Abstract
BACKGROUND: Colorectal cancer (CRC) is one of the most common malignant tumors in the digestive system worldwide, with its mortality ranking second among all cancers. Studies have indicated that disruptions in circadian rhythm (CR) are associated with the occurrence of various cancers; however, the relationship between CR and CRC requires further evidence, and research on the application of CR in CRC is still limited. METHODS: In this study, we employed both bulk and single-cell RNA sequencing to explore the dysregulation of CR in patients with CRC. By constructing a CR subtype classifier, we conducted an in-depth analysis of the prognostic significance, the status of the tumor microenvironment, and response to immune checkpoint blockade (ICB) therapy between different CR clusters. Furthermore, we developed a CR scoring system (CRS) using machine learning to predict overall survival and identified several genes as potential targets affecting CRC prognosis. RESULTS: Our findings revealed significant alterations in CR genes and status between CRC and normal tissues using bulk and single-cell transcriptome sequencing. Patients with CRC could be categorized into two distinct CR clusters (CR cluster 1 and 2). The prognosis of CR cluster 2, with higher epithelial-mesenchymal transition (EMT) and angiogenesis scores, was significantly worser than that of CR cluster 1. These clusters exhibited distinct levels of tumor-infiltrating lymphocytes. CR cluster 2 with a notably higher proportion of patients with microsatellite-instability-high (MSI-H), potentially benefit from ICB therapy. The proportion of patients belonging to consensus molecular subtype 4 (CMS4) in CR cluster 2 was also notably higher than in CR cluster 1. Additionally, the CRS combined with tumor stage demonstrated superior overall survival prediction efficacy compared to traditional tumor stage. We revealed a potential link between model genes (LSAMP, MS4A2, NAV3, RAB3B, SIX4) and the disruption of CR and patient prognosis. CONCLUSION: This study not only provide new insights into the assessment of CR status in CRC patients but also develop a prognosis model based on CR-related genes, offering a new tool for personalized risk assessment in CRC.