Using gut microbiota as a diagnostic tool for colorectal cancer: machine learning techniques reveal promising results.

Introduction. Increasing evidence suggests a correlation between gut microbiota and colorectal cancer (CRC).Hypothesis/Gap Statement. However, few studies have used gut microbiota as a diagnostic biomarker for CRC.Aim. The objective of this study was to explore whether a machine learning (ML) model based on gut microbiota could be used to diagnose CRC and identify key biomarkers in the model.Methodology. We sequenced the 16S rRNA gene from faecal samples of 38 participants, including 17 healthy subjects and 21 CRC patients. Eight supervised ML algorithms were used to diagnose CRC based on faecal microbiota operational taxonomic units (OTUs), and the models were evaluated in terms of identification, calibration and clinical practicality for optimal modelling parameters. Finally, the key gut microbiota was identified using the random forest (RF) algorithm.Results. We found that CRC was associated with the dysregulation of gut microbiota. Through a comprehensive evaluation of supervised ML algorithms, we found that different algorithms had significantly different prediction performance using faecal microbiomes. Different data screening methods played an important role in optimization of the prediction models. We found that naïve Bayes algorithms [NB, accuracy=0.917, area under the curve (AUC)=0.926], RF (accuracy=0.750, AUC=0.926) and logistic regression (LR, accuracy=0.750, AUC=0.889) had high predictive potential for CRC. Furthermore, important features in the model, namely s__metagenome_g__Lachnospiraceae_ND3007_group (AUC=0.814), s__Escherichia_coli_g__Escherichia-Shigella (AUC=0.784) and s__unclassified_g__Prevotella (AUC=0.750), could each be used as diagnostic biomarkers of CRC.Conclusions. Our results suggested an association between gut microbiota dysregulation and CRC, and demonstrated the feasibility of the gut microbiota to diagnose cancer. The bacteria s__metagenome_g__Lachnospiraceae_ND3007_group, s__Escherichia_coli_g__Escherichia-Shigella and s__unclassified_g__Prevotella were key biomarkers for CRC.

Streptococcus, the Predominant Bacterium to Predict the Severity of Liver Injury in Alcoholic Liver Disease.

Background: New evidence implies that the imbalance of gut microbiota is associated with the progression of alcoholic liver disease (ALD) and that the composition of gut microbiota is altered in ALD patients. However, the predominant bacterium in patients involved in the progress of ALD has not been identified. The purpose of this study is to investigate the predominant bacterium in the early and end-stages of ALD as well as the relationship between the bacterium and the degree of liver injury. Methods: We enrolled 21 alcoholic fatty liver (AFL) patients, 17 alcoholic liver cirrhosis (ALC) patients and 27 healthy controls, and sequenced the 16S rRNA gene of their fecal microbiota. The gut microbiota composition and its relationship with the indicators of clinical hepatic function were assessed using canonical correspondence analysis (CCA), spearman correlation heatmap and multivariate association with linear (MaAsLin) Models. Results: The composition and structure of gut microbiota changed greatly in different stages of ALD, and the degree of disorder was aggravated with the progression of ALD, even in the early stage. Moreover, the relative abundance of Streptococcus was highly enriched only in patients with ALC (P <0.001), and positively correlated with AST level (P = 0.029). The abundance of Streptococcus distinguished the liver injury of ALC patients from the controls with an area under the receiver-operating characteristic curve (AUC) of 0.877 (P < 0.001). Conclusions: These findings indicate that the imbalance of gut microbiota exists at the early and end-stages of ALD, and the degree of disorder is aggravated with the progression of ALD. Streptococcus, as the predominant bacterium, may be a microbiological marker to evaluate the severity of liver injury in ALD patients.