Identification of necroptotic biomarkers associated with immune microenvironment in sepsis based on the protein–protein interaction network and machine learning

•Machine learning and protein-protein interaction network identify CD40LG, TXN, and AIM2 as the key players of necroptosis in sepsis.•CD40LG, TXN, and AIM2 showed their diagnostic utility in distinguishing sepsis from control samples.•The mRNA levels of CD40LG, TXN, and AIM2 strongly correlated with...

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Published in:Clinica chimica acta Vol. 577; p. 120489
Main Authors: Wu, Mengze, Zou, Zhao, Peng, Yuce, Luo, Suxin
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.09.2025
Subjects:
AIM2
Animals
Biomarkers - analysis
Biomarkers - metabolism
CD40LG
Humans
Immune Infiltration
Machine Learning
Mice
Necroptosis
Necroptosis - genetics
Protein Interaction Maps - genetics
Sepsis
Sepsis - diagnosis
Sepsis - genetics
Sepsis - immunology
Sepsis - metabolism
TXN
PPIN
AUC
NF-κB
TUNEL
Necroptosis
LASSO
iNOS
KEGG
MF
sCD40LG
RT-qPCR
RIPK3
GSEA
IL-1β
MAPK
NRDEGs
PCA
GEO
NETs
AIM2
GSVA
SVM-RFE
mCD40LG
Sepsis
NGS
GAPDH
TLR4
TXN
Machine Learning
BP
LPS
TPM
DEGs
ANOVA
CCL5
RIPK1
FADD
ssGSEA
CC
CCR1
MLKL
PMA
GO
hub-NRDEGs
SALI
Immune Infiltration
CD40LG
NRGs
TNF-α
ISSN:0009-8981, 1873-3492, 1873-3492
Online Access:Get full text
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Summary:•Machine learning and protein-protein interaction network identify CD40LG, TXN, and AIM2 as the key players of necroptosis in sepsis.•CD40LG, TXN, and AIM2 showed their diagnostic utility in distinguishing sepsis from control samples.•The mRNA levels of CD40LG, TXN, and AIM2 strongly correlated with the abundance of most immunocytes in sepsis.•Targeting TXN by PX-12 aggravated the necroptotic process in LPS-induced THP-1-derived macrophages. Necroptosis is inflammatorily sparked and closely associated with sepsis, but the crosstalk between necroptosis and inflammation in sepsis has rarely been studied in depth. This study is designed to reveal the role of necroptosis in the pathogenesis and development of sepsis by screening and validating hub necroptotic septic genes. We obtained datasets from the Gene Expression Omnibus (GEO) database. We screened differentially expressed genes (DEGs) and intersected them with necroptotic genes from the literature. Intersected genes were organized into one protein–protein interaction network (PPIN) and determined by machine learning algorithms as hub necroptotic DEGs (hub-NRDEGs). All hub-NRDEGs were examined for their septic change and diagnostic value and connected with septic changes in the immune microenvironment for their inflammatory roles. Experiments verified these genes using septic murine and cellular models. We obtained 4974 DEGs and concentrated on 336 necroptosis-related genes by the intersection. Experiencing the dual selection of PPIN and machine learning analysis, three hub-NRDEGs, CD40LG, TXN, and AIM2, were identified. Based on their transcriptomic profile, we classified septic samples into two groups, revealing their signalling discrepancy. They correlate with most immunocytes, and their abundance differentiates the immune infiltration widely. Experimental validation converged with the transcriptomic profile of three hub-NRDEGs in septic status. One three-panel necroptotic signature for diagnosing sepsis was proposed. We initially screened the association between hub-NRDEGs and immunocytes, and we validated the expressional change of hub-NRDEGs in the experimental sepsis. Our necroptotic gene signature may contribute to the rapid diagnosis of sepsis.
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content type line 23
ISSN:0009-8981
1873-3492
1873-3492
DOI:10.1016/j.cca.2025.120489