基于坏死性凋亡基因鉴定肝癌亚型并构建预后模型
Identification of subtypes of liver cancer and construction of prognostic model based on necrosis-related genes
毛雅珍 1陈虹全 2陈勇 1齐元麟2
作者信息
- 1. 福州市第一总医院 检验科(福建 福州 350009)
- 2. 福建医科大学基础医学院生物化学与分子生物学系(福建 福州 350122)
- 折叠
摘要
目的:构建肝癌坏死性凋亡相关基因(NEGs)的预后风险模型并验证.方法:通过无监督聚类分析TCGA和ICGC数据库的肝癌患者的67个NEGs分群,并探讨不同集群间预后的差异.通过单因素Cox分析筛选出预后相关的基因,使用聚类和多因素Cox回归构建预后模型,并验证模型准确性和预测能力.结果:将67个N EGs分成2个亚型,包括NEGclusterA和NEGclusterB,生存分析显示N EGclusterB的预后优于NEGclusterA(P＜0.05).单因素Cox分析得到133个预后相关基因,并分为genecluster A和gene-clusterB 2 个亚型,genecluster A 的预后优于 genecluster B(P＜0.001).确定 3 个基因(SLC1A5、MYBL2和CFHR3)构建预后风险评分模型,在TCGA训练集和验证集中,高风险组患者的预后均较差(P＜0.05).结论:该预测模型可以独立预测肝癌的预后,并初步揭示不同肝癌集群间免疫细胞浸润的差异.
Abstract
Objective:To construct and verify a prognostic model based on Necroptosis genes(NEGs)in liver cancer.Methods:Through unsupervised clustering analysis in liver cancer patients from TCGA and ICGC databases,67 NEGs were grouped into two clusters.The differ-ences in prognosis between clusters were explored.Prognosis-related genes were selected through single-factor Cox regression analysis.A prognostic model was built using clustering analy-sis and multi-factor Cox regression,and the model's accuracy and predictive ability were validated.Results:The 67 NEGs were divided into two subtypes,namely NEGclusterA and NEGclusterB.Survival analysis indicated a better prognosis for patients in B compared to A(P＜0.05).Single-factor Cox analysis identified 133 prognosis-related genes,further classified into genecluster A and genecluster B,the prognosis of A was better than B(P＜0.001).Three genes(SLC1A5,MYBL2,and CFHR3)were determined to construct the prognostic risk scoring model.In both TCGA training and validation cohorts,patients in the high-risk group exhibited poorer prognosis(P＜0.05).Conclu-sion:This predictive model can independently forecast the prognosis of liver cancer and provides initial insights into the differences in immune cell infiltration among different liver cancer clusters.
关键词
坏死性凋亡/肝肿瘤/预测模型/TCGAKey words
Necrosis/Liver cancer/Prediction model/TCGA引用本文复制引用
基金项目
福州市科技计划项目(2021-S-173)
国家自然科学基金资助项目(81773055)
福建省自然科学基金项目(2018J01829)
出版年
2024
NETL
NSTL
万方数据