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中华肺部疾病杂志(电子版)

中华肺部疾病杂志(电子版) ›› 2023, Vol. 16 ›› Issue (06) : 761 -765. doi: 10.3877/cma.j.issn.1674-6902.2023.06.003

论著

基于转录组数据分析识别脓毒症肺炎免疫表型
张宪超, 张实†()   
  1. 250013　济南，山东第一医科大学附属中心医院病理科
    250013　济南，山东第一医科大学附属中心医院呼吸与危重症学科；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
  • 收稿日期:2023-10-13 出版日期:2023-12-25
  • 通信作者: 张实
  • 基金资助:
    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)

Identification of immunophenotypes in sepsis pneumonia based on transcriptome data analysis

Xianchao Zhang, Shi Zhang†()   

  1. Department of Pathology, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013 China
    Department of Pulmonary and Critical Care Medicine, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013 China; Department of Pulmonary and Critical Care Medicine, Xinqiao Hospital Affiliated to Army Medical University, Chongqing, 400037 China
  • Received:2023-10-13 Published:2023-12-25
  • Corresponding author: Shi Zhang
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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(电子版), 2023, 16(06): 761-765.

Xianchao Zhang, Shi Zhang. Identification of immunophenotypes in sepsis pneumonia based on transcriptome data analysis[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2023, 16(06): 761-765.

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。

关键词: 脓毒症肺炎, 免疫表型, 精准治疗
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.

