An In-Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single-Cell...

Area of Science:

• Computational biology
• Immunology
• Genomics

Background:

• Sepsis remains a leading cause of mortality worldwide, necessitating novel diagnostic and therapeutic strategies.
• Identifying reliable sepsis biomarkers is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

• To discover novel sepsis-related biomarkers through in-silico analysis of single-cell RNA sequencing (sc-RNA) data.
• To identify key genes and cellular interactions involved in sepsis pathogenesis.

Main Methods:

• Acquisition of sc-RNA data and metabolism-related genes from public databases.
• Identification and annotation of cell subpopulations, followed by single-sample geneset enrichment analysis (ssGSEA) and differential gene expression analysis.
• Application of weighted gene co-expression network analysis (WGCNA) and immune infiltration analysis to identify key gene modules and hub genes.

Main Results:

• Five distinct cell subpopulations were identified, with specific enrichment for immune-related functions like antigen processing and leukocyte activation.
• Analysis revealed complex intercellular communication networks involving galectin 9 (LGALS9) and Macrophage Migration Inhibitory Factor (MIF).
• Six hub genes (FBXO4, FOXK1, MSH2, NSA2, TMEM128, SBDS) were identified as potential sepsis biomarkers, showing correlations with immune cell types.

Conclusions:

• The identified hub genes represent promising biomarkers for sepsis diagnosis and prognosis.
• Understanding the identified cellular communication pathways may offer new therapeutic targets for sepsis treatment.
• This study provides a computational framework for biomarker discovery in complex diseases like sepsis.