Gut microbes on the risk of advanced adenomas

Abstract

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.

Keywords: Advanced adenomas; Enteroviruses; Gut bacteria; Metagenomic sequencing; Prediction model.

Fig. 1

Gut bacteria in AA: Descriptive analysis, diversity analysis, differential analysis, correlation analysis and modeling were used for gut bacteria between AA and NC: Histogram of the bacterial relative abundance (A), NMDS and PCA analysis (B), Box diagram (C), Chord chart (D), T-test (E), and CatBoost model (F)

Fig. 2

EVs in AA: Descriptive analysis, diversity analysis, differential analysis, correlation analysis and modeling were used for EVs between AA and NC: Histogram of the viral relative abundance (A), NMDS and PCA analysis (B), Box diagram (C), Chord chart (D), T-test (E), and CatBoost model (F)

Fig. 3

Correlation among gut bacteria, EVs, and clinical information: Correlation heat maps were used to show the relationship between gut microbes and clinical information in different groups, including NC + AA group, NC group and AA group: Heatmaps of gut bacteria (A-C), Heatmaps of EVs (D-F), and Heatmaps of gut bacteria and viruses (G-I).

Fig. 4

Typing and analysis of gut microbes: Differential gut bacteria and viruses were used for gut typing, and finally 4 subtypes were obtained via unsupervised clustering. The proportions of gut bacteria and viruses were analyzed, and gut microbes with the most significant differences in proportions were screened among 4 subtypes: Unsupervised clustering (A), Stacked bar graph of the top 20 abundant gut bacteria in the 4 subtypes, 2 viral subtypes and 2 bacterial subtypes (B), Box diagram of the top 20 abundant gut bacteria in the 4 subtypes, 2 viral subtypes and 2 bacterial subtypes (C), 4 bacteria with most significant differences in proportion among 4 subtypes (D), Stacked bar graph of the top 20 abundant gut viruses in the 4 subtypes, 2 viral subtypes and 2 bacterial subtypes (E), Box diagram of the top 20 abundant EVs in the 4 subtypes, 2 viral subtypes and 2 bacterial subtypes (F), and 4 viruses with most significant differences in proportion among 4 subtypes (G)

Fig. 5

Differential analysis of four subtypes: Based on gut typing, the differential bacteria and viruses between AA and NC were screened: LDA analysis of 4 subtypes (A). Differences in diversity among the 4 subtypes and the diversity of the 4 subtypes between AA and NC were analyzed: NMDS and PCOA analysis in the 4 subtypes (B), NMDS and PCOA analysis of 4 subtypes between AA and NC (C)

Fig. 6

Modeling based on specific gut bacteria after typing: CatBoost models based on differential gut bacteria were built: Before typing (A), After typing (B)