Key words: Septic pneumonia, Immunophenotype, Precise treatment
图1 筛选与肺炎介导脓毒症预后有关的分子。注：BIRC5：包含杆状病毒IAP重复序列蛋白5；CD1D：白细胞分化抗原1D；CHIT1：几丁质酶1；CMTM3：趋化素样因子超家族成员3；CTSS：组织蛋白酶S；CX3CR1：人CX3C趋化因子受体1；FCGR3A编码Fcγ受体Ⅲa；FCGR3B：编码Fcγ受体Ⅲβ；GPI：葡萄糖磷酸异构酶；HBEGF：肝素结合性表皮生长因子；HLA-B：人类白细胞相关抗原β；IKBKB：核因子κB激酶亚基β抑制因子；IL16：白介素16；IL1B：白介素1β；IL1RL1：白介素1受体样1；IL27RA：白介素27受体α；IL6R：白介素6受体；ITGAL：整合素αL；JAK1：JAK激酶1；LTB：大肠杆菌不耐热性肠毒素；PIK3CD：磷脂酰肌醇－3激酶催化亚基δ；PLXNC1：神经丛蛋白C1；PRKCB：蛋白激酶Cβ；PRTN3：白细胞蛋白酶3；PTX3：正五聚蛋白3；SDC4：多配体蛋白聚糖4；SEMA4D：信号素4D；SOS1：同源物1；TGFA：转化生长因子α；TNFRSF12A：肿瘤坏死因子受体超家族成员12α；TNFRSF25：肿瘤坏死因子受体超家族成员25
图2 脓毒症发热的一致性热图
图3 脓毒症患者生存分析。注：A:脓毒症Cluster B 28 d累积病死率高于Cluster A[HR 3.173 95%CI(2.117, 4.457)](P<0.001)；B: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)
图4 GSVA热图
1
姜　东，冉龙艳，雷传江，等. 脓毒症的前世今生及序贯器官衰竭认知及理念[J/CD]. 中华肺部疾病杂志(电子版), 2023, 16(5): 605-610.
2
Mandell LA, Niederman MS. Aspiration Pneumonia[J]. N Engl J Med, 2019, 380(7): 651-663.
3
Vadász I, Husain-Syed F, Dorfmüller P, et al. Severe organising pneumonia following COVID-19[J]. Thorax, 2021, 76(2): 201-204.
4
de Benedictis FM, Kerem E, Chang AB, et al. Complicated pneumonia in children[J]. Lancet, 2020, 396(10253): 786-798.
5
Cavallazzi R, Furmanek S, Arnold FW, et al. The burden of communityacquired pneumonia requiring admission to ICU in the United States[J]. Chest, 2020, 158: 1008-1016.
6
Laporte L, Hermetet C, Jouan Y, et al. Ten-year trends in intensive care admissions for respiratory infections in the elderly[J]. Ann Intensive Care, 2018, 8: 84.
7
Reinhart K, Daniels R, Kissoon NJ, et al. Recognizing sepsis as a global health priority-A WHO resolution[J]. N Engl J Med, 2017, 377: 414-417.
8
Fleischmann C, Scherag A, Adhikari NK, et al. Assessment of global incidence and mortality of hospital-treated sepsis: current estimates and limitations[J]. Am J Respir Crit Care Med, 2016, 193: 259-272.
9
Davenport EE, Burnham KL, Radhakrishnan J, et al. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study[J]. Lancet Respir Med, 2016, 4: 259-271.
10
Kempker JA, Martin GS. Does sepsis case mix heterogeneity prevent outcome comparisons? [J]. Crit Care Med, 2016, 44: 2288-2289.
11
Li J, Li M, Su L, et al. Alterations of T helper lymphocyte subpopulations in sepsis, severe sepsis, and septic shock: a prospective observational study[J]. Inflammation, 2015, 38: 995-1002.
12
Poll T, Veerdonk FL, Scicluna BP, et al. The immunopathology of sepsis and potential therapeutic targets[J]. Nat Rev Immunuol, 2017, 17: 407-420.
13
Zhang S, Wu Z, Chang W, et al. Classification of Patients With Sepsis According to Immune Cell Characteristics: A Bioinformatic Analysis of Two Cohort Studies[J]. Front Med (Lausanne), 2020, 7: 598652.
14
Zhang S, Liu F, Wu Z, et al. Contribution of m6A subtype classification on heterogeneity of sepsis[J]. Ann Transl Med, 2020, 8(6): 306.
15
Zhang S, Chang W, Xie J, et al. The Efficacy, Safety, and Optimal Regimen of Corticosteroids in Sepsis: A Bayesian Network Meta-Analysis[J]. Crit Care Explor, 2020, 2(4): e0094.
16
Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis[J]. JAMA, 2019, 321: 2003-2017.
17
Sweeney TE, Perumal TM, Henao R, et al. A community approach to mortality prediction in sepsis via gene expression analysis[J]. Nat Commun, 2018, 9: 694.
18
Wu HP, Chung K, Lin CY, et al. Associations of T helper 1, 2, 17 and regulatory T lymphocytes with mortality in severe sepsis[J]. Inflamm Res, 62: 751-763.
19
Delano MJ, Ward PA. Sepsis-induced immune dysfunction: can immune therapies reduce mortality? [J]. J Clin Invest, 2016, 126: 23-31.
20
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation[J]. Cell, 2011, 144: 646-674.
21
Liu B, Zhang T, Li Y, et al. Kernel Probabilistic K-Means Clustering[J]. Sensors (Basel), 2021, 21(5): 1892.
22
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data[J]. BMC Bioinformatics, 2013, 14: 7.
23
van der Poll T, Shankar-Hari M, Wiersinga WJ. The immunology of sepsis[J]. Immunity, 2021, 54(11): 2450-2464.
24
Gotts JE, Matthay MA. Sepsis: pathophysiology and clinical management[J]. BMJ, 2016, 353: i1585.
25
Welte T, Dellinger RP, Ebelt H, et al. Efficacy and safety of trimodulin，a novel polyclonal antibody preparation, in patients with severe community-acquired pneumonia: a randomized, placebo-controlled, double-blind, multicenter, phase Ⅱ trial (CIGMA study)[J]. Intensive Care Med, 2018, 44: 438-448.
26
Aarts M-AW, Hancock JN, Heyland D, et al. Empiric antibiotic therapy for suspected ventilator-associated pneumonia: a systematic review and meta-analysis of randomized trials[J]. Crit Care Med, 2008, 36: 108-117.
27
Meyer NJ, Gattinoni L, Calfee CS. Acute respiratory distress syndrome[J]. Lancet, 2021, 398(10300): 622-637.
28
Zhang S, Chu C, Wu Z, et al. IFIH1 Contributes to M1 Macrophage Polarization in ARDS[J]. Front Immunol, 2021, 11: 580838.
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