Bioinformatics-led identification of potential biomarkers and inflammatory infiltrates in burn injury.

Abstract

Burn injury is a life-threatening disease with a poor prognosis. The immune change and underlying mechanisms remain largely unknown. Thus, this study aims to find potential biomarkers and analyze the immune infiltrates after burn injury. Gene expression data of burn patients were obtained from the Gene Expression Omnibus database. Key immune-related genes were screened by differential and LASSO regression analysis. Based on key immune-related genes, patients were divided into two clusters by consensus cluster analysis. Immune infiltration was analyzed by the ssGSEA method and the immune score was calculated by the PCA method. A nomogram model was constructed based on the calculated immune score and clinical features. Finally, the expression of screened key genes was validated by an external cohort and q-PCR experiment. Fifty-nine immune-related genes were differently expressed in burn patients. After LASSO regression analysis, 12 key genes remained, namely AZU1, OLR1, RNASE2, FGF13, NR1D2, NR2E1, TLR5, CAMP, DEFA4, PGLYRP1, CTSG and CCR3. Then, patients were divided into two clusters. Immune infiltration analysis revealed that more immune cells were infiltrated and more pathways were activated in cluster A, in which patients showed high immune scores. Finally, a nomogram model was constructed and showed high accuracy and reliability. The expression pattern of 12 key genes in an external cohort and clinical samples was in accordance with the theoretical analysis results. In conclusions, this research elucidated the key role of immune response in burns and could be used as a guide for burn treatment.