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Updated: Aug 17, 2025
Author Spotlight: Advancing Immune Monitoring in Critical Care Patients Using Whole Blood Assays
Published on: September 20, 2024
Machine learning links different gene patterns of viral infection to immunosuppression and immune-related biomarkers
Peng Wang1, Zexin Zhang2, Rongjie Lin3
1Department of Burns and Plastic and Cosmetic Surgery, Xi'an Ninth Hospital, Xi'an, China.
Introduction:
Viral infection, typically disregarded, has a significant role in burns. However, there is still a lack of biomarkers and immunotherapy targets related to viral infections in burns.
Methods:
Virus-related genes (VRGs) that were extracted from Gene Oncology (GO) database were included as hallmarks. Through unsupervised consensus clustering, we divided patients into two VRGs molecular patterns (VRGMPs). Weighted gene co-expression network analysis (WGCNA) was performed to study the relationship between burns and VRGs. Random forest (RF), least absolute shrinkage and selection operator (LASSO) regression, and logistic regression were used to select key genes, which were utilized to construct prognostic signatures by multivariate logistic regression. The risk score of the nomogram defined high- and low-risk groups. We compared immune cells, immune checkpoint-related genes, and prognosis between the two groups. Finally, we used network analysis and molecular docking to predict drugs targeting CD69 and SATB1. Expression of CD69 and SATB1 was validated by qPCR and microarray with the blood sample from the burn patient.
Results:
We established two VRGMPs, which differed in monocytes, neutrophils, dendritic cells, and T cells. In WGCNA, genes were divided into 14 modules, and the black module was correlated with VRGMPs. A total of 65 genes were selected by WGCNA, STRING, and differential expression analysis. The results of GO enrichment analysis were enriched in Th1 and Th2 cell differentiation, B cell receptor signaling pathway, alpha-beta T cell activation, and alpha-beta T cell differentiation. Then the 2-gene signature was constructed by RF, LASSO, and LOGISTIC regression. The signature was an independent prognostic factor and performed well in ROC, calibration, and decision curves. Further, the expression of immune cells and checkpoint genes differed between high- and low-risk groups. CD69 and SATB1 were differentially expressed in burns.
Discussion:
This is the first VRG-based signature (including 2 key genes validated by qPCR) for predicting survival, and it could provide vital guidance to achieve optimized immunotherapy for immunosuppression in burns.
Insights
This study identifies a novel 2-gene signature for predicting survival in burn patients, offering potential for optimized immunotherapy. The signature, based on virus-related genes (VRGs), aids in managing viral infections in burns.
Area of Science:
- Immunology
- Genomics
- Burn Medicine
Background:
- Viral infections play a significant role in burn injuries, yet specific biomarkers and immunotherapy targets remain scarce.
- Understanding the genetic landscape of viral infections in burns is crucial for improving patient outcomes.
Purpose of the Study:
- To identify virus-related genes (VRGs) and construct a prognostic signature for burn patients.
- To explore the relationship between VRGs, immune cell infiltration, and patient survival.
- To identify potential therapeutic targets for immunotherapy in burn patients.
Main Methods:
- Utilized Gene Ontology (GO) database for VRG extraction and unsupervised consensus clustering to define molecular patterns.
- Applied Weighted Gene Co-expression Network Analysis (WGCNA), Random Forest (RF), LASSO, and logistic regression for gene selection and signature construction.
- Validated key gene expression (CD69 and SATB1) using qPCR and microarray in burn patient blood samples.
Main Results:
- Established two distinct VRG molecular patterns (VRGMPs) associated with differential immune cell infiltration.
- Developed a 2-gene prognostic signature (CD69 and SATB1) that independently predicts survival in burn patients.
- Identified significant differences in immune cell and checkpoint gene expression between high- and low-risk groups.
Conclusions:
- The developed 2-gene signature is the first VRG-based tool for predicting burn patient survival.
- This signature provides crucial guidance for optimizing immunotherapy strategies to address immunosuppression in burns.
- CD69 and SATB1 represent potential therapeutic targets for managing viral infections in burn patients.
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