Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning

被引：11
作者：
She, Han [1 ,2 ]
Tan, Lei [1 ,2 ]
Yang, Ruibo [2 ]
Zheng, Jie [3 ]
Wang, Yi [1 ,2 ]
Du, Yuanlin [2 ]
Peng, Xiaoyong [1 ]
Li, Qinghui [1 ]
Lu, Haibin [4 ]
Xiang, Xinming [1 ]
Hu, Yi [2 ]
Liu, Liangming [1 ]
Li, Tao [1 ]
机构：
[1] Army Med Univ, Daping Hosp, Shock & Transfus Dept, State Key Lab Trauma,Burns & Combined Injury, Chongqing, Peoples R China
[2] Army Med Univ, Daping Hosp, Dept Anesthesiol, Chongqing, Peoples R China
[3] Chongqing Univ, Sch Med, Chongqing, Peoples R China
[4] Army Med Univ, Daping Hosp, Dept Intens Care Unit, Chongqing, Peoples R China
来源：
FRONTIERS IN GENETICS | 2023年 / 14卷
基金：
中国国家自然科学基金;
关键词：
sepsis; necroptosis; machine learning algorithm; immune cell infiltration; nomogram; CROSSTALK; CELLS;
D O I：
10.3389/fgene.2023.1158029
中图分类号：
Q3 [遗传学];
学科分类号：
071007 ; 090102 ;
摘要：
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.
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页数：12
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