Table 1_Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework.xls

BackgroundSepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcriptomics data has recently emerged as a valuable resource for disease phenotyping and endotyping, making it a promising tool for predicting disease stages. Therefore, we aimed to establish an advanced machine learning framework to predict sepsis and septic shock using transcriptomics datasets with rapid turnaround methods.
MethodsWe retrieved four NCBI GEO transcriptomics datasets previously generated from peripheral blood samples of healthy individuals and patients with sepsis and septic shock. The datasets were processed for bioinformatic analysis and supplemented with a series of bench experiments, leading to the identification of a hub gene panel relevant to sepsis and septic shock. The hub gene panel was used to establish a novel prediction model to distinguish sepsis from septic shock through a multistage machine learning pipeline, incorporating linear discriminant analysis, risk score analysis, and ensemble method combined with Least Absolute Shrinkage and Selection Operator analysis. Finally, we validated the prediction model with the hub gene dataset generated by RT-qPCR using peripheral blood samples from newly recruited patients.
ResultsOur analysis led to identify six hub genes (GZMB, PRF1, KLRD1, SH2D1A, LCK, and CD247) which are related to NK cell cytotoxicity and septic shock, collectively termed 6-HubGss. Using this panel, we created SepxFindeR, a machine learning model that demonstrated high accuracy in predicting sepsis and septic shock and distinguishing septic shock from sepsis in a cross-database context. Remarkably, the SepxFindeR model proved compatible with RT-qPCR datasets based on the 6-HubGss panel, facilitating the identification of newly recruited patients with sepsis and septic shock.
ConclusionsOur bioinformatic approach led to the discovery of the 6-HubGss biomarker panel and the development of the SepxFindeR machine learning model, enabling accurate prediction of septic shock and distinction from sepsis with rapid processing capabilities.

【背景】脓毒症（Sepsis）是一种危及生命的病症，每年在全球造成数百万例死亡。目前仍亟需能够预测脓毒症进展为脓毒性休克的生物标志物，但快速且可靠的检测方法依然匮乏。近年来，转录组学（Transcriptomics）数据已成为疾病表型分型与内型分型的宝贵资源，成为预测疾病分期的极具潜力的工具。为此，本研究旨在构建先进的机器学习框架，结合快速检测流程，利用转录组学数据集预测脓毒症与脓毒性休克。
【方法】本研究检索了四组既往采集自健康个体及脓毒症、脓毒性休克患者外周血样本的NCBI GEO（Gene Expression Omnibus，基因表达综合数据库）转录组学数据集。对上述数据集开展生物信息学分析，并辅以一系列湿实验，最终筛选得到与脓毒症及脓毒性休克相关的核心基因集（hub gene panel）。基于该核心基因集，本研究通过整合线性判别分析、风险评分分析以及结合最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator，LASSO）分析的集成学习方法，构建了一套多阶段机器学习流程，用以建立区分脓毒症与脓毒性休克的新型预测模型。最后，本研究利用新招募患者的外周血样本，通过实时定量聚合酶链反应（RT-qPCR）获取核心基因数据集，对上述预测模型进行验证。
【结果】本研究通过分析筛选得到6个与NK细胞细胞毒性及脓毒性休克相关的核心基因（GZMB、PRF1、KLRD1、SH2D1A、LCK及CD247），将其统称为6-HubGss。基于该核心基因集，本研究构建了机器学习模型SepxFindeR，该模型在跨数据库场景下可精准预测脓毒症与脓毒性休克，并能有效区分脓毒症与脓毒性休克。值得注意的是，SepxFindeR模型与基于6-HubGss基因集的RT-qPCR数据集兼容性良好，可助力快速识别新招募的脓毒症及脓毒性休克患者。
【结论】本研究的生物信息学分析策略成功筛选得到6-HubGss生物标志物集，并开发了SepxFindeR机器学习模型，可实现脓毒性休克的精准预测以及与脓毒症的有效区分，且具备快速检测处理能力。