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Exploration of the Shared Gene Signatures between Myocardium and Blood in Sepsis: Evidence from Bioinformatics
Qi Long1,2, Gang Li1,2, Qiufen Dong1,2
1Department of Critical Care Medicine, Hubei Province Hospital of Traditonal Chinese Medicine, 856 Luoyu Street, Wuhan, Hubei 430061, China.
Insights
Researchers identified six key genes (SMU1, CLIC3, SP100, ARHGAP25, DECR1, TNS3) that can serve as blood biomarkers for septic cardiomyopathy, aiding in diagnosis and mortality prediction.
Area of Science:
- Biochemistry
- Genomics
- Computational Biology
Background:
- Septic cardiomyopathy is a common complication of sepsis with significant mortality.
- Current diagnosis relies heavily on echocardiography, with a lack of specific blood biomarkers.
- Identifying blood-based biomarkers could improve early diagnosis and patient management.
Purpose of the Study:
- To identify potential blood biomarkers for septic cardiomyopathy by analyzing gene expression in both blood and myocardium.
- To develop a predictive model for disease classification and mortality risk in sepsis patients.
Main Methods:
- Bioinformatics and machine learning approaches were used on public datasets (Gene Expression Omnibus).
- Differential gene expression analysis, functional enrichment, and unsupervised clustering were performed.
- Random forest, lasso, and logistic regression models were employed for biomarker selection and validation.
Main Results:
- 1,049 genes were commonly altered in the blood and myocardium of sepsis patients.
- Upregulated genes were linked to inflammation; downregulated genes to mitochondrial function.
- A six-gene signature (SMU1, CLIC3, SP100, ARHGAP25, DECR1, TNS3) accurately classified sepsis subtypes and predicted mortality.
Conclusions:
- Identified a panel of genes with potential as biomarkers for septic cardiomyopathy.
- Demonstrated that myocardial changes in sepsis are reflected in peripheral blood.
- These biomarkers may improve prognosis prediction in sepsis patients.
Background:
Septic cardiomyopathy is widespread during sepsis and has adverse effects on mortality. Diagnosis of septic cardiomyopathy now mainly depends on transthoracic echocardiogram. Although some laboratory tests such as troponin T and atrial brain natriuretic peptide play a role in the diagnosis, specific blood biochemistry biomarkers are still lacking. Objective and Methods. In our study, we sought to find potential biological markers from genes and pathways that are covariant in the blood and myocardium of septic patients. Bioinformatics and machine learning methods were applied to achieve our goal. Datasets of myocardium and peripheral blood of patients with sepsis were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were selected and received functional enrichment analysis. Unsupervised hierarchical clustering analysis was performed to identify the subtypes of sepsis. Random forest, lasso regression, and logistic regression were used for variable screening and model construction. Internal and external validation sets were applied to verify the efficiency of the model in classifying disease and predicting mortality.
Results:
By defining significance for genes using Student's t-test, we obtained 1,049 genes commonly changed in both myocardium and blood of patients with sepsis. The upregulated genes (LogFC >0) were related to inflammation pathways, and downregulated (LogFC <0) genes were related to mitochondrial and aerobic metabolism. We divided 468 sepsis patients into two groups with different clinical result based on the mortality-related commonly changed genes (104 genes), using unsupervised hierarchical clustering analysis. In our validation datasets, a six-gene model (SMU1, CLIC3, SP100, ARHGAP25, DECR1, and TNS3) was obtained and proven to perform well in classifying groups and predicting mortality.
Conclusion:
We have identified genes that have the potential to become biomarkers for septic cardiomyopathy. Additionally, the pathophysiological changes in the myocardium of patients with sepsis were also reflected in peripheral blood to some extent. The co-occurring pathological processes can affect the prognosis of sepsis.
