were used to screen the relevant genes;multivariate Cox regression was used to construct a risk scoring model based on gene expression and regression coefficient;and the prognostic efficacy and tumor immune microenvironment of the model were analyzed.Results 6 optimal lncRNAs were used to constructed the risk scoring model.The survival time of the high-risk group was significantly lower than low-risk group(P<0.0001),the AUC of 1,3 and 5 years were all greater than 0.65,which proves that the model has good prognostic prediction ability.Cox regression analysis showed that the risk scoring model had independent prognostic ability.The analysis of tumor immune microenvironment showed that there were significant differences in immune cell composition,immune activated pathway in high and low risk groups.Conclusion The 6 genes risk scoring model can better predict the prognostic risk of patients with ESCC;these genes may serve as potential targets for the research of hypoxia-related regulatory in ESCC;the immune microenvironment may lead to the survival difference among different risk groups.