Gut microbes on the risk of advanced adenomas.

Background: More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. Objective: To analyze the characteristic microbes in AA. Methods: Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. Results: The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. Conclusion: Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing. Highlights: The bacteria (including Prevotella sp900557255, Alistipes putredinis, Megamonas funiformis, etc.) and viruses (including Lilyvirus, Felixounavirus, and Drulisvirus, etc.) existed differences in AA. And there were correlations between AA and basic information, lipid index and serological index. Prediction models based on bacteria and viruses were established to distinguish AA, and the accuracy reached 87.27% and 83.33%. A new typing method was established based on bacteria and viruses to divide gut microbes into 4 subtypes. Prediction models after typing had higher accuracy (100% in B1V1, 96.34% in B1V2, 100% in B2V1, 98.46% in B2V2). [ABSTRACT FROM AUTHOR]