Gut microbiota-based algorithms in the prediction of metachronous adenoma in colorectal cancer patients following surgery

Abstract Background/Aims:The occurrence of metachronous colorectal cancer (CRC) in patients after surgery has been well documented. Despite high risk, there is a lack of reliable factors or models to be used for predicting the development of metachronous CRC. The objectives of this study were to examine mucosal and fecal microbiota, and to assess their values in the prediction of metachronous colorectal adenoma among CRC patients who underwent surgical removal of their primary CRC. Methods: A cohort of CRC patients was prospectively enrolled, and their mucosal and fecal samples were used for analysis of gut microbiota by sequencing the 16S rRNA genes. The relatively predominant gut microbial populations, in combination with clinical risk factors, were utilized to generate Random-Forest (RF) algorithms for the predication of metachronous adenoma. Results: Patients with metachronous adenoma in the MA group exhibited significantly lower mucosal microbial alpha-diversity compared to those individuals in the nMA group. Linear discriminant analysis of effect size (LEfSe) identified 10 predominant bacterial genera, some of which were identified as independent risk factors for metachronous adenoma. The microbiota-based RF model was established utilizing specific members of predominant gut microbiota and independent clinical risk factors (high body mass index (BMI) and the status of synchronous adenoma) in combination. The RF model had an AUC of 0.885 for predicting metachronous adenoma. The RF model performed well on fecal and off-tumor samples with the AUC of 0.835 and 0.889, respectively. Further, we generated a RF model by including specific bacteria taxa for differential prediction of metachronous adenoma from liver metastasis, which showed good performance with AUC of 0.86. Finally, we introduced a risk score for potential clinical application using the four independent predictive factors, and the scoring system had an AUC of 0.94. The presence of two or more risk factors for metachronous adenoma had a sensitivity and specificity of 90.9% and 89.5%, respectively. Conclusions: The findings have demonstrated that the microbiota-based models and scoring system have good ability to predict the risk for developing metachronous adenoma after surgical resection. The newly established algorithms may hold potential to guide individual postoperative surveillance plan for CRC patients.