Mekata Kengo, Kyo Michihito, Tan Modong, Shime Nobuaki, Hirohashi Nobuyuki
Department of Radiation Disaster Medicine, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
J Intensive Care. 2025 May 30;13(1):30. doi: 10.1186/s40560-025-00802-1.
The heterogeneity of host responses in sepsis has hindered efforts to develop targeted therapies for this large patient population. Although growing evidence has identified sepsis endotypes based on the microarray data, studies using RNA-seq data-which offers higher sensitivity and a broader dynamic range-remain limited. We hypothesized that integrating RNA-seq data from patients with sepsis would reveal molecular endotypes with distinct biological and clinical signatures.
In this meta-analysis, we systematically searched for publicly available RNA-seq datasets of sepsis. Using identified datasets, we applied a consensus clustering algorithm to identify distinct endotypes. To characterize the biological differences between these endotypes, we performed gene-set enrichment analysis and immune cell deconvolution. Next, we investigated the association between these endotypes and mortality risks. We finally developed gene classifiers for endotype stratification and validated our endotype classification by applying these classifiers to an external cohort.
A total of 280 adults with sepsis from four datasets were included in this analysis. Using an unsupervised approach, we identified three distinct endotypes: coagulopathic (n = 83, 30%), inflammatory (n = 118, 42%), and adaptive endotype (n = 79, 28%). The coagulopathic endotype exhibited upregulated coagulation signaling, along with an increased monocyte and neutrophil composition, although the adaptive endotype demonstrated enhanced adaptive immune cell responses, marked by elevated T and B cell compositions. The inflammatory endotype was characterized by upregulated TNF-α/NF-κB signaling and IL-6/JAK/STAT3 pathways with an increased neutrophil composition. Patients with the coagulopathic endotype had a significantly higher risk of mortality than those with the adaptive endotype (30% vs. 16%, odds ratio 2.19, 95% confidence interval 1.04-4.78, p = 0.04). To enable the practical application of these findings, we developed endotype classification models and identified 14 gene classifiers. In a validation cohort of 123 patients, we consistently identified these three endotypes. Furthermore, the mortality risk pattern was reproduced, with the coagulopathic endotype showing greater mortality risk than the adaptive endotype (34% vs 18%, p = 0.10).
This multicohort RNA-seq meta-analysis identified three biologically and clinically distinct sepsis endotypes characterized by coagulopathic, adaptive, and inflammatory responses. This endotype-based approach to patient stratification may facilitate the development of more precise therapeutic strategies for sepsis.
脓毒症患者宿主反应的异质性阻碍了针对这一庞大患者群体开发靶向治疗方法的努力。尽管越来越多的证据已基于微阵列数据确定了脓毒症的内型，但使用RNA测序数据（其具有更高的灵敏度和更宽的动态范围）的研究仍然有限。我们假设整合脓毒症患者的RNA测序数据将揭示具有不同生物学和临床特征的分子内型。
在这项荟萃分析中，我们系统地搜索了公开可用的脓毒症RNA测序数据集。使用已确定的数据集，我们应用了一致性聚类算法来识别不同的内型。为了表征这些内型之间的生物学差异，我们进行了基因集富集分析和免疫细胞反卷积。接下来，我们研究了这些内型与死亡风险之间的关联。我们最终开发了用于内型分层的基因分类器，并通过将这些分类器应用于外部队列来验证我们的内型分类。
本分析共纳入了来自四个数据集的280名成年脓毒症患者。使用无监督方法，我们确定了三种不同的内型：凝血病型（n = 83，30%）、炎症型（n = 118，42%）和适应性内型（n = 79，28%）。凝血病型内型表现出凝血信号上调，同时单核细胞和中性粒细胞组成增加，而适应性内型则表现出适应性免疫细胞反应增强，以T细胞和B细胞组成增加为特征。炎症型内型的特征是TNF-α/NF-κB信号通路和IL-6/JAK/STAT3通路上调，中性粒细胞组成增加。凝血病型内型的患者死亡风险显著高于适应性内型患者（30%对16%，优势比2.19，95%置信区间1.04 - 4.78，p = 0.04）。为了使这些发现能够实际应用，我们开发了内型分类模型并确定了14个基因分类器。在一个123名患者的验证队列中，我们一致识别出了这三种内型。此外，死亡风险模式得以重现，凝血病型内型的死亡风险高于适应性内型（34%对18%，p = 0.10）。
这项多队列RNA测序荟萃分析确定了三种在生物学和临床上不同的脓毒症内型，其特征为凝血病性、适应性和炎症反应。这种基于内型的患者分层方法可能有助于为脓毒症开发更精确的治疗策略。
