[1]刘 基,孙振纲,邓 岩,等.炎症对胰腺癌患者预后的评估及免疫状态的影响[J].医学信息,2023,36(05):13-21.[doi:10.3969/j.issn.1006-1959.2023.05.003]
　LIU Ji,SUN Zhen-gang,DENG Yan,et al.Effects of Inflammation on Prognosis and Immune Status in Patients with Pancreatic Adenocarcinoma[J].Journal of Medical Information,2023,36(05):13-21.[doi:10.3969/j.issn.1006-1959.2023.05.003]
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炎症对胰腺癌患者预后的评估及免疫状态的影响()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
36卷
期数:
2023年05期
页码:
13-21
栏目:
生物信息学
出版日期:
2023-03-01

文章信息/Info

Title:
Effects of Inflammation on Prognosis and Immune Status in Patients with Pancreatic Adenocarcinoma
文章编号:
1006-1959（2023）05-0013-09
作者:
刘 基; 孙振纲; 邓 岩; 等
（1.长江大学医学部，湖北 荆州 434000；2.长江大学附属荆州医院，湖北 荆州 434000）
Author(s):
LIU Ji; SUN Zhen-gang; DENG Yan; et al.
(1.Medical Department of Yangtze University,Jingzhou 434000,Hubei,China;2.Jingzhou Hospital,Yangtze University,Jingzhou 434000,Hubei,China)
关键词:
胰腺癌; 生物学分析; 差异表达基因
Keywords:
Pancreatic adenocarcinoma; Biological analysis; Differentially expressed genes
分类号:
R735.9
DOI:
10.3969/j.issn.1006-1959.2023.05.003
文献标志码:
A
摘要:
目的 研究炎症相关基因对胰腺癌（PAAD）患者预后及免疫状态的影响。方法 从TCGA公共数据库下载PAAD患者mRNA表达谱及其临床数据，从GTEx数据库中下载正常患者mRNA表达谱，使用最小收缩和选择运算子Cox分析在TCGA队列中构建多基因预后模型并在GEO数据库（GSE57495和GSE62452）中进行验证。Kaplan-Meier分析高风险组和低风险组的总生存期（OS），应用单变量和多变量Cox分析确定OS的独立预测因子。另采用GO功能和KEGG途径进行功能富集分析，运行ESTIMATE版本进行肿瘤免疫环境分析，最后绘制整合患者临床信息和风险评分的列线图。结果 共得到29个差异表达基因，除HPN基因在正常组织中表达高于肿瘤组织，其余基因在肿瘤组织表达均高于正常组织（P＜0.05）；通过预后模型的构建，筛选出CXCL9、LY6E、HBEGF、MET、CXCL10、RTP4共6个炎症相关差异表达基因与预后相关；高风险组患者OS低于低风险组（P＜0.001），构建预后模型高、低风险组的ROC曲线面积为0.774；通过单因素和多因素回归分析显示，风险评分是OS的独立预后因子，其中单因素分析风险评分风险比（HR）为3.194，可信区间（CI）为2.077~4.911（P＜0.001），多因素分析风险评分HR为3.302，CI为2.074~5.260（P＜0.001）。GO富集分析显示，差异表达基因主要集中于细胞趋化作用、细胞对脂多糖的反应等；KEGG富集分析显示，差异表达基因主要参与细胞因子受体相互作用、病毒蛋白与细胞因子及细胞因子受体的相互作用生物功能及肿瘤坏死因子信号通路、趋化因子信号通路、Toll样受体信号通路。通过GEO数据库验证结果一致，肿瘤免疫微环境分析显示不同风险组的免疫状态明显不同，低风险组免疫评分、肿瘤评分和免疫及肿瘤微环境综合评分高于高风险组；列线图分析显示，性别、年龄以及风险分数可以预测患者2、3年的总生存率。结论 筛选出与炎症相关的差异表达基因，并构建预后模型，差异表达基因的风险评分可以作为独立预后因子。
Abstract:
Objective To investigate the effect of inflammation-related genes on the prognosis and immune status of patients with pancreatic adenocarcinoma(PAAD). Methods The mRNA expression profile and clinical data of PAAD patients were downloaded from the TCGA public database, and the mRNA expression profile of normal patients was downloaded from the GTEx database. The multi-gene prognostic model was constructed in the TCGA cohort using the minimum contraction and selection operator Cox analysis and verified in the GEO database (GSE57495 and GSE62452). Kaplan-Meier analysis was used to analyze the overall survival (OS) of the high-risk group and the low-risk group. Univariate and multivariate Cox analysis were used to determine the independent predictors of OS. In addition, GO function and KEGG pathway were used for functional enrichment analysis, and ESTIMATE version was used for tumor immune environment analysis. Finally, a nomogram integrating clinical information and risk score of patients was drawn.Results A total of 29 differentially expressed genes were obtained. Except that the expression of HPN gene in normal tissues was higher than that in tumor tissues, the expression of other genes in tumor tissues was higher than that in normal tissues (P<0.05). Through the construction of the prognostic model, CXCL9, LY6E, HBEGF, MET, CXCL10 and RTP4 were screened out as six inflammation-related differentially expressed genes related to prognosis. The OS of patients in the high-risk group was lower than that in the low-risk group (P<0.001), and the ROC curve area of the high-risk and low-risk groups was 0.774. Univariate and multivariate regression analysis showed that risk score was an independent prognostic factor for OS, the risk ratio (HR) of risk score in univariate analysis was 3.194, and the confidence interval (CI) was 2.077-4.911 (P<0.001), while the risk score of multivariate analysis was 3.302, and the CI was 2.074-5.260 (P<0.001). GO enrichment analysis showed that the differentially expressed genes mainly focused on cell chemotaxis and cell response to lipopolysaccharide. KEGG enrichment analysis showed that differentially expressed genes were mainly involved in cytokine receptor interaction, biological function of interaction between viral protein and cytokines and cytokine receptors, tumor necrosis factor signaling pathway, chemokine signaling pathway and Toll-like receptor signaling pathway. The results of GEO database were consistent. The analysis of tumor immune microenvironment showed that the immune status of different risk groups was significantly different. The immune score, tumor score and comprehensive score of immune and tumor microenvironment in low risk group were higher than those in high risk group. Nomogram analysis showed that gender, age and risk score could predict the 2-and 3-year overall survival rate of patients.Conclusion Differentially expressed genes related to inflammation are screened and a prognostic model is constructed. The risk score of differentially expressed genes can be used as an independent prognostic factor.

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更新日期/Last Update: 1900-01-01