Du Chao, Tan Stephanie C, Bu Heng-Fu, Subramanian Saravanan, Geng Hua, Wang Xiao, Xie Hehuang, Wu Xiaowei, Zhou Tingfa, Liu Ruijin, Xu Zhen, Liu Bing, Tan Xiao-Di
Department of Gastroenterology, Weihai Municipal Hospital of Shandong University, Weihai, Shandong, China.
Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
Front Immunol. 2024 Nov 28;15:1493895. doi: 10.3389/fimmu.2024.1493895. eCollection 2024.
Sepsis 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.
We 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.
Our analysis led to identify six hub genes (, and ) which are related to NK cell cytotoxicity and septic shock, collectively termed 6-HubG. 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-HubG panel, facilitating the identification of newly recruited patients with sepsis and septic shock.
Our 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.
脓毒症是一种危及生命的疾病，每年在全球导致数百万人死亡。对于预测脓毒症进展为感染性休克的生物标志物的需求仍然至关重要，目前仍缺乏快速、可靠的方法。转录组学数据最近已成为疾病表型分析和内型分析的宝贵资源，使其成为预测疾病阶段的有前途的工具。因此，我们旨在建立一个先进的机器学习框架，使用转录组学数据集和快速周转方法来预测脓毒症和感染性休克。
我们检索了四个先前从健康个体以及脓毒症和感染性休克患者的外周血样本中生成的NCBI GEO转录组学数据集。对这些数据集进行生物信息学分析处理，并辅以一系列实验台实验，从而确定了与脓毒症和感染性休克相关的核心基因panel。该核心基因panel用于建立一种新型预测模型，通过多阶段机器学习管道（包括线性判别分析、风险评分分析以及结合最小绝对收缩和选择算子分析的集成方法）来区分脓毒症和感染性休克。最后，我们使用新招募患者的外周血样本通过RT-qPCR生成的核心基因数据集对预测模型进行了验证。
我们的分析确定了六个与自然杀伤细胞细胞毒性和感染性休克相关的核心基因（ 、 和 ），统称为6-HubG。使用该panel，我们创建了SepxFindeR，这是一种机器学习模型，在跨数据库背景下预测脓毒症和感染性休克以及区分感染性休克与脓毒症方面表现出高准确性。值得注意的是，SepxFindeR模型被证明与基于6-HubG panel的RT-qPCR数据集兼容，有助于识别新招募的脓毒症和感染性休克患者。
我们的生物信息学方法导致发现了6-HubGss生物标志物panel并开发了SepxFindeR机器学习模型，能够快速准确地预测感染性休克并将其与脓毒症区分开来。
