Table3_Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning.DOCX

Background: The precise diagnostic and prognostic biological markers were needed in immunotherapy for sepsis. Considering the role of necroptosis and immune cell infiltration in sepsis, differentially expressed necroptosis-related genes (DE-NRGs) were identified, and the relationship between DE-NRGs and the immune microenvironment in sepsis was analyzed.
Methods: Machine learning algorithms were applied for screening hub genes related to necroptosis in the training cohort. CIBERSORT algorithms were employed for immune infiltration landscape analysis. Then, the diagnostic value of these hub genes was verified by the receiver operating characteristic (ROC) curve and nomogram. In addition, consensus clustering was applied to divide the septic patients into different subgroups, and quantitative real-time PCR was used to detect the mRNA levels of the hub genes between septic patients (SP) (n = 30) and healthy controls (HC) (n = 15). Finally, a multivariate prediction model based on heart rate, temperature, white blood count and 4 hub genes was established.
Results: A total of 47 DE-NRGs were identified between SP and HC and 4 hub genes (BACH2, GATA3, LEF1, and BCL2) relevant to necroptosis were screened out via multiple machine learning algorithms. The high diagnostic value of these hub genes was validated by the ROC curve and Nomogram model. Besides, the immune scores, correlation analysis and immune cell infiltrations suggested an immunosuppressive microenvironment in sepsis. Septic patients were divided into 2 clusters based on the expressions of hub genes using consensus clustering, and the immune microenvironment landscapes and immune function between the 2 clusters were significantly different. The mRNA levels of the 4 hub genes significantly decreased in SP as compared with HC. The area under the curve (AUC) was better in the multivariate prediction model than in other indicators.
Conclusion: This study indicated that these necroptosis hub genes might have great potential in prognosis prediction and personalized immunotherapy for sepsis.

背景：脓毒症（sepsis）免疫治疗亟需精准的诊断与预后生物标志物。鉴于坏死性凋亡（necroptosis）与免疫细胞浸润在脓毒症发病中的作用，本研究首先鉴定出差异表达坏死性凋亡相关基因（differentially expressed necroptosis-related genes, DE-NRGs），并分析了DE-NRGs与脓毒症免疫微环境的关联。
方法：本研究在训练队列中采用机器学习算法筛选与坏死性凋亡相关的核心基因（hub genes）。采用CIBERSORT算法进行免疫浸润景观分析。随后，通过受试者工作特征（receiver operating characteristic, ROC）曲线及列线图（nomogram）验证了上述核心基因的诊断价值。此外，采用一致性聚类将脓毒症患者分为不同亚组，并通过实时定量聚合酶链反应检测了30例脓毒症患者（septic patients, SP）与15例健康对照（healthy controls, HC）的核心基因mRNA表达水平。最后，本研究构建了基于心率、体温、白细胞计数及4个核心基因的多变量预测模型。
结果：本研究在脓毒症患者与健康对照中共鉴定出47个DE-NRGs，并通过多种机器学习算法筛选出4个与坏死性凋亡相关的核心基因（BACH2、GATA3、LEF1及BCL2）。上述核心基因的高诊断价值经ROC曲线及列线图模型得到验证。此外，免疫评分、相关性分析及免疫细胞浸润结果显示，脓毒症患者体内存在免疫抑制性微环境。通过一致性聚类基于核心基因表达谱将脓毒症患者分为2个亚组，且两个亚组间的免疫微环境特征与免疫功能均存在显著差异。与健康对照相比，脓毒症患者体内4个核心基因的mRNA表达水平显著降低。多变量预测模型的曲线下面积（area under the curve, AUC）优于其他单项指标。
结论：本研究表明，上述坏死性凋亡相关核心基因在脓毒症的预后预测及个性化免疫治疗中具有巨大应用潜力。