中华普通外科学文献(电子版)2024,Vol.18Issue(5) :368-376.DOI:10.3877/cma.j.issn.1674-0793.2024.05.010

胰腺癌双硫死亡相关的lncRNA预后模型的构建及免疫反应研究

Construction of a disulfidptosis-related lncRNA prognostic model for pancreatic cancer and study of immune response

马中正 杨云川 马翔 周迟 丁丁 霍俊一 徐楠 崔培元 周磊
中华普通外科学文献(电子版)2024,Vol.18Issue(5) :368-376.DOI:10.3877/cma.j.issn.1674-0793.2024.05.010

胰腺癌双硫死亡相关的lncRNA预后模型的构建及免疫反应研究

Construction of a disulfidptosis-related lncRNA prognostic model for pancreatic cancer and study of immune response

马中正 1杨云川 2马翔 2周迟 3丁丁 1霍俊一 1徐楠 1崔培元 3周磊3
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作者信息

  • 1. 233000 蚌埠医科大学;233000 蚌埠医科大学第一附属医院普外科
  • 2. 233000 蚌埠医科大学第一附属医院普外科;510632 广州,暨南大学
  • 3. 233000 蚌埠医科大学第一附属医院普外科
  • 折叠

摘要

目的 基于双硫死亡相关lncRNA构建胰腺癌的预后模型,并分析其在胰腺癌预后和肿瘤免疫功能预测中的临床价值.方法 首先从肿瘤基因组图谱中获取有关胰腺癌的转录组数据和临床信息数据,通过分析与双硫死亡相关的基因识别双硫死亡相关lncRNA,再通过单因素Cox分析、LASSO分析以及多因素Cox分析,筛选出与双硫死亡密切相关的lncRNA并构建预后模型,单因素和多因素Cox回归独立预后分析,验证该模型风险评分是否可以独立于其他的临床性状作为独立的预后因子.其次,通过受试者工作特征曲线、C-index指数、生存曲线、列线图和主成分分析对风险模型的准确性和稳定性进行验证.最后,进行基因富集分析、免疫相关功能分析、肿瘤突变负荷分析、免疫相关分析、肿瘤免疫功能障碍和排除分析.结果 通过分析确定了双硫死亡相关lncRNA,并对其进行了筛选分析.构建了一个由5个双硫死亡相关lncRNA(EMSLR、AC068580.2、AC096733.2、AC087501.4和AC069360.1)组成的预后模型.根据对该模型的生存分析,预测1、3、5年总生存期的ROC曲线下面积分别为0.675、0.771、0.773,说明该预后模型对患者的生存期具有可靠的预测能力.单因素及多因素独立预后分析验证了该模型可以独立于其他的临床性状,作为独立预测胰腺癌患者的预后因子,并且该模型在高、低风险组之间的免疫细胞群、免疫功能、肿瘤突变负荷和肿瘤免疫功能障碍和排斥四个方面均有显著差异(P<0.05).结论 本研究成功构建了基于5个双硫死亡相关lncRNA的胰腺癌预后模型,该模型作为独立的预后因素显示出其对胰腺癌预后具有强大预测能力,这些发现有助于更好地了解胰腺癌,并可能对其个性化治疗策略及风险预测产生积极影响.

Abstract

Objective To construct a prognostic model for pancreatic cancer based on disulfidptosis-related lncRNA and to analyze its clinical value in pancreatic cancer patients.Methods Initially,transcriptome data and clinical information pertaining to pancreatic cancer were retrieved from the Cancer Genome Atlas.By analyzing genes related to disulfidptosis,lncRNAs related to disulfidptosis were identified.Through univariate Cox analysis,LASSO analysis,and multivariate Cox analysis,lncRNAs closely related to disulfidptosis were screened and a prognostic model was constructed.Subsequently,univariate and multivariate Cox regression analysis was employed to conduct an independent prognostic analysis of the model,verifying whether its risk score functioned as an independent prognostic factor,regardless of other clinical features.Secondly,the accuracy of the prognostic model was verified using ROC curves,C-index,survival curves,nomogram,and principal component analysis.Additionally,gene enrichment analysis,immune-related functional analysis,tumor mutation burden analysis,immune-related analysis,tumor immune dysfunction analysis,and exclusion analysis were also performed.Results The analysis identified and screened disulfidptosis-related lncRNAs.A prognostic model was then constructed,comprising 5 disulfidptosis-related lncRNAs:EMSLR,AC068580.2,AC096733.2,AC087501.4,and AC069360.1.Based on the survival analysis of this model,the areas under the ROC curves for predicting 1-,3-,and 5-year overall survival were 0.675,0.771,and 0.773,respectively,indicating the reliable predictive capability of this prognostic model for patient survival.Secondly,the model was verified through univariate and multivariate independent prognostic analysis to serve as an independent prognostic factor for predicting the prognosis of pancreatic cancer patients.Notably,significant differences in immune cell populations,immune function,tumor mutation burden,as well as tumor immune dysfunction and exclusion were observed between the high-risk and low-risk groups based on the analysis of this model.Conclusions In this study,a prognostic model for pancreatic cancer is successfully constructed based on 5 disulfidptosis-related lncRNAs.As an independent prognostic factor,this model exhibits strong predictive power for the prognosis of pancreatic cancer.These findings enhance our understanding of pancreatic cancer and potentially have positive impacts on personalized treatment strategies and risk assessment for the disease.

关键词

胰腺肿瘤/RNA,长链非编码/双硫死亡/预后/计算生物学

Key words

Pancreatic neoplasms/RNA,long noncoding/Disulfidptosis/Prognosis/Computational biology bioinformatics analysis

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基金项目

蚌埠医科大学自然科学重点项目(2021byzd109)

出版年

2024
中华普通外科学文献(电子版)
中华医学会

中华普通外科学文献(电子版)

CSTPCD
影响因子:0.668
ISSN:1674-0793
参考文献量1
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