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. 2023 Apr 6:14:1158029.
doi: 10.3389/fgene.2023.1158029. eCollection 2023.

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

Han She  1   2 , Lei Tan  1   2 , Ruibo Yang  2 , Jie Zheng  3 , Yi Wang  1   2 , Yuanlin Du  2 , Xiaoyong Peng  1 , Qinghui Li  1 , Haibin Lu  4 , Xinming Xiang  1 , Yi Hu  2 , Liangming Liu  1 , Tao Li  1
Affiliations

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

Han She et al. Front Genet. .

Abstract

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. VSports手机版.

Keywords: immune cell infiltration; machine learning algorithm; necroptosis; nomogram; sepsis. V体育安卓版.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures (V体育安卓版)

FIGURE 1
FIGURE 1
Diagram of the experiment scheme.
FIGURE 2
FIGURE 2
Identification and functional enrichment analysis of DE-NRGs from GEO dataset. (A) Volcano plot of DE-NRGs of GSE65682. Blue dots indicated downregulated DE-NRGs while red dots indicate upregulated DE-NRGs. DE-NRGs were identified as those with student’s t-test p ≤ 0.05. (B) Principal Components Analysis (PCA) score plot of GSE65682. PC1 and PC2 in the figure represent the scores of the first and second principal components respectively. Each scatter represents a sample. The red circle represents septic patients, and the blue circle represents the healthy controls. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DE-NRGs. (D) Gene ontology (GO) results of biological process (BP) cellular component (CC) and molecular function (MF) of DE-NRGs. Gene set enrichment analysis (GSEA) for DE-NRGs in (E) healthy controls and (F) septic patients.
FIGURE 3
FIGURE 3
Four common hub genes were screened out via machine language algorithm. (A) Support vector machine (SVM) was used for screening DE-NRGs. (B) Least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to screen DE-NRGs. LASSO logistic regression was performed with 10-fold cross-validation to screen iteratively reweighted least squares (IRLs) and was performed for 1,000 cycles to select the feature variables based on minimum criteria or 1-se criteria. (C) Random forest (RF) algorithm was used to screen DE-NRGs. Genes with an importance score greater than 1 were used for subsequent signature establishment. (D) VENN diagram of common hub genes.
FIGURE 4
FIGURE 4
Validation of hub genes in the training cohort. (A–D) Validation of the expression of hub genes in patients with sepsis and healthy controls in the training cohort. (E–H) ROC curve of hub genes in the training cohort. (I) Prediction nomogram model was constructed based on the hub genes in the training cohort. (J) ROC curve of the nomogram in the training cohort. ****p < 0.0001.
FIGURE 5
FIGURE 5
Validation of hub genes in the test cohort. (A–D) Validation of the expression of the 4 hub genes in septic patients and healthy controls in the test cohort. (E–H) ROC curve of the hub genes in the test cohort. (I) Prediction nomogram model was constructed based on the hub genes in the test cohort. (J) ROC curve of nomogram in the test cohort. **p < 0.01, ***p < 0.001, ****p < 0.0001.
FIGURE 6
FIGURE 6
The landscape of Immune cell infiltration and the correlation analysis in training cohort. (A) Person’s correlation analysis of 22 types of immune cells. (B) Analysis of immune-cell proportion comparisons between septic patients and healthy controls by CIBERSORT. (C) Person’s correlation analysis between infiltrating immune cells and the 4 hub genes. Red nodes indicated positive correlation while blue nodes indicated negative correlation. (D) Immune scores analyzed by the R package “estimate” between healthy control (HC) and septic patients (SP). *p ≤ 0.05, **p < 0.01, ***p < 0.001.
FIGURE 7
FIGURE 7
Consensus clustering of septic patients and immune microenvironment landscape analysis. (A) Consensus clustering heatmap showed the optimal classification of septic samples with K = 2. (B) Consensus CDF. (C) Delta area. (D–G) Gene expression of the 4 hub genes between Cluster A and Cluster B. (H) PCA analysis showed a different distribution pattern in Cluster A and Cluster B. (I) The fraction of the 22 types of immune cells in Cluster A and Cluster B. (J) The immune function scores between Cluster A and Cluster B. *p ≤ 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
FIGURE 8
FIGURE 8
Multivariate prediction nomogram model of sepsis. (A) Heart Rate. (B) Temperature. (C) White blood count. (D) Normalized gene expression of the 4 hub genes between HC and SP by qPCR. (E) Person’s correlation analysis between immune cells proportion of blood routine and the 4 hub genes. (F) Prediction nomogram model was constructed based on multivariate indicators. (G) ROC curve of multivariate indicators. *p ≤ 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
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