Su Qiyuan, Huang Jingtao, Zhang Yunlong, Liu Zhou, Lv Zhihua, Zhang Chunming, Ling Chengxiu, Su Hanwen, Zhan Liying, Zhang Zhengjun
Wisdom Lake Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou, China.
Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Front Med (Lausanne). 2025 Jul 1;12:1521827. doi: 10.3389/fmed.2025.1521827. eCollection 2025.
Sepsis biomarker research over the past 30 years has been plagued by the use of wrong animal models and inappropriate patient selections, leading to the failure of translating findings into precision medicine. Thousands of sepsis-related gene biomarkers have been published, but this excess hinders medical advancement because (1) an overwhelming number of genes make targeted drug development and precision medicine unfeasible; (2) many biomarkers lack cross-cohort validation, rendering them clinically unhelpful. Our goal is to identify a highly informative, single-digit set of sepsis biomarkers to advance precision medicine.
We conducted large-scale research on heterogeneous populations, including patients with sepsis, severe sepsis, and septic shocks, and collected plasma samples from 32 sepsis patients and 18 healthy controls at Renmin Hospital of Wuhan University, China. RNA was isolated using the HYCEZMBIO Serum/Plasma RNA Kit, and RT-qPCR was performed on the Roche Light Cycler 480 platform. An AI-based max-logistic competing classifier was applied across 11 cohorts with thousands of samples, using both self-designed and public datasets to identify the most critical sepsis biomarkers.
Our analysis highlights CKAP4, FCAR, and RNF4 as key genetic drivers in sepsis-related variations. In whole blood, NONO is crucial for immune response, while in plasma, PLEKHO1 and BMP6 reveal further genetic heterogeneities. Pediatric patients also exhibit significant contributions from RNASE2 and OGFOD3. These genes form the most effective miniature set of biomarkers.
Achieving 99.42% accuracy across cohorts, this miniature set outperforms larger published gene sets. These findings provide critical insights for personalized risk assessment, targeted drug development, and tailored treatments for both adult and pediatric sepsis patients.
在过去30年里，脓毒症生物标志物的研究一直受到错误动物模型和不恰当患者选择的困扰，导致研究结果无法转化为精准医学。已经发表了数千种与脓毒症相关的基因生物标志物，但这种过量的情况阻碍了医学进步，原因如下：（1）大量的基因使得靶向药物开发和精准医学变得不可行；（2）许多生物标志物缺乏跨队列验证，在临床上没有帮助。我们的目标是识别一组信息量丰富的、个位数的脓毒症生物标志物，以推动精准医学的发展。
我们对包括脓毒症、严重脓毒症和脓毒性休克患者在内的异质性人群进行了大规模研究，并在中国武汉大学人民医院收集了32例脓毒症患者和18例健康对照的血浆样本。使用HYCEZMBIO血清/血浆RNA试剂盒分离RNA，并在罗氏Light Cycler 480平台上进行RT-qPCR。基于人工智能的最大逻辑竞争分类器应用于11个队列的数千个样本，使用自行设计和公共数据集来识别最关键的脓毒症生物标志物。
我们的分析突出了CKAP4、FCAR和RNF4作为脓毒症相关变异中的关键基因驱动因素。在全血中，NONO对免疫反应至关重要，而在血浆中，PLEKHO1和BMP6显示出进一步的基因异质性。儿科患者中RNASE2和OGFOD3也有显著贡献。这些基因构成了最有效的微型生物标志物集。
该微型生物标志物集在各队列中实现了99.42%的准确率，优于已发表的更大基因集。这些发现为成人和儿科脓毒症患者的个性化风险评估、靶向药物开发和量身定制的治疗提供了关键见解。
