Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are multifactorial but are characterized by an overarching inflammatory response, sharing elements in common with severe inflammatory response syndrome (SIRS) of non-infectious origin. Sepsis presents with a range of pathophysiological and genetic features which make clinical differentiation from SIRS very challenging. This may reflect a poor understanding of the key gene inter-activities and/or pathway associations underlying these disease processes. Improved understanding is critical for early differential recognition of sepsis and SIRS and to improve patient management and clinical outcomes. Judicious selection of gene biomarkers suitable for development of diagnostic tests/testing could make differentiation of sepsis and SIRS feasible. Here we describe a methodologic framework for the identification and validation of biomarkers in SIRS, sepsis and septic shock patients, using a 2-tier gene screening, artificial neural network (ANN) data mining technique, using previously published gene expression datasets. Eight key hub markers have been identified which may delineate distinct, core disease processes and which show potential for informing underlying immunological and pathological processes and thus patient stratification and treatment. These do not show sufficient fold change differences between the different disease states to be useful as primary diagnostic biomarkers, but are instrumental in identifying candidate pathways and other associated biomarkers for further exploration.

中文翻译：

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开发用于鉴定和验证传染性和非传染性严重炎症反应综合征的基因组生物标记物和关键免疫病理过程和控制者的生物信息学框架。

败血症定义为由全身感染导致器官衰竭的宿主反应失调。这是一种危及生命的疾病，通常需要入院重症监护病房（ICU）。涉及的病原体和过程是多因素的，但其特征是总体炎症反应，与非传染性起源的严重炎症反应综合征（SIRS）共有相同的要素。脓毒症具有一系列病理生理学和遗传学特征，这使得与SIRS的临床区分非常具有挑战性。这可能反映出对这些疾病过程背后的关键基因相互作用和/或途径关联的了解不多。更好的理解对于败血症和SIRS的早期差异识别以及改善患者管理和临床结果至关重要。明智地选择适合进行诊断测试/测试的基因生物标记可以使脓毒症和SIRS的分化成为可能。在这里，我们描述了一种使用2层基因筛选，人工神经网络（ANN）数据挖掘技术，使用先前发布的基因表达数据集来识别和验证SIRS，败血症和败血性休克患者中生物标志物的方法框架。已经确定了八个关键的枢纽标志物，这些标志物可以描绘出独特的核心疾病过程，并显示出潜在的潜在基础免疫学和病理学过程的信息，从而为患者的分层和治疗提供信息。这些未显示出不同疾病状态之间足够的倍数变化差异，无法用作主要的诊断生物标记物，