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Identification of diagnostic candidate genes in COVID-19 patients with sepsis
Jiuang Li1, Shiqian Pu1, Lei Shu1
1Department of Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Insights
This study identified five key genes (CD3D, IL2RB, KLRC, CD5, and HLA-DQA1) as potential diagnostic markers for Coronavirus Disease 2019 (COVID-19) patients experiencing sepsis. These findings aid in developing new diagnostic tools for this critical condition.
Area of Science:
- Genomics
- Immunology
- Bioinformatics
Background:
- Coronavirus Disease 2019 (COVID-19) and sepsis share complex pathophysiological links.
- Accurate and early diagnosis is crucial for managing COVID-19 patients with sepsis.
Purpose of the Study:
- To identify pivotal diagnostic candidate genes for COVID-19 patients with sepsis.
- To evaluate the diagnostic utility of identified genes using machine learning and immune infiltration analysis.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) datasets for COVID-19 and sepsis.
- Applied Linear Models for Microarray Data (LIMMA), weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) networks.
- Employed machine learning algorithms (LASSO, Random Forest) and Receiver Operating Characteristic (ROC) curves for gene selection and diagnostic value assessment.
Main Results:
- Identified 3,438 differentially expressed genes (DEGs) in COVID-19 and 595 common genes in sepsis.
- 329 common DEGs between COVID-19 and sepsis were enriched in immune system pathways.
- Selected 8 diagnostic genes with Area Under the Curve (AUC) > 0.9, including five core genes (CD3D, IL2RB, KLRC, CD5, HLA-DQA1) associated with immune infiltration.
Conclusions:
- Five core genes (CD3D, IL2RB, KLRC, CD5, HLA-DQA1) show diagnostic utility in COVID-19 patients with sepsis.
- These genes are potential peripheral blood diagnostic markers.
- Findings support the development of novel diagnostic strategies for sepsis in COVID-19.
Purpose:
Coronavirus Disease 2019 (COVID-19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID-19 patients with sepsis.
Patients And Methods:
We obtained a COVID-19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID-19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration.
Results:
The COVID-19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID-19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes.
Conclusion:
Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA-DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID-19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID-19 patients with sepsis.
