背景:脓毒症是一种临床综合征,由于对感染的炎症反应失调。越来越多的证据表明,人类白细胞抗原(HLA)基因在败血症的免疫反应中起关键作用。然而,HLA基因在脓毒症中的作用仍未得到全面了解。方法:从开始到2021年9月10日,在基因表达综合(GEO)和ArrayExpress数据库中进行了系统搜索。进行随机森林(RF)和改良的Lasso惩罚回归来识别多转录组数据中的hub基因,因此我们构建了一个预测模型,即HLA分类器。ArrayExpress数据库,作为外部验证,被用来评估其诊断,预后,和预测性能。通过CIBERSORTx工具和单样品基因集富集分析(ssGSEA)计算免疫细胞浸润评分。进行基因集变异分析(GSVA)和ssGSEA以确定在不同亚组中显著富集的通路。接下来,我们从多个角度系统地将HLA分类器与免疫学特征相关联,如免疫相关的细胞浸润,关键的分子途径,和细胞因子表达。最后,通过实时定量聚合酶链反应(qRT-PCR)验证临床样本中HLA基因的表达水平。结果:共包括9个数据集,包括1,251名患者。基于多转录组数据集中的RF和改进的Lasso惩罚回归,五个HLA基因(B2M,HLA-DQA1,HLA-DPA1,TAP1和TAP2)被鉴定为hub基因,用于构建HLA分类器。在发现队列中,与临床特征或基因型相比,HLA分类器表现出更高的诊断价值(AUC=0.997),并且在预测死亡率方面表现更好(AUC=0.716).令人鼓舞的是,在ArrayExpress数据库中也观察到类似的结果.在E-MTAB-7581数据集中,在HLA高危亚组中使用氢化可的松(OR:2.84,95%CI1.07-7.57,p=0.037)与死亡风险增加相关,但在HLA低风险亚组中没有。此外,CIBERSORTx和ssGSEA的免疫浸润分析显示,B细胞,激活的树突状细胞,NK细胞,T辅助细胞,浸润淋巴细胞(IL)在HLA低危表型中明显丰富,而Tregs和骨髓来源的抑制细胞(MDSCs)在HLA高风险表型中更丰富。HLA分类器与B细胞呈显著负相关,激活的树突状细胞,NK细胞,T辅助细胞,和IL,然而与Tregs和MDSCs显著正相关。随后,分子通路分析揭示了细胞因子-细胞因子受体(CCR)相互作用,人类白细胞抗原(HLA),和抗原呈递细胞(APC)共刺激在HLA低风险基因型中显著富集,这与多转录组数据中的HLA分类器呈显著负相关。最后,几种细胞因子的表达水平(IL-10,IFNG,TNF)在HLA亚组之间有显著差异,在多转录组数据中,IL-10/TNF的比率与HLA评分呈显着正相关。qRT-PCR的结果证实与对照样品相比,脓毒症样品中B2M的表达水平较高以及HLA-DQA1、HLA-DPA1、TAP1和TAP2的表达水平较低。结论:基于5个HLA基因,诊断和预后模型,即HLA分类器,成立了,这与对氢化可的松的反应和免疫抑制状态密切相关,可能有助于对特定治疗的个性化咨询。
Background: Sepsis is a clinical syndrome, due to a dysregulated inflammatory response to infection. Accumulating evidence shows that human leukocyte antigen (HLA) genes play a key role in the immune responses to sepsis. Nevertheless, the effects of HLA genes in sepsis have still not been comprehensively understood. Methods: A systematical search was performed in the Gene Expression Omnibus (GEO) and ArrayExpress databases from inception to 10 September 2021. Random forest (RF) and modified Lasso penalized regression were conducted to identify hub genes in multi-transcriptome data, thus we constructed a prediction model, namely the HLA classifier. ArrayExpress databases, as external validation, were utilized to evaluate its diagnostic, prognostic, and predictive performance. Immune cell infiltration score was calculated via CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched in different subgroups. Next, we systematically correlated the HLA classifier with immunological characteristics from multiple perspectives, such as immune-related cell infiltration, pivotal molecular pathways, and cytokine expression. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to validate the expression level of HLA genes in clinical samples. Results: A total of nine datasets comprising 1,251 patients were included. Based on RF and modified Lasso penalized regression in multi-transcriptome datasets, five HLA genes (B2M, HLA-DQA1, HLA-DPA1, TAP1, and TAP2) were identified as hub genes, which were used to construct an HLA classifier. In the discovery cohort, the HLA classifier exhibited superior diagnostic value (AUC = 0.997) and performed better in predicting mortality (AUC = 0.716) than clinical characteristics or endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. In the E-MTAB-7581 dataset, the use of hydrocortisone in the HLA high-risk subgroup (OR: 2.84, 95% CI 1.07-7.57, p = 0.037) was associated with increased risk of mortality, but not in the HLA low-risk subgroup. Additionally, immune infiltration analysis by CIBERSORTx and ssGSEA revealed that B cells, activated dendritic cells, NK cells, T helper cells, and infiltrating lymphocytes (ILs) were significantly richer in HLA low-risk phenotypes, while Tregs and myeloid-derived suppressor cells (MDSCs) were more abundant in HLA high-risk phenotypes. The HLA classifier was significantly negatively correlated with B cells, activated dendritic cells, NK cells, T helper cells, and ILs, yet was significantly positively correlated with Tregs and MDSCs. Subsequently, molecular pathways analysis uncovered that cytokine-cytokine receptor (CCR) interaction, human leukocyte antigen (HLA), and antigen-presenting cell (APC) co-stimulation were significantly enriched in HLA low-risk endotypes, which was significantly negatively correlated with the HLA classifier in multi-transcriptome data. Finally, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different between the HLA subgroups, and the ratio of IL-10/TNF was significantly positively correlated with HLA score in multi-transcriptome data. Results of qRT-PCR validated the higher expression level of B2M as well as lower expression level of HLA-DQA1, HLA-DPA1, TAP1, and TAP2 in sepsis samples compared to control sample. Conclusion: Based on five HLA genes, a diagnostic and prognostic model, namely the HLA classifier, was established, which is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.