Machine learning-based identification of leptin-associated biomarkers and prognostic prediction models in sepsis

Leptin has been implicated in the prognosis of sepsis, yet its mechanistic role remains unclear. This study aimed to develop leptin-associated diagnostic and prognostic models for sepsis and identify potential biomarkers using machine learning approaches.

Non-negative matrix factorization (NMF) was used to identify leptin-related molecular subtypes of sepsis. Weighted gene co-expression network analysis (WGCNA) determined relevant gene modules and hub genes. Differentially expressed genes (DEGs) between sepsis patients and controls were intersected with WGCNA results to refine key genes. Based on these analyses, a prognostic classification model predicting 28-day mortality was developed using the Least Absolute Shrinkage and Selection Operator and Random Forest algorithms, while a time-to-event prognostic model was constructed with Random Survival Forest and Gradient Boosting Machine. Single-cell RNA sequencing was performed to assess expression patterns of core genes across immune cell types. Expression validation was conducted using qPCR and Western blotting.

Three leptin-associated sepsis subtypes with distinct prognoses were identified. The pink and salmon modules from WGCNA were significantly associated with sepsis. Seventy core genes were selected from the DEGs and WGCNA intersection. The prognostic classification model and the time-to-event prognostic model demonstrated strong predictive performance in both the training and external validation cohorts. TFRC and PILRA were consistently highlighted through machine learning, single-cell data, and experimental validation as potential biomarkers.

We established leptin-related prognostic models for sepsis using integrated machine learning. TFRC and PILRA may serve as promising biomarkers, offering insights into sepsis heterogeneity and clinical management.