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Published on: August 12, 2020
Pyroptosis-Related Molecular Clusters and Immune Infiltration in Pediatric Sepsis
Mingxin Lin1, Chenxi Li1, Ye Wang1
1Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian, People's Republic of China.
Insights
This study identifies pyroptosis-related genes in pediatric sepsis, revealing key genes that can predict the condition. These findings offer a new approach for diagnosing pediatric sepsis.
Area of Science:
- Immunology
- Genetics
- Computational Biology
Background:
- Pediatric sepsis involves a dysregulated immune response to infection.
- Pyroptosis, a form of programmed cell death, is linked to inflammation but its role in pediatric sepsis is unknown.
Purpose of the Study:
- To explore pyroptosis-related differentially expressed genes (DEGs) in pediatric sepsis.
- To identify molecular clusters and hub genes associated with pyroptosis in pediatric sepsis.
- To develop a predictive model for pediatric sepsis risk assessment.
Main Methods:
- Analysis of pyroptosis-related DEGs in the GSE13904 dataset.
- Weighted gene co-expression network analysis (WGCNA) to identify cluster-specific DEGs.
- Machine learning models (RF, SVM, GLM, XGB) to identify the optimal predictive model.
- Validation of hub genes using ROC analysis and qRT-PCR on clinical samples.
Main Results:
- Dysregulated pyroptosis-related DEGs were identified in pediatric sepsis.
- Three pyroptosis-related molecular clusters were determined.
- Support Vector Machine (SVM) showed the best predictive performance.
- Five hub genes demonstrated satisfactory diagnostic value and were significantly upregulated in pediatric sepsis patients.
Conclusions:
- Pyroptosis plays a significant role in pediatric sepsis.
- A predictive model based on pyroptosis-related hub genes shows promise for evaluating pediatric sepsis risk.
Background:
Pediatric sepsis is a complex and heterogeneous condition resulting from a dysregulated immune response to infection. Pyroptosis, a newly recognized form of programmed cell death, has been implicated in the progression of various inflammatory diseases. However, the role of pyroptosis-related genes in pediatric sepsis remains unclear.
Methods:
Based on the GSE13904 dataset, we explored the pyroptosis-related differentially expressed genes (DEGs) in pediatric sepsis. We analyzed the molecular clusters based on pyroptosis-related DEGs. The WGCNA algorithm was performed to identify cluster-specific DEGs. The optimal machine model was identified by multiple machine learning methods (RF, SVM, GLM, XGB). The diagnostic value of hub genes in pediatric sepsis was verified in the training (GSE13904) and validation set (GSE26440) through ROC. qRT-PCR was used to verify the expression levels of 5 hub genes in whole blood between the pediatric sepsis and the control.
Results:
The dysregulated pyroptosis-related DEGs were identified in pediatric sepsis. Three pyroptosis-related molecular clusters were determined in pediatric sepsis. SVM presented the best discriminative performance with relatively lower residual and root mean square error. The nomogram, calibration curve, and decision curve analysis indicated the accuracy of SVM model to predict pediatric sepsis. 5 hub genes based on SVM presented satisfactory performance in the training and validation sets. These hub genes expression levels in pediatric sepsis were significantly higher than those in healthy controls in clinical samples.
Conclusion:
Our study systematically analyzed the relationship between pyroptosis and pediatric sepsis, and constructed a promising predictive model to evaluate the risk of pediatric sepsis.
