Abstract
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.
概要
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。
创新点
鉴定与脓毒症生存预后密切相关的IRRGs,构建风险评分模型。
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 selection operator(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。
结论
利用9个IRRGs构建了与脓毒症28天生存预后有关的风险评估模型,且在GSE95233数据集中进行验证,证实这些IRRGs可作为脓毒症患者的预后生物标志物。
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Acknowledgments
This work was supported by the Key Research and Development Program of Zhejiang Province (No. 2019C03076) and the Opening Foundation of the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases (No. 2018KF02), China.
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Shuai JIANG and Wenyuan ZHANG conceived the idea and performed data analyses, wrote and edited the manuscript. Yuanqiang LU contributed to the study design, data analysis, and writing and editing of the manuscript. All authors have read and approved the final manuscript, and therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data.
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Shuai JIANG, Wenyuan ZHANG, and Yuanqiang LU declare that they have no conflict of interest.
This article does not contain any studies with human or animal subjects performed by any of the authors.
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Jiang, S., Zhang, W. & Lu, Y. Development and validation of novel inflammatory response-related gene signature for sepsis prognosis. J. Zhejiang Univ. Sci. B 23, 1028–1041 (2022). https://doi.org/10.1631/jzus.B2200285
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DOI: https://doi.org/10.1631/jzus.B2200285