ORIGINAL RESEARCH article
Front. Cell. Infect. Microbiol.
Sec. Clinical and Diagnostic Microbiology and Immunology
Volume 15 - 2025 | doi: 10.3389/fcimb.2025.1630446
This article is part of the Research TopicAdvancements in Sepsis Diagnosis Utilizing Next-Generation Sequencing Approaches for Personalized MedicineView all 18 articles
Machine Learning-Based Identification of Leptin-Associated Biomarkers and Prognostic Prediction Models in Sepsis
Provisionally accepted
- Sichuan Provincial People's Hospital, Chengdu, China
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Background: 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. Methods: 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. Results: 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 DEG 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. Conclusion: 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.
Keywords: Leptin, Sepsis, machine learning, prognosis, diagnosis
Received: 17 May 2025; Accepted: 12 Sep 2025.
Copyright: © 2025 Liu, Song, Liao, Yang, Jiang and Zuo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qiunan Zuo, zuoqiunan@med.uestc.edu.cn
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