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已发表论文
通过生物信息学和机器学习方法鉴定脓毒症诱导的急性肺损伤生物标志物，并进行实验验证
Authors Luo Y, Xu J, He N, Cao W
Received 3 June 2025
Accepted for publication 30 August 2025
Published 4 October 2025 Volume 2025:18 Pages 13635—13650
DOI https://doi.org/10.2147/JIR.S539899
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Anh Ngo
Yannian Luo,1,* Juan Xu,2,* Nannan He,1 Wen Cao1
1Department of Critical Care Medicine, The Second Hospital of Lanzhou University, Lanzhou, Gansu Province, People’s Republic of China; 2Department of Pediatric Neurology, The Second Hospital of Lanzhou University, Lanzhou, Gansu Province, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Wen Cao, Email smilecao2009@126.com
Background: Sepsis-induced acute lung injury (ALI) remains a life-threatening condition due to the lack of reliable early diagnostic biomarkers. Machine learning offers powerful tools for analyzing high-dimensional gene expression data and identifying potential biomarkers and therapeutic targets.
Methods: Five datasets (GSE10474, GSE32707, GSE66890, GSE10361, GSE3037) were obtained from the GEO database. After assessment and normalization, GSE10474, GSE32707, and GSE66890 were combined as a training set to identify differentially expressed genes (DEGs). DEGs were intersected with genes from key modules identified by weighted gene co-expression network analysis (WGCNA), yielding 213 overlapping genes. These were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Eight machine learning algorithms (RF, SVM, GLM, GBM, KNN, NNET, LASSO, DT) were used to develop diagnostic models, which were validated on GSE10361 and GSE3037. Model performance was evaluated using a nomogram, calibration curves, and decision curve analysis (DCA). Immune and inflammatory states were assessed using the CIBERSORT algorithm. Potential therapeutic compounds were identified through the DSigDB database via the Enrichr platform. Molecular docking and molecular dynamics simulations examined interactions between Resveratrol and selected targets. In vitro experiments validated these findings.
Results: A total of 213 candidate genes were identified by intersecting DEGs with WGCNA-derived MEblue module genes. GO and KEGG analyses indicated associations with immune activation and bacterial infection. Four key genes (DDAH2, PNPLA2, STXBP2, TCN1) were selected using eight machine learning algorithms. The diagnostic model showed good performance via nomogram, calibration curve, and DCA. Molecular docking revealed stable binding of Resveratrol to these genes. In vitro, Resveratrol pretreatment alleviated LPS-induced ALI by modulating the core genes.
Conclusion: The four genes may serve as diagnostic biomarkers for sepsis-ALI. Resveratrol represents a potential therapeutic strategy by targeting these genes.
Keywords: sepsis-induced ALI, machine learning algorithms, WGCNA analysis, CIBERSORT analysis, molecular docking
1Department of Critical Care Medicine, The Second Hospital of Lanzhou University, Lanzhou, Gansu Province, People’s Republic of China; 2Department of Pediatric Neurology, The Second Hospital of Lanzhou University, Lanzhou, Gansu Province, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Wen Cao, Email smilecao2009@126.com
Background: Sepsis-induced acute lung injury (ALI) remains a life-threatening condition due to the lack of reliable early diagnostic biomarkers. Machine learning offers powerful tools for analyzing high-dimensional gene expression data and identifying potential biomarkers and therapeutic targets.
Methods: Five datasets (GSE10474, GSE32707, GSE66890, GSE10361, GSE3037) were obtained from the GEO database. After assessment and normalization, GSE10474, GSE32707, and GSE66890 were combined as a training set to identify differentially expressed genes (DEGs). DEGs were intersected with genes from key modules identified by weighted gene co-expression network analysis (WGCNA), yielding 213 overlapping genes. These were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Eight machine learning algorithms (RF, SVM, GLM, GBM, KNN, NNET, LASSO, DT) were used to develop diagnostic models, which were validated on GSE10361 and GSE3037. Model performance was evaluated using a nomogram, calibration curves, and decision curve analysis (DCA). Immune and inflammatory states were assessed using the CIBERSORT algorithm. Potential therapeutic compounds were identified through the DSigDB database via the Enrichr platform. Molecular docking and molecular dynamics simulations examined interactions between Resveratrol and selected targets. In vitro experiments validated these findings.
Results: A total of 213 candidate genes were identified by intersecting DEGs with WGCNA-derived MEblue module genes. GO and KEGG analyses indicated associations with immune activation and bacterial infection. Four key genes (DDAH2, PNPLA2, STXBP2, TCN1) were selected using eight machine learning algorithms. The diagnostic model showed good performance via nomogram, calibration curve, and DCA. Molecular docking revealed stable binding of Resveratrol to these genes. In vitro, Resveratrol pretreatment alleviated LPS-induced ALI by modulating the core genes.
Conclusion: The four genes may serve as diagnostic biomarkers for sepsis-ALI. Resveratrol represents a potential therapeutic strategy by targeting these genes.
Keywords: sepsis-induced ALI, machine learning algorithms, WGCNA analysis, CIBERSORT analysis, molecular docking