Abstract
Colorectal cancer (CRC) is the third most common cancer, and it can be prevented by performing early screening. As a hallmark of cancer, the human microbiome plays important roles in the occurrence and development of CRC. Recently, the blood microbiome has been proposed as an effective diagnostic tool for various diseases, yet its performance on CRC deserves further exploration. In this study, 133 human feces and 120 blood samples are collected, including healthy individuals, adenoma patients, and CRC patients. The blood cfDNA and fecal genome are subjected to shotgun metagenome sequencing. After removing human sequences, the microbial sequences in blood are analyzed. Based on the differential microbes and functions, random forest (RF) models are constructed for adenoma and CRC diagnosis. The results show that alterations of blood microbial signatures can be captured under low coverage (even at 3×). RF diagnostic models based on blood microbial markers achieve high area under the curve (AUC) values for adenoma patients (0.8849) and CRC patients (0.9824). When the fragmentation pattern is combined with microbial and KEGG markers, higher AUC values are obtained. Furthermore, compared to the blood microbiome, the fecal microbiome shows a different community composition, whereas their changes in KEGG pathways are similar. Pathogenic bacteria Fusobacterium nucleatum (F. nucleatum) in feces increased gradually from the healthy group to the adenoma and CRC groups. Additionally, F. nucleatum in feces and blood shows a positive correlation in CRC patients. Cumulatively, the integration of blood microbiome and fragmentation pattern is promising for CRC diagnosis.IMPORTANCEThe cell-free DNA of the human microbiome can enter the blood and can be used for cancer diagnosis, whereas its diagnostic potential in colorectal cancer and association with gut microbiome has not been explored. The microbial sequences in blood account for less than 1% of the total sequences. The blood microbial composition, KEGG functions, and fragmentation pattern are different among healthy individuals, adenoma patients, and CRC patients. Machine learning models based on these differential characteristics achieve high diagnostic accuracy, especially when they are integrated with fragmentation patterns. The great difference between fecal and blood microbiomes indicates that microbial sequences in blood may originate from various organs. Therefore, this study provides new insights into the community composition and functions of the blood microbiome of CRC and proposes an effective non-invasive diagnostic tool.