Genomic biomarkers from urine cells indicate a unique metabolomic difference between sepsis and sterile inflammation (validation cohort)

Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset. Urine was collected from 41 sepsis patients within 12 hours of sepsis onset. Urine was collected from 39 SIRS patients within 4 hours after end of surgery.脓毒症（Sepsis）是一类时间敏感性疾病，伴随显著的病死率、致残率与医疗成本负担，诊断延迟时其危害尤为突出。临床医师常难以精准区分脓毒症与无菌性全身炎症反应综合征（Systemic Inflammatory Response Syndrome, SIRS），尤其是那些因大型手术引发无菌性组织损伤的患者。脓毒症由宿主对病原体的应答失调所驱动，而SIRS则源于组织损伤。此前已有研究利用全血或血液特定细胞组分的转录组谱分析（Transcriptomic Profiling）挖掘脓毒症与非感染性SIRS之间潜在的病因学差异；基于血液的基因微阵列（Gene Microarrays）也已被证实可有效区分脓毒症与SIRS。临床常规会从重症患者体内采集尿液样本，但尿液脓毒症生物标志物的诊断效用仍未明确。本研究纳入单中心的SIRS与脓毒症患者前瞻性队列，旨在验证如下假说：通过对全基因组转录组尿液RNA特征开展机器学习特征筛选，可识别出在脓毒症发作12小时内区分脓毒症与无菌性SIRS的基因表达模式。本研究共收集41名脓毒症患者在其脓毒症发作后12小时内的尿液样本，以及39名SIRS患者在其手术结束后4小时内的尿液样本。