论文标题
用于败血症诊断的强大且可推广的免疫相关签名
A robust and generalizable immune-relatedsignature for sepsis diagnostics
论文作者
论文摘要
高通量测序可以并行检测数万个基因，从而提供了提高包括败血症在内的多种疾病的诊断准确性的机会，包括败血症，这是对感染的侵略性炎症反应，可能导致器官衰竭和死亡。败血症的早期筛查在诊所中至关重要，但尚无有效的诊断生物标志物。在这里，我们提出了一种新颖的方法，即经常出现的逻辑回归，以从血液转录组数据中识别败血症的诊断生物标志物。一个包括五个免疫相关基因的面板，包括五个与免疫相关的基因，LRRN3，IL2RB，FCER1A，TLR5和S100A12，被确定为败血症的诊断生物标志物（LIFTS）。举起败血症患者与正常对照的患者在高精度（AUROC = 0.9959; ic = [0.9722-1.0]）上跨三个独立平台上的九个验证群体上的患者均优于现有标记。我们的分析确定了一个准确的预测模型和可重复的转录组生物标志物，该模型可以为临床诊断测试和生物机械研究奠定基础。
High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death. Early screening of sepsis is essential in clinic, but no effective diagnostic biomarkers are available yet. Here, we present a novel method, Recurrent Logistic Regression, to identify diagnostic biomarkers for sepsis from the blood transcriptome data. A panel including five immune-related genes, LRRN3, IL2RB, FCER1A, TLR5, and S100A12, are determined as diagnostic biomarkers (LIFTS) for sepsis. LIFTS discriminates patients with sepsis from normal controls in high accuracy (AUROC = 0.9959 on average; IC = [0.9722-1.0]) on nine validation cohorts across three independent platforms, which outperforms existing markers. Our analysis determined an accurate prediction model and reproducible transcriptome biomarkers that can lay a foundation for clinical diagnostic tests and biological mechanistic studies.
