Wang Xuesong, Guo Zhe, Wang Ziwen, Wang Xinrui, Xia Yuxiang, Wu Dishan, Wang Zhong
Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100084, China.
Int J Mol Sci. 2025 Apr 23;26(9):3993. doi: 10.3390/ijms26093993.
Sepsis is a severe systemic response to infection that may lead to the dysfunction of multiple organ systems and may even be life-threatening. Circadian rhythm-related genes (CRDRGs) regulate the circadian clock and affect many physiological processes, including immune responses. In patients with sepsis, circadian rhythms may be disrupted, thus leading to problems such as immune responses. RNA-seq datasets of sepsis and control groups were downloaded from the Gene Expression Omnibus (GEO) database, and two sepsis subtypes were identified based on differentially expressed CRDRGs. Two gene modules related to sepsis diagnosis and subtypes were obtained using the weighted co-expression network (WGCNA) algorithm. Subsequently, using four machine learning algorithms (random forest, support vector machine, a generalized linear model, and xgboost), genes related to sepsis diagnosis were identified from the intersection genes of the two modules, and a diagnostic model was constructed. Single-cell sequencing (scRNA-seq) data were obtained from the GEO database to explore the expression landscape of diagnostic-related genes in different cell types. Finally, an RT-qPCR analysis of diagnosis-related genes confirmed the differences in expression trends between the two groups. Multiple differentially expressed CRDRGs were observed in the sepsis and control groups, and two subtypes were identified based on their expression levels. There were apparent differences in the distribution of samples of the two subtypes in two-dimensional space and the pathways involved. Using multiple machine learning algorithms, the intersection genes in the two most relevant modules of the WGCNA were identified, and a robust diagnostic model was constructed with five genes ( and ). The AUC of this model reached 0.987 on the validation set, showing an excellent prediction performance. In this study, two sepsis subtypes were identified, and a sepsis diagnostic model was constructed via consensus clustering and machine learning algorithms. Five genes were identified as diagnostic markers for sepsis and can thus assist in clinical diagnosis and guide personalized treatment.
脓毒症是一种对感染的严重全身反应，可能导致多器官系统功能障碍，甚至危及生命。昼夜节律相关基因（CRDRGs）调节生物钟并影响许多生理过程，包括免疫反应。在脓毒症患者中，昼夜节律可能被打乱，从而导致免疫反应等问题。从基因表达综合数据库（GEO）下载脓毒症组和对照组的RNA测序数据集，并基于差异表达的CRDRGs识别出两种脓毒症亚型。使用加权共表达网络（WGCNA）算法获得了与脓毒症诊断和亚型相关的两个基因模块。随后，使用四种机器学习算法（随机森林、支持向量机、广义线性模型和XGBoost），从两个模块的交集基因中识别出与脓毒症诊断相关的基因，并构建了一个诊断模型。从GEO数据库获取单细胞测序（scRNA-seq）数据，以探索不同细胞类型中诊断相关基因的表达情况。最后，对诊断相关基因进行逆转录定量PCR（RT-qPCR）分析，证实了两组之间表达趋势的差异。在脓毒症组和对照组中观察到多个差异表达的CRDRGs，并根据其表达水平识别出两种亚型。这两种亚型的样本在二维空间中的分布和涉及的途径存在明显差异。使用多种机器学习算法，识别出WGCNA两个最相关模块中的交集基因，并构建了一个由五个基因组成的稳健诊断模型（和）。该模型在验证集上的曲线下面积（AUC）达到0.987，显示出优异的预测性能。在本研究中，识别出两种脓毒症亚型，并通过共识聚类和机器学习算法构建了脓毒症诊断模型。确定了五个基因作为脓毒症的诊断标志物，因此可以协助临床诊断并指导个性化治疗。
