Bioinformatics-Led Identification of Potential Biomarkers and Inflammatory Infiltrates in Burn Injury

Area of Science:

• Immunology
• Genomics
• Computational Biology

Background:

• Burn injury poses a severe threat with poorly understood immune system alterations.
• Identifying immune biomarkers is crucial for improving burn patient prognosis.

Purpose of the Study:

• To identify potential immune-related gene (IRG) biomarkers for burn injury.
• To analyze immune cell infiltration and develop a predictive model for burn patients.

Main Methods:

• Utilized Gene Expression Omnibus data for differential gene expression and LASSO regression to screen key IRGs.
• Employed consensus clustering, Gene Set Enrichment Analysis (GSEA), and Principal Component Analysis (PCA) for immune profiling.
• Constructed a nomogram model integrating immune scores and clinical features, validated with external data and qPCR.

Main Results:

• Identified 59 differentially expressed IRGs, narrowing down to 12 key genes (AZU1, OLR1, RNASE2, FGF13, NR1D2, NR2E1, TLR5, CAMP, DEFA4, PGLYRP1, CTSG, CCR3).
• Classified patients into two clusters based on key IRGs, with Cluster A exhibiting higher immune cell infiltration and pathway activation.
• Developed a validated nomogram model demonstrating high accuracy in predicting outcomes.

Conclusions:

• The study highlights the critical role of immune responses in burn injury pathogenesis.
• The identified key IRGs and nomogram model offer potential tools for clinical assessment and treatment guidance in burn care.