Identification of pyroptosis-related hub genes and construction of a diagnostic model for acute liver failure based on integrated bioinformatics and machine learning.
- 1Department of Infectious Diseases, The First Affiliated Hospital of Nanchang University, Nanchang, China.
- 2Department of Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
- 3Department of Infectious Diseases, The First Affiliated Hospital of Nanchang University, Nanchang, China. [email protected].
Abstract
Background
Acute liver failure (ALF) is a life-threatening syndrome marked by massive hepatocyte loss and dysregulated inflammation. Although pyroptosis, an inflammatory mode of programmed cell death, is thought to contribute to ALF pathogenesis, its exact role has yet to be fully defined.Aims
This study aimed to identify key pyroptosis-related genes in ALF through integrated bioinformatics analysis and machine learning approaches and to provide a molecular basis for potential risk prediction.Methods
Expression profiling datasets related to acute liver failure (GSE14668 and GSE38941) were obtained from the Gene Expression Omnibus (GEO) database, followed by batch effect correction and differential expression analysis. The resulting differentially expressed genes were intersected with pyroptosis‑related genes retrieved from the GeneCards database and published literature. Weighted gene co‑expression network analysis (WGCNA) was performed to identify ALF‑associated co‑expression modules. Candidate hub genes were subsequently prioritized through protein-protein interaction (PPI) network analysis and random forest classification. LASSO logistic regression was employed to further refine the gene signature, and a nomogram model was constructed based on the selected key genes. External validation was conducted using independent hepatic cohorts (GSE62029, GSE120652) and a whole-blood cohort (GSE142255), with calibration curves and decision curve analysis further confirming the model's robustness in the hepatic datasets. Additionally, the expression levels of the key genes were preliminarily validated in a lipopolysaccharide (LPS) plus nigericin‑induced hepatocyte pyroptosis model using CCK‑8 and RT‑qPCR assays.Results
A total of 2,849 differentially expressed genes were identified. Intersecting these with 84 pyroptosis‑related genes and the core module genes derived from WGCNA yielded 11 candidate genes, from which nine hub genes were subsequently prioritized: GZMB, IL18, PYCARD, GZMA, NLRC4, ZBP1, TLR4, AIM2, and CASP1. Functional enrichment analysis revealed that these genes were predominantly involved in inflammasome assembly, regulation of IL‑1β production, and pyroptosis, and were significantly enriched in the NOD‑like receptor signaling pathway. LASSO regression further refined the signature to four genes: GZMB, IL18, NLRC4, and ZBP1. A nomogram constructed from these four genes achieved an AUC of 1.0 in the training cohort and 0.988 in the combined liver‑tissue external validation cohorts, with calibration curves and decision curve analysis further confirming its robustness. RT‑qPCR analysis confirmed that the expression levels of IL18 and ZBP1 were significantly elevated in an LPS plus Nigericin‑induced hepatocyte pyroptosis model, whereas GZMB and NLRC4 showed no significant changes. This differential pattern suggests that the four‑gene signature reflects immune-hepatocyte crosstalk in the complex inflammatory microenvironment of ALF, rather than purely hepatocyte‑autonomous pyroptosis, highlighting the need for further functional studies.Conclusions
This study reveals the potential pivotal role of a pyroptosis‑related gene signature centered on GZMB, IL18, NLRC4, and ZBP1 in the pathogenesis and progression of acute liver failure. The nomogram model constructed from these four genes demonstrates excellent predictive performance, offering a novel molecular basis for the targeted therapeutic intervention of ALF.
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BibTeX
@article{peng2026identification,
title = {{Identification of pyroptosis-related hub genes and construction of a diagnostic model for acute liver failure based on integrated bioinformatics and machine learning.}},
author = {Yuchen Peng and Liping Liu and Xiaoping Wu},
journal = {BMC gastroenterology},
year = {2026},
month = aug,
doi = {10.1186/s12876-026-05181-x},
url = {https://europepmc.org/article/MED/42968014},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Identification of pyroptosis-related hub genes and construction of a diagnostic model for acute liver failure based on integrated bioinformatics and machine learning.
AU - Yuchen Peng
AU - Liping Liu
AU - Xiaoping Wu
PY - 2026
DA - 2026/08/04
JO - BMC gastroenterology
DO - 10.1186/s12876-026-05181-x
UR - https://europepmc.org/article/MED/42968014
AB - Background Acute liver failure (ALF) is a life-threatening syndrome marked by massive hepatocyte loss and dysregulated inflammation. Although pyroptosis, an inflammatory mode of programmed cell death, is thought to contribute to ALF pathogenesis, its exact role has yet to be fully defined.Aims This study aimed to identify key pyroptosis-related genes in ALF through integrated bioinformatics analysis and machine learning approaches and to provide a molecular basis for potential risk prediction.Methods Expression profiling datasets related to acute liver failure (GSE14668 and GSE38941) were obtained from the Gene Expression Omnibus (GEO) database, followed by batch effect correction and differential expression analysis. The resulting differentially expressed genes were intersected with pyroptosis‑related genes retrieved from the GeneCards database and published literature. Weighted gene co‑expression network analysis (WGCNA) was performed to identify ALF‑associated co‑expression modules. Candidate hub genes were subsequently prioritized through protein-protein interaction (PPI) network analysis and random forest classification. LASSO logistic regression was employed to further refine the gene signature, and a nomogram model was constructed based on the selected key genes. External validation was conducted using independent hepatic cohorts (GSE62029, GSE120652) and a whole-blood cohort (GSE142255), with calibration curves and decision curve analysis further confirming the model's robustness in the hepatic datasets. Additionally, the expression levels of the key genes were preliminarily validated in a lipopolysaccharide (LPS) plus nigericin‑induced hepatocyte pyroptosis model using CCK‑8 and RT‑qPCR assays.Results A total of 2,849 differentially expressed genes were identified. Intersecting these with 84 pyroptosis‑related genes and the core module genes derived from WGCNA yielded 11 candidate genes, from which nine hub genes were subsequently prioritized: GZMB, IL18, PYCARD, GZMA, NLRC4, ZBP1, TLR4, AIM2, and CASP1. Functional enrichment analysis revealed that these genes were predominantly involved in inflammasome assembly, regulation of IL‑1β production, and pyroptosis, and were significantly enriched in the NOD‑like receptor signaling pathway. LASSO regression further refined the signature to four genes: GZMB, IL18, NLRC4, and ZBP1. A nomogram constructed from these four genes achieved an AUC of 1.0 in the training cohort and 0.988 in the combined liver‑tissue external validation cohorts, with calibration curves and decision curve analysis further confirming its robustness. RT‑qPCR analysis confirmed that the expression levels of IL18 and ZBP1 were significantly elevated in an LPS plus Nigericin‑induced hepatocyte pyroptosis model, whereas GZMB and NLRC4 showed no significant changes. This differential pattern suggests that the four‑gene signature reflects immune-hepatocyte crosstalk in the complex inflammatory microenvironment of ALF, rather than purely hepatocyte‑autonomous pyroptosis, highlighting the need for further functional studies.Conclusions This study reveals the potential pivotal role of a pyroptosis‑related gene signature centered on GZMB, IL18, NLRC4, and ZBP1 in the pathogenesis and progression of acute liver failure. The nomogram model constructed from these four genes demonstrates excellent predictive performance, offering a novel molecular basis for the targeted therapeutic intervention of ALF.
SN - 1471-230X
ER -