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Yuan-qiang Lu


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Journal of Zhejiang University SCIENCE B

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Development and validation of novel inflammatory response-related gene signature for sepsis prognosis

Author(s):  Shuai JIANG, Wenyuan ZHANG, Yuanqiang LU

Affiliation(s):  Department of Emergency Medicine, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China; more

Corresponding email(s):  luyuanqiang@zju.edu.cn

Key Words:  Gene signature; Inflammatory response-related gene (IRRG); Prognosis; Immune function; Sepsis

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Shuai JIANG, Wenyuan ZHANG, Yuanqiang LU. Development and validation of novel inflammatory response-related gene signature for sepsis prognosis[J]. Journal of Zhejiang University Science B,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2200285

@article{title="Development and validation of novel inflammatory response-related gene signature for sepsis prognosis",
author="Shuai JIANG, Wenyuan ZHANG, Yuanqiang LU",
journal="Journal of Zhejiang University Science B",
year="in press",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Development and validation of novel inflammatory response-related gene signature for sepsis prognosis
%A Shuai JIANG
%A Wenyuan ZHANG
%A Yuanqiang LU
%J Journal of Zhejiang University SCIENCE B
%P 1028-1041
%@ 1673-1581
%D in press
%I Zhejiang University Press & Springer

T1 - Development and validation of novel inflammatory response-related gene signature for sepsis prognosis
A1 - Shuai JIANG
A1 - Wenyuan ZHANG
A1 - Yuanqiang LU
J0 - Journal of Zhejiang University Science B
SP - 1028
EP - 1041
%@ 1673-1581
Y1 - in press
PB - Zhejiang University Press & Springer
ER -

Due to the low specificity and sensitivity of biomarkers in sepsis diagnostics, the prognosis of sepsis patient outcomes still relies on the assessment of clinical symptoms. Inflammatory response is crucial to sepsis onset and progression; however, the significance of inflammatory response-related genes (IRRGs) in sepsis prognosis is uncertain. This study developed an IRRG-based signature for sepsis prognosis and immunological function. The Gene Expression Omnibus (GEO) database was retrieved for two sepsis microarray datasets, GSE64457 and GSE69528, followed by gene set enrichment analysis (GSEA) comparing sepsis and healthy samples. A predictive signature for IRRGs was created using least absolute shrinkage and selection operator (LASSO). To confirm the efficacy and reliability of the new prognostic signature, Cox regression, Kaplan-Meier (K-M) survival, and receiver operating characteristic (ROC) curve analyses were performed. Subsequently, we employed the GSE95233 dataset to independently validate the prognostic signature. A single-sample GSEA (ssGSEA) was conducted to quantify the immune cell enrichment score and immune-related pathway activity. We found that more gene sets were enriched in the inflammatory response in sepsis patient samples than in healthy patient samples, as determined by GSEA. The signature of nine IRRGs permitted the patients to be classified into two risk categories. Patients in the low-risk group showed significantly better 28-d survival than those in the high-risk group. ROC curve analysis corroborated the predictive capacity of the signature, with the area under the curve (AUC) for 28-d survival reaching 0.866. Meanwhile, the ssGSEA showed that the two risk groups had different immune states. The validation set and external dataset showed that the signature was clinically predictive. In conclusion, a signature consisting of nine IRRGs can be utilized to predict prognosis and influence the immunological status of sepsis patients. Thus, intervention based on these IRRGs may become a therapeutic option in the future.


目的:探讨免疫反应相关的基因(inflammatory response-related genes,IRRGs)在预测脓毒症患者生存预后中的作用。
方法:对Gene Expression Omnibus(GEO)数据库中478例脓毒症患者的微阵列芯片数据进行综合生物信息学分析,利用least absolute shrinkage and selectionoperator(LASSO)-Cox回归分析筛选与脓毒症28天生存率密切相关的IRRGs,并以此构建脓毒症预后风险评分模型。使用受试者工作特征曲线(ROC)及生存曲线(基于Kaplan-Meier法)评估预后风险评分模型的预测效能及区分度,并用GSE95233数据集进行验证。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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