基于内质网应激相关基因构建胰腺癌预后预测模型

吕胜男; 彭新宇; 张健; 刘欢; 魏锋

doi:10.3969/j.issn.1001-5256.2023.12.021

基于内质网应激相关基因构建胰腺癌预后预测模型

DOI: 10.3969/j.issn.1001-5256.2023.12.021

吉林大学第一医院普通外科中心肝胆胰腺外科，长春 130021

基金项目:

国家自然科学基金 (81972273);

国家自然科学基金 (82273276)

伦理学声明：本研究方案于2022年2月28日经由吉林大学第一医院伦理委员会审批，批号：2022-017。

利益冲突声明：本文不存在任何利益冲突。

作者贡献声明：吕胜男负责课题设计，数据收集与分析，实验设计及操作，撰写论文；彭新宇负责数据分析及撰写代码；张健负责修改文章；刘欢负责组织样本收集及处理；魏锋负责拟定研究思路，指导文章撰写并最后定稿。

详细信息

通信作者:
魏锋， wei_feng@jlu.edu.cn （ORCID： 0000-0001-8451-4516）

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出版历程
- 收稿日期: 2023-09-10
- 录用日期: 2023-10-09
- 出版日期: 2023-12-12

Establishment of a prognostic prediction model for pancreatic cancer based on endoplasmic reticulum stress-related genes

Department of Hepatobiliary and Pancreatic Surgery，General Surgery Center，The First Hospital of Jilin University，Changchun 130021，China

Research funding:

National Natural Science Foundation of China (81972273);

National Natural Science Foundation of China (82273276)

More Information

Corresponding author: FENG Wei， wei_feng@jlu.edu.cn （ORCID： 0000-0001-8451-4516）

摘要

摘要: 目的阐明内质网应激基因在胰腺癌预后中的作用，寻找胰腺癌的预后标志物并构建预后预测模型。方法从TCGA和GTEx数据库下载转录组测序数据，通过MsigDB网站获取内质网应激基因，通过单因素Cox回归分析，获取预后相关基因，利用共识聚类分析方法构建胰腺癌分子分型，获取两亚群间的差异表达基因，通过Lasso回归分析，获得胰腺癌预后相关的核心基因，用于构建胰腺癌的预后预测模型，通过GEO数据库获取数据集对模型的预测性能进行验证。利用CIBERSORT分析风险评分与免疫浸润的相关性。实时荧光定量PCR检测基因在胰腺癌组织和细胞系中的表达情况。计量资料两组间比较采用成组t检验。计数资料两组间比较采用χ²检验。利用Log-rank检验进行生存期的比较。通过评估ROC曲线下的面积以评价模型的预测价值。结果内质网应激基因CEBPB、MARCKS、PMAIP1、UBXN10是胰腺癌预后的独立危险因素，基于它们的表达特征能够将TCGA胰腺癌队列分为两个亚群，簇A和簇B，簇A患者的总体生存期比簇B更短（χ²=12.232，P=0.01）。利用Lasso回归从簇A和簇B间差异表达的影响胰腺癌预后的基因中筛选出5个核心基因，获得的风险评分为：0.156×CDA+0.135×AHNAK2+0.020×RHOV+0.095×LY6D+0.054×SPRR1B，ROC曲线反映该模型的总体预测性能良好，1、3、5年的ROC曲线下面积分别为0.731、0.712、0.686，并且依据此模型划分的低风险组的总体生存期要长于高风险组（χ²=11.733，P=0.001）。该模型在外部数据集GSE57495中展现出了良好的预测性能。实时荧光定量PCR结果表明，与癌旁正常组织相比，CDA、AHNAK2、RHOV、LY6D、SPRR1B在40对胰腺癌组织中显著上调（t值分别为2.529、2.458、3.314、3.583、5.082，P值均<0.05）。结论根据CDA、AHNAK2、RHOV、LY6D、SPRR1B的表达特征可以预测胰腺癌预后，该组基因的整体高表达与胰腺癌患者的不良预后相关。
- 胰腺肿瘤 /
- 内质网应激 /
- 预后 /
- 危险因素
Abstract: Objective To investigate the role of endoplasmic reticulum stress genes in the prognosis of pancreatic cancer， and to establish a prognostic prediction model based on the prognostic markers for pancreatic cancer. Methods Transcriptome sequencing data were downloaded from TCGA and GTEx databases， and MsigDB website was used to obtain endoplasmic reticulum stress genes. A univariate Cox regression analysis was performed to obtain the genes associated with the prognosis of pancreatic cancer， and a consensus clustering analysis was used to construct the molecular typing of pancreatic cancer， while the differentially expressed genes between the two subgroups were obtained. A Lasso regression analysis was used to obtain the core genes associated with the prognosis of pancreatic cancer， which were used to construct a prognostic prediction model for pancreatic cancer. Related datasets were obtained from the GEO database to validate the predictive performance of the model. The CIBERSORT analysis was used to investigate the correlation between risk score and immune infiltration. Quantitative real-time PCR was used to measure the expression of genes in pancreatic cancer tissue and cell lines. The independent-samples t test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between groups. Survival was compared using Log-rank test. The predictive value of the model was evaluated by evaluating the area under the ROC curve. Results The endoplasmic reticulum stress genes CEBPB， MARCKS， PMAIP1， and UBXN10 were independent risk factors for the prognosis of pancreatic cancer， and based on the expression characteristics of these genes， the TCGA pancreatic cancer cohort was divided into two subgroups， i.e.， cluster A and cluster B， while the cluster A patients had a significantly shorter overall survival time than the cluster B patients （P<0.01）. The Lasso regression analysis obtained 5 core genes from the differentially expressed genes affecting the prognosis of pancreatic cancer， and the risk scoring system was established as risk score=0.156×CDA+0.135×AHNAK2+0.020×RHOV+0.095×LY6D+0.054×SPRR1B. The ROC curve analysis showed that this model had good overall predictive performance， with the area under the ROC curve of 0.731 at 1 year， 0.712 at 3 years， and 0.686 at 5 years， and the low-risk group based on this model had a significantly longer overall survival time than the high-risk group （χ²=11.733， P=0.001）. The model showed good predictive performance in the external dataset GSE57495. Quantitative real-time PCR results showed that the expression levels of CDA， AHNAK2， RHOV， LY6D， and SPRR1B in 40 pancreatic cancer tissue samples were significantly upregulated compared with those in normal adjacent tissue samples （t=2.529， 2.458， 3.314， 3.583， and 5.082， all P<0.05）. Conclusion The expression characteristics of CDA， AHNAK2， RHOV， LY6D， and SPRR1B can be used to predict the prognosis of pancreatic cancer， and the high expression levels of these genes are associated with the poor prognosis of pancreatic cancer patients.
- Pancreatic Neoplasms /
- Endoplasmic Reticulum Stress /
- Prognosis /
- Risk Factors

