脓毒症预后的新型炎症反应相关基因标记的开发和验证。

Shuai Jiang, Wenyuan Zhang, Yuanqiang Lu
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引用次数: 2

摘要

由于生物标志物在脓毒症诊断中的特异性和敏感性较低,脓毒症患者预后的预后仍然依赖于对临床症状的评估。炎症反应对败血症的发生和发展至关重要;然而,炎症反应相关基因(IRRGs)在脓毒症预后中的意义尚不确定。本研究建立了一种基于irrg的脓毒症预后和免疫功能指标。检索两个败血症微阵列数据集GSE64457和GSE69528的基因表达Omnibus (GEO)数据库,然后进行基因集富集分析(GSEA)比较败血症和健康样本。使用最小绝对收缩和选择算子(LASSO)创建了IRRGs的预测特征。为了确认新预后特征的有效性和可靠性,进行了Cox回归、Kaplan-Meier (K-M)生存和受试者工作特征(ROC)曲线分析。随后,我们使用GSE95233数据集独立验证预后特征。单样本GSEA (ssGSEA)用于量化免疫细胞富集评分和免疫相关途径活性。根据GSEA测定,我们发现脓毒症患者样本中炎症反应中富集的基因组多于健康患者样本。9个IRRGs的签名允许将患者分为两个风险类别。低危组患者28 d生存率明显高于高危组。ROC曲线分析证实了该特征的预测能力,28 d生存的曲线下面积(AUC)达到0.866。同时,ssGSEA显示两个风险组具有不同的免疫状态。验证集和外部数据集表明该特征具有临床预测性。综上所述,一个由9个IRRGs组成的特征可以用来预测脓毒症患者的预后和影响免疫状态。因此,基于这些IRRGs的干预可能在未来成为一种治疗选择。
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Development and validation of novel inflammatory response-related gene signature for sepsis prognosis.

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.

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