Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza - Lin, Siran; Peng, YuBing; Xu, Yuzhen; Zhang, Wei; Wu, Jing; Zhang, Wenhong; Shao, Lingyun; Gao, Yan (Frontiers in Cellular and Infection Microbiology, 2022)
H1N1 is the most common subtype of the influenza viruses circulating worldwide and can cause severe influenza in some populations. Early prediction and interventions for patients who may develop severe influenza will greatly reduce their mortality. In this study, we conducted a comprehensive analysis of a total of 180 PBMC samples from two published datasets from the GEO DataSets. Differentially expressed gene (DEG) analysis and weighted correlation network analysis (WGCNA) analysis were conducted to provide candidate DEGs for model building. Functional enrichment analysis and CIBERSORT were also performed to clarify the difference of the composition and function of PBMCs between the severe and mild patients. Finally, a risk score model was developed by lasso regression analysis, with six genes (CX3CR1, KLRD1, MMP8, PRTN3, RETN and SCD) involved. The model performed moderately in early identification of severe ones in H1N1 patients.