Combining Single-Cell and Bulk Transcriptomic Analysis with Machine Learning Reveals LDHA as a Lactate-Associated Biomarker for Diagnosis and Prognosis in Sepsis - Report - MDSpire

Combining Single-Cell and Bulk Transcriptomic Analysis with Machine Learning Reveals LDHA as a Lactate-Associated Biomarker for Diagnosis and Prognosis in Sepsis

  • By

  • Zhiying Lin

  • Hanping Shi

  • Xiaohong Chen

  • Chunli Yang

  • February 24, 2026

  • 0 min

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Clinical Report: LDHA as a Lactate-Associated Biomarker for Sepsis

Overview

This study identifies LDHA as a potential biomarker for diagnosing and prognosing sepsis by integrating single-cell and bulk transcriptomic analyses with machine learning. The findings underscore the importance of lactate-associated immune cell populations in understanding sepsis severity.

Background

Sepsis is a critical condition characterized by organ dysfunction due to a dysregulated response to infection, leading to high mortality rates in ICUs. Identifying reliable biomarkers is essential for improving diagnosis and treatment outcomes. Recent advancements in transcriptomic technologies provide new avenues for understanding the metabolic and immune dysregulation associated with sepsis.

Data Highlights

No numerical data available.

Key Findings

  • LDHA was identified as a key lactate-associated gene with diagnostic and prognostic relevance in sepsis.
  • Integration of single-cell RNA sequencing and bulk RNA sequencing datasets facilitated the identification of lactate-associated immune cell populations.
  • The study developed a lactate-related diagnostic model, termed the 'Lacty Model', for sepsis.
  • Elevated serum lactate levels are strongly correlated with increased mortality in sepsis patients.
  • Machine learning approaches enhanced the identification of robust biomarkers linked to immune–metabolic dysregulation.

Clinical Implications

The identification of LDHA as a biomarker may aid clinicians in diagnosing and assessing the prognosis of sepsis patients. Incorporating lactate measurements into clinical practice could improve patient management and treatment strategies.

Conclusion

The findings highlight the potential of LDHA as a biomarker for sepsis, emphasizing the need for further research to validate its clinical utility. Integrating advanced transcriptomic analyses with machine learning may pave the way for improved diagnostic tools in sepsis management.

References

  1. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2026 | SCCM
  2. Lactate dehydrogenase is an indicator for outcomes of short-term and long-term in septic patients | PLOS One
  3. npj Digital Medicine — A Blood Transcriptomic Profile Utilizing Machine Learning for Digital Diagnosis and Classification of Alzheimer’s Disease
  4. npj Digital Medicine — Spatial Multi-Omics Enhanced by Machine Learning Reveals Lactate-Driven Therapeutic Targets and Reprogramming of the Tumor Microenvironment in Cancer
  5. Intensive Care Medicine — Identifying Common Misconceptions About Lactate Clearance in Sepsis
  6. Critical Care (Springer) — Microvascular phenotypes in pediatric sepsis identified by machine learning: prognostic implications for organ dysfunction and mortality
  7. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2026 | SCCM
  8. Early Restrictive or Liberal Fluid Management for Sepsis-Induced Hypotension - PubMed
  9. Lactate dehydrogenase is an indicator for outcomes of short-term and long-term in septic patients | PLOS One

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