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Machine Learning Screening and Validation of PANoptosis-Related Gene Signatures in Sepsis

  • 0Department of Intensive Care Unit, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Journal of Inflammation Research +

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July 25, 2024

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July 25, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

PANoptosis, a cell death process, is linked to sepsis, impacting immune responses. Identifying PANoptosis-related genes offers new diagnostic and therapeutic strategies for sepsis.

Area Of Science

  • Immunology
  • Molecular Biology
  • Genetics

Background

  • Sepsis is a life-threatening condition characterized by organ dysfunction and immune dysregulation.
  • PANoptosis, involving interconnected programmed cell death pathways, is a novel concept with an unclear role in sepsis.
  • Understanding PANoptosis in sepsis is crucial for developing new treatment strategies.

Purpose Of The Study

  • To investigate the role of PANoptosis-related genes (PRGs) in sepsis.
  • To identify immune characteristics associated with PRGs in sepsis.
  • To develop a predictive model for sepsis based on PRGs.

Main Methods

  • Utilized the GSE65682 dataset to identify PRGs and immune characteristics.
  • Employed ConsensusClusterPlus for sepsis sample classification based on PRGs.
  • Applied Weighted Gene Co-Expression Network Analysis (WGCNA) to identify hub genes.
  • Developed and validated machine learning models, including SVM, for sepsis prediction.

Main Results

  • PRG expression was dysregulated in sepsis patients, correlating with immune cell infiltration.
  • Two distinct PANoptosis-related clusters were identified, linked to immune pathways.
  • The SVM model demonstrated high predictive accuracy (AUC=0.967 internally, 0.989 externally) and was validated through nomogram and survival analysis.

Conclusions

  • PANoptosis is intricately associated with sepsis, influencing immune responses.
  • PRGs serve as potential biomarkers for sepsis diagnosis.
  • This study provides insights into potential therapeutic targets for sepsis.

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