Pediatric Sepsis Biomarker Candidates from Multi-Dataset RNA-Seq Analysis

Weidong Ye's 2026 study provides a list of 237 high-confidence gene biomarkers for pediatric sepsis. The analysis integrates six bulk RNA-seq datasets covering 497 pediatric sepsis patients and 116 healthy control samples. Biomarkers were identified using weighted gene co-expression network analysis and 14 machine learning models.

Use Cases

• Validate biomarker candidates like RORA and GPR183 in independent sepsis gene expression datasets.
• Analyze correlations between biomarker expression levels and immune cell infiltration metrics, such as CD8+ T cell counts.
• Prioritize genes for functional studies based on their prominence across multiple machine learning models.
• Use the identified gene modules to build classifiers for distinguishing pediatric sepsis patients from healthy controls.

Strengths

• Integrated data from 613 total samples (497 patients, 116 controls) across six source datasets.
• Identified 237 specific gene biomarkers through a multi-model machine learning approach.
• Includes functional and immune infiltration analysis supporting biomarker roles.

Limitations

• Dataset is a summary table (11.6 KB), not the raw RNA-seq expression data.
• Sample size and demographic details of the integrated patient cohorts are not provided.
• Biomarker performance metrics (e.g., AUC, sensitivity) from the 14 models are not included.

Provenance

Source

Weidong Ye via figshare.

Collection Method

Bioinformatics analysis integrating six public bulk RNA-seq datasets related to pediatric sepsis.

Time Range

null

Freshness

Last updated in April 2026.

Geography

null