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图 1 胰腺癌预后相关内质网应激基因森林图

Figure 1. Forest map of ER stress genes associated with prognosis in pancreatic cancer

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图 2 依据胰腺癌内质网应激基因划分的两个亚群的聚类热图及生存曲线

注： a，共识聚类分析将胰腺癌分为两个亚群； b，两个亚群的Kaplan-Meier生存曲线。

Figure 2. Cluster heat map and survival curve of two clusters of pancreatic cancer based on ER stress genes

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图 3 Lasso回归结果及模型预测效果

注： a，Lasso回归交叉验证曲线；b，Lasso回归系数路径图；c，预测模型ROC曲线；d，预测模型高低风险组的Kaplan-Meier生存曲线。

Figure 3. The results of Lasso regression and the effectiveness of this prediction model

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图 4 模型对外部数据集生存期的预测结果

注： a，对GSE57495进行预测的ROC曲线； b，GSE57495高低风险组的生存曲线。

Figure 4. Survival prediction of external datasets using this model

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图 5 CDA、AHNAK2、RHOV、LY6D、SPRR1B在肿瘤组织和正常组织的表达量

Figure 5. The expression levels of CDA， AHNAK2， RHOV， LY6D， and SPRR1B in the tumor and normal tissues

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图附录A、B见二维码

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表 1 引物序列

Table 1. Primer sequences

引物名称	引物序列（5'-3'）
CDA	上游引物：GCCAAGAAGTCAGCCTACTGCC 下游引物：CTTCTGGATAGCGGTCCGTTCA
AHNAK2	上游引物：TCCTGGTGGAAGCGAGATTCAG 下游引物：ACCACCTGTGACACTGTAGCCA
RHOV	上游引物：ACTGCGCTGGACACCTTCTCTG 下游引物：CACGCCAGGAAGACATCGGTAT
LY6D	上游引物：AGCTCTCGCTTCTGCAAGACCA 下游引物：TCTCATTGCACAGGTCCTCCTG
SPRR1B	上游引物：CTGCCCTTCAATAGTCACTCCAG 下游引物：CTCATACGCAGAATGGGATAGGG

下载: 导出CSV

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