tetano
Editor, Senior Moderator
Front Cell Infect Microbiol
. 2022 Jan 4;11:776840.
doi: 10.3389/fcimb.2021.776840. eCollection 2021.
Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza
Siran Lin[SUP] 1 [/SUP], YuBing Peng[SUP] 2 [/SUP], Yuzhen Xu[SUP] 1 [/SUP], Wei Zhang[SUP] 1 [/SUP], Jing Wu[SUP] 1 [/SUP], Wenhong Zhang[SUP] 1 3 4 5 [/SUP], Lingyun Shao[SUP] 1 [/SUP], Yan Gao[SUP] 1 [/SUP]
Affiliations
Abstract
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.
Keywords: H1N1; differentially expressed gene; prediction; risk score model; severe influenza.
. 2022 Jan 4;11:776840.
doi: 10.3389/fcimb.2021.776840. eCollection 2021.
Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza
Siran Lin[SUP] 1 [/SUP], YuBing Peng[SUP] 2 [/SUP], Yuzhen Xu[SUP] 1 [/SUP], Wei Zhang[SUP] 1 [/SUP], Jing Wu[SUP] 1 [/SUP], Wenhong Zhang[SUP] 1 3 4 5 [/SUP], Lingyun Shao[SUP] 1 [/SUP], Yan Gao[SUP] 1 [/SUP]
Affiliations
- PMID: 35059324
- PMCID: PMC8764189
- DOI: 10.3389/fcimb.2021.776840
Abstract
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.
Keywords: H1N1; differentially expressed gene; prediction; risk score model; severe influenza.