Objective:

To identify specific lactate-associated immune cell populations and construct a diagnostic model for sepsis using integrated transcriptomic data and machine learning.

Key Findings:

• LDHA was identified as a potential biomarker associated with sepsis severity and prognosis, highlighting its clinical relevance.
• Elevated serum lactate levels correlate with increased mortality in sepsis.
• Integration of single-cell and bulk transcriptomic data enhances the identification of robust biomarkers.

Interpretation:

The findings suggest that LDHA and lactate metabolism play critical roles in sepsis pathogenesis and could serve as valuable biomarkers for diagnosis and prognosis, potentially guiding therapeutic strategies.

Limitations:

• Retrospective design may introduce biases that could affect the reliability of the findings.
• Heterogeneous clinical annotations could affect data interpretation and generalizability.
• Incomplete covariates limit the robustness of survival analyses, necessitating cautious interpretation.

Conclusion:

The study highlights the importance of lactate-associated biomarkers in sepsis and suggests that LDHA could be a significant target for further research and clinical application, warranting additional studies to validate its role.