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Background <p>Sepsis-induced metabolic dysregulation, particularly abnormal lactate metabolism, is closely associated with disease severity and mortality. This study aimed to systematically identify lactate-related genes involved in sepsis and evaluate their diagnostic and prognostic value using integrated transcriptomic analyses.</p> Methods <p>Single-cell RNA sequencing data were analyzed using AUCell, singscore, and ssGSEA algorithms combined with correlation analysis to identify lactate-related genes. A comprehensive machine learning framework incorporating 131 algorithms was applied for feature selection and construction of a diagnostic model. Survival analyses were performed to assess the prognostic significance of candidate genes. The expression of key genes was further validated by qPCR and western blotting in clinical samples and a sepsis animal model.</p> Results <p>Sepsis patients were stratified into distinct molecular subgroups exhibiting significant differences in prognosis, clinical characteristics, pathway enrichment, immune infiltration, and immune checkpoint gene expression. Single-cell analysis revealed that MARCO⁺ macrophages exhibited relatively high lactate-related activity compared with most immune populations, while plasma cells showed comparable levels. Sixteen genes were identified at the intersection of macrophage differentially expressed genes and lactate-related genes. Machine learning analysis further identified six core diagnostic genes. Survival analysis demonstrated that high LDHA expression was significantly associated with poor prognosis in sepsis patients. Consistently, elevated LDHA expression was observed in septic animals compared with controls.</p> Conclusion <p>By integrating single-cell and bulk RNA-seq data, we developed and validated a novel diagnostic model for sepsis, termed the Lacty Model, and identified LDHA as a key biomarker associated with poor prognosis. These findings highlight the potential clinical relevance of lactate metabolism–related genes in sepsis and provide a foundation for future mechanistic and therapeutic studies.</p>

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Integration of single-cell and bulk transcriptomics with machine learning identifies LDHA as a lactate-related diagnostic and prognostic biomarker in sepsis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Zhiying Lin,
  • Hanping Shi,
  • Xiaohong Chen,
  • Chunli Yang

摘要

Background

Sepsis-induced metabolic dysregulation, particularly abnormal lactate metabolism, is closely associated with disease severity and mortality. This study aimed to systematically identify lactate-related genes involved in sepsis and evaluate their diagnostic and prognostic value using integrated transcriptomic analyses.

Methods

Single-cell RNA sequencing data were analyzed using AUCell, singscore, and ssGSEA algorithms combined with correlation analysis to identify lactate-related genes. A comprehensive machine learning framework incorporating 131 algorithms was applied for feature selection and construction of a diagnostic model. Survival analyses were performed to assess the prognostic significance of candidate genes. The expression of key genes was further validated by qPCR and western blotting in clinical samples and a sepsis animal model.

Results

Sepsis patients were stratified into distinct molecular subgroups exhibiting significant differences in prognosis, clinical characteristics, pathway enrichment, immune infiltration, and immune checkpoint gene expression. Single-cell analysis revealed that MARCO⁺ macrophages exhibited relatively high lactate-related activity compared with most immune populations, while plasma cells showed comparable levels. Sixteen genes were identified at the intersection of macrophage differentially expressed genes and lactate-related genes. Machine learning analysis further identified six core diagnostic genes. Survival analysis demonstrated that high LDHA expression was significantly associated with poor prognosis in sepsis patients. Consistently, elevated LDHA expression was observed in septic animals compared with controls.

Conclusion

By integrating single-cell and bulk RNA-seq data, we developed and validated a novel diagnostic model for sepsis, termed the Lacty Model, and identified LDHA as a key biomarker associated with poor prognosis. These findings highlight the potential clinical relevance of lactate metabolism–related genes in sepsis and provide a foundation for future mechanistic and therapeutic studies.