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Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and
Chunyu Li1,2, Ke Wu1,2, Rui Yang3
1Department of Respiratory and Critical Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China.
Scientific Reports
|April 10, 2024
Summary
This study explores immunogenic cell death (ICD) genes in COVID-19 pathogenesis. We identified five key genes to predict severe coronavirus disease 2019 (COVID-19) and developed a nomogram to aid early identification of critical cases.
Area of Science:
- Immunology
- Molecular Biology
- Virology
Background:
- Severe COVID-19, often fatal due to ARDS, requires understanding its molecular pathogenesis.
- Immunogenic cell death (ICD) plays a role in immune responses, but its involvement in severe COVID-19 is understudied.
Purpose of the Study:
- To investigate the role of ICD-related genes in COVID-19 pathogenesis.
- To identify molecular biomarkers for predicting severe COVID-19 cases.
Main Methods:
- Systematic evaluation of ICD-related genes in COVID-19 patients.
- Consensus clustering, immune infiltration, and functional enrichment analyses.
- Machine learning for risk gene identification and nomogram construction.
Main Results:
- Significant alterations in immune infiltration characteristics between severe and non-severe COVID-19.
- Identification of five predictive risk genes: KLF5, NSUN7, APH1B, GRB10, and CD4.
- Development and validation of a nomogram for predicting severe COVID-19 risk.
Conclusions:
- ICD-related genes are implicated in COVID-19 severity.
- The identified genes and nomogram can aid in early identification of high-risk patients.
- This research offers insights into COVID-19 pathogenesis and potential therapeutic targets.
