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[1]张宪超,张 实.基于转录组数据分析识别脓毒症肺炎免疫表型[J].中华肺部疾病杂志,2023,(06):761-765.[doi:10.3877/cma.j.issn.1674-6902.2023.06.003]
　Zhang Xianchao,Zhang Shi..Identification of immunophenotypes in sepsis pneumonia based on transcriptome data analysis[J].,2023,(06):761-765.[doi:10.3877/cma.j.issn.1674-6902.2023.06.003]
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基于转录组数据分析识别脓毒症肺炎免疫表型(PDF)

《中华肺部疾病杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年06期
页码:
761-765
栏目:
论著
出版日期:
2023-12-20

文章信息/Info

Title:
Identification of immunophenotypes in sepsis pneumonia based on transcriptome data analysis
作者:
张宪超1; 张 实2; 3
250013 济南,山东第一医科大学附属中心医院病理科1、呼吸与危重症学科2
400037 重庆,陆军(第三)军医大学第二附属医院呼吸与危重症学科3
Author(s):
Zhang Xianchao1;  Zhang Shi2; 3.
1Department of Pathology, 2Department of Pulmonary and Critical Care Medicine, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013 China; 3Department of Pulmonary and Critical Care Medicine, Xinqiao Hospital Affiliated to Army Medical University, Chongqing, 400037 China
关键词:
脓毒症肺炎;  免疫表型;  精准治疗
Keywords:
Septic pneumonia;  Immunophenotype;  Precise treatment
分类号:
R563
DOI:
10.3877/cma.j.issn.1674-6902.2023.06.003
摘要:
目的 基于转录组数据分析识别不同脓毒症肺炎(septic pneumonia)免疫表型。方法 选择基因表达综合数据库(Gene Expression Omnibus, GEO)公共数据库脓毒症肺炎外周血转录组芯片数据二次分析。采用单因素COX回归分析筛选与脓毒症肺炎预后相关免疫分子。免疫预后分子采用无监督聚类识别免疫表型。采用基因集变异分析(gene set variation analysis, GSVA)算法评价免疫表型特点。结果 筛选脓毒症479例,其中脓毒症肺炎183例。257个免疫分子表达与脓毒症28 d累积病死率相关(P<0.05); 87个免疫分子表达与肺炎介导脓毒症28 d累计病死率相关(P<0.05),其中显著相关免疫分子31个(P<0.01)。识别免疫表型Cluster A 脓毒症286例,脓毒症肺炎117例和Cluster B 脓毒症193例,脓毒症肺炎66例。脓毒症Cluster B 28 d天累积病死率高于Cluster A,[HR 3.173 95%CI(2.117, 4.457)](P<0.001)。脓毒症肺炎Cluster B 28 d累积病死率高于Cluster A,[HR 3.523 95% CI(1.699, 7.035)](P<0.001)。GSVA分析显示 Cluster A为免疫活化表型; Cluster B为免疫抑制表型。免疫抑制表型病死率高于免疫活化表型。结论 转录组数据二次分析识别脓毒症肺炎免疫活化表型Cluster A和免疫抑制表型Cluster B,为精准治疗提供依据。
Abstract:
Objective To identify immunophenotypes in sepsis pneumonia based on transcriptome data analysis. Methods Datasets from observational cohort studies in GEO public database that included consecutive sepsis patients admitted to intensive care units were downloaded. We analyzed genome-wide gene expression profiles in blood from sepsis patients by using machine learning and bioinformatics. Results A total of 479 sepsis patients, including 183 with septic pneumonia, were enrolled. The 28-day cumulative mortality of sepsis patients was linked with the expression of 257 immunological molecules(P<0.05). Patients with pneumonia-mediated sepsis had a 28-day cumulative death rate that was connected with the expression of 87 immune molecules(P<0.05), of which 31 immune molecules had a substantially higher correlation(P<0.01). Cluster A(286 instances of sepsis, 117 cases of septic pneumonia)and Cluster B, two immunophenotypes, were found(193 cases of sepsis, 66 cases of septic pneumonia). Sepsis patients in the Cluster B group had a higher 28-day cumulative death rate than those in the Cluster A group [HR 3.173 95%CI(2.117, 4.457), P<0.001]. The 28-day cumulative mortality of Cluster B in septic pneumonia was higher than that of Cluster A [HR 3.523 95%CI(1.699, 7.035)〗, P<0.001]. Conclusion The present study developed a comprehensive tool to identify the immunoparalysis endotype and immunocompetent status in hospitalized patients with sepsis and provides novel clues for further targeting of therapeutic approaches.

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金青年项目(82202413); 山东省自然科学基金青年项目(ZR2022QH332); 济南市科技局临床医学科技创新计划(202134058); 济南市中心医院引进人才科研启动经费(YJRC2021010)
通信作者: 张 实, Email: 394873967@qq.com
更新日期/Last Update: 2023-12-20