基于机器学习分析，识别具有预后和治疗价值的两种稳健的脓毒症亚类。

BACKGROUND: Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed. METHODS AND RESULTS: A total of 1,936 patients (sepsis samples, n=1,692; normal samples, n=244) in 7 discovery datasets were included to conduct weighted gene co-expression network analysis (WGCNA) to filter out candidate genes related to sepsis. Then, two subtypes of sepsis were classified in the training sepsis set (n=1,692), the Adaptive and Inflammatory, using K-means clustering analysis on 90 sepsis-related features. We validated these subtypes using 617 samples in 5 independent datasets and the merged 5 sets. Cibersort method revealed the Adaptive subtype was related to high infiltration levels of T cells and natural killer (NK) cells and a better clinical outcome. Immune features were validated by single-cell RNA sequencing (scRNA-seq) analysis. The Inflammatory subtype was associated with high infiltration of macrophages and a disadvantageous prognosis. Based on functional analysis, upregulation of the Toll-like receptor signaling pathway was obtained in Inflammatory subtype and NK cell-mediated cytotoxicity and T cell receptor signaling pathway were upregulated in Adaptive group. To quantify the cluster findings, a scoring system, called, risk score, was established using four datasets (n=980) in the discovery cohorts based on least absolute shrinkage and selection operator (LASSO) and logistic regression and validated in external sets (n=760). Multivariate logistic regression analysis revealed the risk score was an independent predictor of outcomes of sepsis patients (OR [odds ratio], 2.752, 95% confidence interval [CI], 2.234-3.389, P<0.001), when adjusted by age and gender. In addition, the validation sets confirmed the performance (OR, 1.638, 95% CI, 1.309-2.048, P<0.001). Finally, nomograms demonstrated great discriminatory potential than that of risk score, age and gender (training set: AUC=0.682, 95% CI, 0.643-0.719; validation set: AUC=0.624, 95% CI, 0.576-0.664). Decision curve analysis (DCA) demonstrated that the nomograms were clinically useful and had better discriminative performance to recognize patients at high risk than the age, gender and risk score, respectively. CONCLUSIONS: In-depth analysis of a comprehensive landscape of the transcriptome characteristics of sepsis might contribute to personalized treatments and prediction of clinical outcomes.

背景：脓毒症是一种具有高发病率和死亡率的异质性综合征。目前急需开发出优化且有效的分类方法。

方法和结果：共纳入 7 个发现数据集的 1936 名患者（脓毒症样本，n=1692；正常样本，n=244），采用加权基因共表达网络分析（WGCNA）筛选与脓毒症相关的候选基因。然后，使用 K 均值聚类分析对 90 个与脓毒症相关的特征对训练集（n=1692）中的 2 种脓毒症亚型进行分类，即适应性和炎症性。我们使用 5 个独立数据集和合并的 5 个数据集的 617 个样本对这些亚型进行验证。Cibersort 方法表明，适应性亚型与 T 细胞和自然杀伤（NK）细胞的高浸润水平以及更好的临床结局相关。通过单细胞 RNA 测序（scRNA-seq）分析验证了免疫特征。炎症性亚型与巨噬细胞的高浸润相关，且预后不良。基于功能分析，我们发现炎症性亚型中 Toll 样受体信号通路上调，适应性亚型中 NK 细胞介导的细胞毒性和 T 细胞受体信号通路上调。为了量化聚类结果，我们使用发现队列中的 4 个数据集（n=980）基于最小绝对值收缩和选择算子（LASSO）和逻辑回归建立了一个称为风险评分的评分系统，并在外部集（n=760）中进行验证。多变量逻辑回归分析表明，该评分是脓毒症患者结局的独立预测因子（比值比 [OR]，2.752，95%置信区间 [CI]，2.234-3.389，P<0.001），在调整年龄和性别后依然如此。此外，验证集证实了该评分的性能（OR，1.638，95%CI，1.309-2.048，P<0.001）。最后，列线图显示其具有比风险评分、年龄和性别更高的鉴别潜力（训练集：AUC=0.682，95%CI，0.643-0.719；验证集：AUC=0.624，95%CI，0.576-0.664）。决策曲线分析（DCA）表明，该列线图具有临床实用性，并且比年龄、性别和风险评分更能区分高危患者。

结论：深入分析脓毒症转录组特征的综合图谱可能有助于实现个性化治疗和预测临床结局。