School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, PR China.
Guangxi Key Laboratory of AIDS Prevention and Treatment & Guangxi Universities Key Laboratory of Prevention and Control of Highly Prevalent Disease, Nanning, 530021, Guangxi, PR China.
J Med Microbiol. 2023 Jun;72(6). doi: 10.1099/jmm.0.001699.
Increasing evidence suggests a correlation between gut microbiota and colorectal cancer (CRC). However, few studies have used gut microbiota as a diagnostic biomarker for CRC. 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. 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. 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 (AUC=0.814) (AUC=0.784) and (AUC=0.750), could each be used as diagnostic biomarkers of CRC. Our results suggested an association between gut microbiota dysregulation and CRC, and demonstrated the feasibility of the gut microbiota to diagnose cancer. The bacteria and were key biomarkers for CRC.
越来越多的证据表明肠道微生物群与结直肠癌（CRC）之间存在相关性。然而，很少有研究将肠道微生物群用作 CRC 的诊断生物标志物。本研究旨在探讨基于肠道微生物群的机器学习（ML）模型是否可用于诊断 CRC 并识别模型中的关键生物标志物。我们对 38 名参与者的粪便样本进行了 16S rRNA 基因测序，其中包括 17 名健康受试者和 21 名 CRC 患者。使用 8 种监督 ML 算法基于粪便微生物群操作分类单元（OTU）来诊断 CRC，并根据识别，校准和临床实用性来评估模型以获得最佳建模参数。最后，使用随机森林（RF）算法确定关键肠道微生物群。我们发现 CRC 与肠道微生物群的失调有关。通过对监督 ML 算法的综合评估，我们发现不同算法使用粪便微生物组具有明显不同的预测性能。不同的数据筛选方法在优化预测模型中起着重要作用。我们发现，朴素贝叶斯算法[NB，准确率=0.917，AUC=0.926]，RF（准确率=0.750，AUC=0.926）和逻辑回归（LR，准确率=0.750，AUC=0.889）对 CRC 具有较高的预测潜力。此外，模型中的重要特征，即（AUC=0.814）（AUC=0.784）和（AUC=0.750），均可作为 CRC 的诊断生物标志物。我们的研究结果表明肠道微生物群失调与 CRC 之间存在关联，并证明了肠道微生物群诊断癌症的可行性。细菌和是 CRC 的关键生物标志物。
