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

作者：Fang Lü, Ting Lei, Jie Zhou, Hao Liang, Ping Cui, Taiping Zuo, Li Ye, Hui Chen, Jiegang Huang · 发表于：Journal of Medical Microbiology · 年份：2023 · DOI：10.1099/jmm.0.001699 · 被引用次数：10 · 研究领域：Gut microbiota and health、Colorectal Cancer Screening and Detection、Radiomics and Machine Learning in Medical Imaging

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, namel...