RESUMEN
The aim of this study was to investigate the correlation between CRGs and immunoinfiltration in keloid, develop a predictive model for keloid occurrence, and explore potential therapeutic drugs. The microarray datasets (GSE7890 and GSE145725) were obtained from Gene Expression Omnibus database to identify the differentially expressed genes (DEGs) between keloid and non-keloid samples. Key genes were identified through immunoinfiltration analysis and DEGs, then analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, followed by the identification of protein-protein interaction networks, transcription factors, and miRNAs associated with key genes. Additionally, a logistic regression analysis was performed to develop a predictive model for keloid occurrence, and potential candidate drugs for keloid treatment were identified. Three key genes (FDX1, PDHB, DBT) were identified, showing involvement in acetyl-CoA biosynthesis, mitochondrial matrix, oxidoreductase activity, and the tricarboxylic acid cycle. Immune infiltration analysis suggested the involvement of B cells, Th1 cells, DCs, T helper cells, APC co-inhibition, and T cell co-inhibition in keloid. These genes were used to develop a logistic regression-based nomogram for predicting keloid occurrence with an AUC of 0.859 and good calibration.We identified 32 potential drug molecules and extracted the top 10 compounds based on their P-values, showing promise in targeting key genes and potentially effective against keloid. Our study identified some genes in keloid pathogenesis and potential therapeutic drugs. The predictive model enhance early diagnosis and management. Further research is needed to validate and explore clinical implications.
RESUMEN
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.