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中华普通外科学文献(电子版)

中华普通外科学文献(电子版) ›› 2024, Vol. 18 ›› Issue (05) : 368 -376. doi: 10.3877/cma.j.issn.1674-0793.2024.05.010

论著

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
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,†()   
  1. 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
  • 收稿日期:2024-03-21 出版日期:2024-10-01
  • 通信作者: 周磊
  • 基金资助:
    蚌埠医科大学自然科学重点项目(2021byzd109)

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

Zhongzheng Ma1, Yunchuan Yang2, Xiang Ma2, Chi Zhou3, Ding Ding1, Junyi Huo1, Nan Xu1, Peiyuan Cui3, Lei Zhou3,†()   

  1. 1. Bengbu Medical College, Bengbu 233000, China; Department of General Surgery, the First Affiliated Hospital of Bengbu Medical College, Bengbu 233000, China
    2. Department of General Surgery, the First Affiliated Hospital of Bengbu Medical College, Bengbu 233000, China; Jinan University, Guangzhou 510632, China
    3. Department of General Surgery, the First Affiliated Hospital of Bengbu Medical College, Bengbu 233000, China
  • Received:2024-03-21 Published:2024-10-01
  • Corresponding author: Lei Zhou
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引用本文：

马中正, 杨云川, 马翔, 周迟, 丁丁, 霍俊一, 徐楠, 崔培元, 周磊. 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[J]. 中华普通外科学文献(电子版), 2024, 18(05): 368-376.

Zhongzheng Ma, Yunchuan Yang, Xiang Ma, Chi Zhou, Ding Ding, Junyi Huo, Nan Xu, Peiyuan Cui, Lei Zhou. Construction of a disulfidptosis-related lncRNA prognostic model for pancreatic cancer and study of immune response[J]. Chinese Archives of General Surgery(Electronic Edition), 2024, 18(05): 368-376.

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。

关键词: 胰腺肿瘤, RNA，长链非编码, 双硫死亡, 预后, 计算生物学
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.

Key words: Pancreatic neoplasms, RNA, long noncoding, Disulfidptosis, Prognosis, Computational biology bioinformatics analysis
图1 本研究的流程图
表1 178例胰腺癌患者的临床特征[例(%)]
项目 总体 对照组 实验组 χ2值 P值
例数 178 89 89    
年龄(岁)       0.090 0.794
≤65 94(52.81) 48(53.93) 46(51.69)    
>65 84(47.19) 41(46.07) 43(48.31)    
性别       0.091 0.763
女 80(44.94) 39(43.82) 41(46.07)    
男 98(55.06) 50(56.18) 48(53.93)    
分级       6.791 0.147
G1 31(17.42) 11(12.36) 20(22.47)    
G2 95(53.37) 46(51.69) 49(55.06)    
G3 48(26.97) 29(32.58) 19(21.35)    
G4 2(1.12) 1(1.12) 1(1.12)    
未明确 2(1.12) 2(2.25) 0(0)    
分期       4.884 0.299
Ⅰ 21(11.8) 10(11.24) 11(12.36)    
Ⅱ 147(82.58) 71(79.78) 76(85.39)    
Ⅲ 3(1.69) 2(2.25) 1(1.12)    
Ⅳ 4(2.25) 4(4.49) 0(0)    
未明确 3(1.69) 2(2.25) 1(1.12)    
T分期       3.594 0.464
T1 7(3.93) 4(4.49) 3(3.37)    
T2 24(13.48) 14(15.73) 10(11.24)    
T3 142(79.78) 67(75.28) 75(84.27)    
T4 3(1.69) 2(2.25) 1(1.12)    
未明确 2(1.12) 2(2.25) 0(0)    
M分期       4.093 0.129
M0 80(44.94) 39(43.82) 41(46.07)    
M1 4(2.25) 4(4.49) 0(0)    
未明确 94(52.81) 46(51.69) 48(53.93)    
N分期       0.839 0.657
N0 49(27.53) 22(24.72) 27(30.34)    
N1 124(69.66) 64(71.91) 60(67.42)    
未明确 5(2.81) 3(3.37) 2(2.25)    
图2 鉴定胰腺癌的双硫死亡相关lncRNA预后模型　A为双硫死亡相关lncRNA的桑基图；B为森林图，显示了通过单因素Cox回归分析得出的预后相关双硫死亡相关lncRNA，红色代表高风险lncRNA，绿色代表低风险lncRNA；C为双硫死亡相关lncRNA的LASSO回归系数；D为LASSO回归模型，获得9个具有最小λ值的预后lncRNA；E为相关性热图，显示了模型中双硫死亡相关基因和双硫死亡相关lncRNA之间的关系
图3 在（A-D）实验组、（E-H）对照组和（I-M）所有患者中，对风险模型的预后价值进行Kaplan-Meier生存分析　A、E和I显示患者随着风险评分的增加而分布；B、F和J显示患者的生存时间和风险评分；C、G和K显示5个双硫死亡相关lncRNA的热图；D、H和L为高风险组和低风险组胰腺癌患者的总生存期的生存分析；M为所有患者中胰腺癌患者的无进展生存期的Kaplan-Meier生存分析。蓝色代表生存人数，红色代表死亡人数
图4 胰腺癌风险模型的预后价值　A、B分别为胰腺癌总生存期的单因素、多因素Cox回归分析；C为不同时间点风险模型的ROC曲线；D为预测5年总体生存期的ROC曲线；E为风险模型和临床变量的C-index曲线
图5 构建和验证了列线图和主成分分析　A为基于风险模型和临床变量，构建了预测胰腺癌患者1、2、3年总生存期的列线图；B为列线图的校准曲线；C为所有基因的主成分分析；D为双硫死亡相关基因的主成分分析；E为双硫死亡相关lncRNA的主成分分析；F为风险lncRNA的主成分分析
图6 胰腺癌患者中风险模型与肿瘤微环境之间的相关性　A为高风险组与低风险组之间免疫评分、基质细胞评分和肿瘤纯度（ESTIMATE）评分的比较；B为两组风险组免疫功能基因的箱线图比较；C为胰腺癌患者不同风险组22种免疫细胞丰度的热图；D为高风险组与低风险组之间22种免疫细胞的丰度差异
图7 肿瘤突变负荷和肿瘤免疫功能障碍与排斥分析　A、B分别为瀑布图，显示了高风险组、低风险组中15个基因的肿瘤突变负荷；C显示高风险组与低风险组之间的肿瘤突变负荷；D为低风险组和高风险组的肿瘤免疫功能障碍与排斥分析；E显示肿瘤突变负荷与存活概率之间的相关性；F为根据肿瘤突变负荷和风险模型划分的患者Kaplan-Meier生存曲线
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