Exploring the Role of Circadian Rhythm-Related Genes in the Identification of Sepsis Subtypes and the Construction of Diagnostic Models Based on RNA-seq and scRNA-seq

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 (<i>ARHGEF18, CHD3, PHC1, SFI1,</i> and <i>SPOCK2</i>). 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.