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Identification of m5C-Related gene diagnostic biomarkers for sepsis: a machine learning study
Siming Lin1,2, Kexin Cai1,2, Shaodan Feng1,2
1Department of Emergency Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Background:
Sepsis is a serious condition that occurs when the body's response to infection becomes uncontrolled, resulting in a high risk of death. Despite improvements in healthcare, identifying sepsis early is difficult because of its diverse nature and the absence of distinct biomarkers. Recent studies suggest that 5-methylcytosine (m5C)-related genes play a significant role in immune responses, yet their diagnostic potential in sepsis remains unexplored.
Methods:
This research combined and examined four sepsis-related datasets (GSE95233, GSE57065, GSE100159, and GSE65682) sourced from the Gene Expression Omnibus (GEO)database to discover m5C-related genes with differential expression. Various machine learning methods, such as decision tree, random forest, and XGBoost, were utilized in identifying crucial hub genes. Receiver Operating Characteristic (ROC) curve analysis was used to assess the diagnostic accuracy of these genetic markers. Additionally, single-gene enrichment and immune infiltration analyses were conducted to investigate the underlying mechanisms involving these hub genes in sepsis.
Results:
Three hub genes, DNA Methyltransferase 1 (DNMT1), tumor protein P53 (TP53), and toll-like receptor 8 (TLR8), were identified and validated for their diagnostic efficacy, showing area under the curve (AUC) values above 0.7 in both test and validation sets. Enrichment analyses revealed that these genes are involved in key pathways such as p53 signaling and Toll-like receptor signaling. Immune infiltration analysis indicated significant correlations between hub genes and various immune cell types, suggesting their roles in modulating immune responses during sepsis.
Conclusion:
The study highlights the diagnostic potential of m5C-related genes in sepsis and their involvement in immune regulation. These findings offer new insights into sepsis pathogenesis and suggest that DNMT1, TP53, and TLR8 could serve as valuable biomarkers for early diagnosis. Further studies should prioritize validating these biomarkers in clinical settings and investigating their potential for therapy.
Insights
This study identifies three key 5-methylcytosine (m5C)-related genes (DNMT1, TP53, TLR8) as potential biomarkers for early sepsis diagnosis. These genes are linked to immune responses, offering new insights into sepsis and potential therapeutic targets.
Area of Science:
- Genomics
- Immunology
- Computational Biology
Background:
- Sepsis, a life-threatening response to infection, lacks early diagnostic biomarkers due to its complex nature.
- 5-methylcytosine (m5C)-related genes are implicated in immune responses, but their role in sepsis diagnosis is uninvestigated.
Purpose of the Study:
- To identify differentially expressed m5C-related genes in sepsis.
- To evaluate the diagnostic potential of these genes as sepsis biomarkers.
- To explore the underlying immune mechanisms associated with these genes in sepsis.
Main Methods:
- Analysis of four public sepsis gene expression datasets (GSE95233, GSE57065, GSE100159, GSE65682).
- Application of machine learning algorithms (decision tree, random forest, XGBoost) to identify hub genes.
- Receiver Operating Characteristic (ROC) curve analysis for diagnostic accuracy assessment.
- Single-gene enrichment and immune infiltration analyses to elucidate mechanisms.
Main Results:
- Identification and validation of three hub genes: DNA Methyltransferase 1 (DNMT1), tumor protein P53 (TP53), and toll-like receptor 8 (TLR8).
- These genes demonstrated diagnostic efficacy with Area Under the Curve (AUC) > 0.7.
- Enrichment analyses linked these genes to p53 and Toll-like receptor signaling pathways.
- Significant correlations were found between hub genes and immune cell infiltration, indicating immune modulation roles.
Conclusions:
- m5C-related genes, specifically DNMT1, TP53, and TLR8, show promise as diagnostic biomarkers for sepsis.
- These genes are involved in sepsis pathogenesis and immune regulation.
- Further clinical validation is recommended for these potential biomarkers and therapeutic targets.
