AUTHOR=Lin Siran , Peng YuBing , Xu Yuzhen , Zhang Wei , Wu Jing , Zhang Wenhong , Shao Lingyun , Gao Yan 

TITLE=Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=11

YEAR=2022

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2021.776840

DOI=10.3389/fcimb.2021.776840

ISSN=2235-2988

ABSTRACT=<p>H1N1 is the most common subtype of influenza virus circulating worldwide and can cause severe disease in some populations. Early prediction and intervention for patients who develop severe influenza will greatly reduce their mortality. In this study, we conducted a comprehensive analysis of 180 PBMC samples from three published datasets from the GEO DataSets. Differentially expressed gene (DEG) analysis and weighted correlation network analysis (WGCNA) were performed to provide candidate DEGs for model building. Functional enrichment and CIBERSORT analyses were also performed to evaluate the differences in composition and function of PBMCs between patients with severe and mild disease. Finally, a risk score model was built using lasso regression analysis, with six genes (<italic>CX3CR1</italic>, <italic>KLRD1</italic>, <italic>MMP8</italic>, <italic>PRTN3</italic>, <italic>RETN</italic> and <italic>SCD</italic>) involved. The model performed moderately in the early identification of patients that develop severe H1N1 disease.</p>