肝细胞癌中泛凋亡相关长链非编码RNA预后风险模型的构建与评估
Construction and evaluation of a prognostic risk model for PANoptosis-related long non-coding RNA in hepatocellular carcinoma
庹云 1郝定盈 1裴捷 2毛本亮 1庄润宇 2吴帆 2王百林3
作者信息
- 1. 贵州医科大学临床医学院,贵阳 550004
- 2. 暨南大学附属广州红十字会医院普通外科,广州 510220
- 3. 贵州医科大学临床医学院,贵阳 550004;暨南大学附属广州红十字会医院普通外科,广州 510220
- 折叠
摘要
目的 利用生物信息学对癌症基因组图谱(TCGA)数据库进行挖掘并筛选对肝细胞癌(HCC)患者预后有显著影响的泛凋亡(PANoptosis)相关长链非编码RNA(lncRNA),构建HCC中PANoptosis相关lncRNA(PANRlncRNA)的预后风险模型.方法 在TCGA数据库下载377例HCC患者临床数据,在多种数据库中检索关键词"PANoptosis"并从文献中获取泛凋亡相关基因(PANR-Gs),通过对PANR-Gs和lncRNAs进行共表达分析确定PANRlncRNAs,在癌组织及癌旁组织中进行差异性分析.对377例患者的临床数据预处理后筛选到符合标准的343例样本(全集),将全集随机分组为训练集(172例)和测试集(171例),通过单因素Cox、LASSO、多因素Cox风险回归分析建立训练集的预后风险模型,得到每个患者的风险评分,按风险评分的中位值将其分为高、低风险组,采用Kaplan-Meier方法对两组患者进行生存分析,绘制受试者工作特征(ROC)曲线对模型进行效能评估.分析高、低风险组的生存时间和免疫功能,通过Wilcoxon检验完成两组间比较.结果 共筛选到147个PANRlncRNAs,108个差异表达的PANRlncRNAs,最终确定4个PANRlncRNAs构成HCC预后风险模型,该模型在训练集患者的1年、3年和5年生存预测性能曲线下面积(AUC)值分别为0.740、0.774和0.791;高风险组患者的总生存期明显低于低风险组(P＜0.05),测试集和全集中也呈现出良好的预测效能,进一步分析表明在高、低风险组中,免疫细胞及其功能差异有统计学意义(P＜0.05).结论 基于4个PANRlncRNAs建立预后风险模型有良好的预测能力,可有效预测HCC患者的生存预后,其风险评分可能成为HCC独立的预后因素.
Abstract
Objective Using bioinformatics,this study aimed to explore and identify long non-coding RNAs(lncRNAs)related to PANoptosis with significant prognostic impact on hepatocellular carcino-ma(HCC)patients by mining the cancer genome atlas(TCGA)database.Subsequently,a prognostic risk model for PANoptosis-related long non-coding RNAs(PANRlncRNAs)in HCC was constructed.Methods Clinical data of 377 HCC patients were downloaded from the TCGA database.Keyword"PANoptosis"was searched in various databases,and relevant genes were extracted from the literature.Co-expression analysis of PANoptosis-related genes and lncRNAs was performed to determine PANRlncRNAs.Differential analysis was conducted in cancer and adjacent tissues.After preprocessing clinical data for 377 patients,343 sam-ples meeting the criteria(entire dataset)were selected.The entire dataset was randomly divided into a training set(172 cases)and a test set(171 cases).A prognostic risk model for the training set was estab-lished through univariate Cox,LASSO,and multivariate Cox regression analyses.Each patient's risk score was obtained,and they were classified into high or low-risk groups based on the median risk score.Kaplan-Meier analysis was used for survival analysis,and the model's performance was assessed using the receiver operating characteristic(ROC)curve.Analysis of survival time and immune function in high and low-risk groups was conducted,with group comparisons performed using the Wilcoxon test.Results A total of 147 PANRlncRNAs were screened,of which 108 PANRlncRNAs were differentially expressed,and 4 PANRln-cRNAs were identified to constitute a prognostic risk model for HCC.The area under curve(AUC)values for the model's performance in predicting 1-,3-,and 5-year survival in patients from the training set were 0.740,0.774,and 0.791,respectively.The overall survival of high-risk group patients was significantly lower than that of the low-risk group(P＜0.05).The model also exhibited good predictive efficacy in the test set and the entire set.Further analysis revealed differences in immune cells and their functions between high and low-risk groups.Conclusion The prognostic risk model based on 4 PANRlncRNAs exhibits ex-cellent predictive capability and can effectively assess the survival prognosis of HCC patients,and its risk score has the potential to be an independent prognostic factor for HCC.
关键词
肝细胞癌/泛凋亡/长链非编码RNA/风险模型/免疫浸润Key words
Hepatocellular carcinoma/PANoptosis/Long non-coding RNA/Risk model/Immune infiltration引用本文复制引用
基金项目
国家自然科学基金(81974442)
广东省自然科学基金(2020A1515010799)
出版年
2024
NETL
NSTL
万方数据