The gut microbiome plays a major role in protecting the host against the overgrowth of pathogens and sustaining the health of colon. There is intensive evidence revealing the close relationship between gut microbiome and colonic disease, such as colorectal cancer [42–44]. In addition to causing intestinal diseases, gut microbiome is also contribute to obesity, diabetes, allergic asthma and neuropsychiatric diseases [45–47], thus, clinical monitoring of fecal bacteria can assist in the diagnosis of other diseases related to gut microbiome. Furthermore, gut status could be improved by artificially guiding the intervention of diet or the intake of beneficial bacteria according to the changes of gut microbiome , and the improvement could be easily detect from the fecal bacteria. Thus, gut microbiome has become a hot spot in the clinical research.
We have performed high-throughput sequencing on the v3-v4 region of intestinal bacteria 16S rRNA gene in stool and described the patterns of gut microbiome relative to health and CRC patients. Fecal richeness from colorectal cancer patients decreased, in addition, the proportion of various beneficial bacteria decreased, and the proportion of harmful bacteria significantly increased. A dozen of opportunistic pathogens including Bacteroides and Prevotella were significantly increased in patients with colorectal cancer. A couple of pathogens, including Fusobacterium nucleatum [34, 35], Peptostreptococcus anaerobius and enterotoxigenic Bacteroides fragilis [25, 29] which have been established the role in CRC induction, were highly accumulated in CRC patients (Fig. 3; Supplemental table 1). The dysbiosis characteristics could be facilitated to term as taxonomic biomarkers for CRC screening.
We performed machine learning using SVM model between pairs of cohorts to conduct binary classification for classifying CRC and control. A variety of features including taxonomic, functional , and k-mer-based  classification schemes has been used for machine learning approaches. Here, we used 40 bacteria genus showing a great contribution to differ the CRC state versus control as features for machine learning. In addition, FOBT test result was selected as a feature as well for its importance on CRC diagnosis in clinical. Our machine learning results showed high performances in CRC versus control models (Table 1). The high performance of fecal bacteria and FOBT test from stool sample facilitates to establish a new non-invasive method for examination of colorectal cancer.
Colonoscopy and FOBT are widely used in CRC screening, however, for their low compliance or sensitivity, more non-invasive and painless methods with high sensitivity and specificity are required [7–13]. Circulating tumor DNA (ctDNA) is extracellular DNA originated from tumor cells and circulates in a number of bodily fluids, including blood, synovial fluid and cerebrospinal fluid . For the similarity of genetic and epigenetic information provides by ctDNA to that of invasive tumor biopsies, ctDNA has been widely used to detected the gene mutation and termed as a non-invasive diagnostic tool for several cancers . In many tumors, increased methylation of tumor suppressor genes occurs at an early stage, thus, ctDNA methylation profiling detection can be used for as an alternative non-invasive diagnostic tool [53–55]. Some specific DNA methylation sites, such as SEPT9 have been identified as biomarkers of CRC [56, 57]. However, the extremely low level in blood and the non-organ information of ctDNA gives a great challenge to early diagnosis.
In clinical application, changes of gut microbiome can be regularly monitored, early detection and treatment of CRC can improve the late survival rate and reduce the cost of late treatment. In this study, we monitored the gut microbiome and took 40 bacteria genus displaying high weight for classification between CRC and healthy gut as biomarkers for CRC early diagnose. Combined with FOBT test, our method showed an excellent performance on CRC early diagnose. The method benefit to those who cannot receive colonoscopy in a short time, and those who are not willing to use colonoscopy. Compared with the existing methods of CRC diagnosis, our method is non-invasive and painless, not only does it not require complex examination and preparation before sampling, but also improves the sensitivity and specificity of the test compared with the FOBT alone.