Machine learning-based transcriptomic analysis identifies candidate genes in sepsis-induced coagulopathy and explores the immunomodulatory potential of baicalein.

Background: Sepsis is a major contributor to high morbidity and mortality, often leading to coagulation disorders (CD) in affected individuals. Baicalein, a natural compound with well-established anti-inflammatory properties, shows promise as a potential treatment for sepsis. However, its molecular mechanisms in sepsis-associated CD remain poorly understood. This study investigated the therapeutic effects of baicalein in sepsis and identified candidate genes involved in its mechanism of action.

Methods: Transcriptomic data, baicalein-related targets from public databases, and CD-related genes from the literature were analyzed to identify potential candidate genes. Machine learning algorithms and expression validation techniques were employed to screen initial candidate genes from the candidates. A nomogram was then constructed based on these candidate genes. Functional enrichment and immune infiltration analyses were conducted to explore the underlying mechanisms, while molecular docking was used to assess interactions between baicalein and the candidate genes. Gene expression was further validated by reverse transcription-quantitative PCR (RT-qPCR).

Results: Seven initial candidate genes were identified. Machine learning and expression validation confirmed MMP9, ARG1, and FYN as the final candidate genes involved in sepsis. A highly accurate nomogram, constructed using these candidate genes, demonstrated strong predictive value for sepsis diagnosis. Functional enrichment analysis revealed their pivotal roles in sepsis pathogenesis, while immune infiltration analysis indicated immune dysregulation in sepsis. Additionally, molecular docking revealed strong binding interactions between baicalein and proteins encoded by these candidate genes, supporting further investigation of its therapeutic potential in sepsis. However, these in silico findings are preliminary and require validation through in vitro and in vivo experiments to confirm biological activity. RT-qPCR further validated differential expression of these genes in patients with sepsis compared to healthy controls, confirming the results.

Conclusion: This study identified MMP9, ARG1, and FYN as candidate genes in sepsis involved in immune regulation. Additionally, molecular docking revealed strong binding interactions between baicalein and the proteins encoded by these candidate genes, supporting further investigation of its therapeutic potential in sepsis.