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

Frontiers in Cellular and Infection Microbiology

Jing Wu¹  Lingyun Shao¹  Wei Zhang¹  Yuzhen Xu¹  Siran Lin¹  Yan Gao¹  Wenhong Zhang²  YuBing Peng³ 

[1] Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China;Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China;National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China;State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China;Key Laboratory of Medical Molecular Virology (Key Laboratories of the Ministry of Education (MOE)/Key Laboratories of the Ministry of Health (MOH)) and Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China;Department of Urology, RenJi Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China;

关键词: differentially expressed gene;    H1N1;    prediction;    risk score model;    severe influenza;   

DOI  :  10.3389/fcimb.2021.776840

来源: Frontiers

【 摘 要 】

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 (CX3CR1, KLRD1, MMP8, PRTN3, RETN and SCD) involved. The model performed moderately in the early identification of patients that develop severe H1N1 disease.

【 授权许可】

CC BY