Gut-microbiota in Colorectal Cancer Patients: 16S rRNA Sequencing analysis and Machine-learning Algorithm Prediction

Objective: The incidence and mortality of colorectal cancer (CRC) have been increasing, making research into factors related to CRC necessary. This article aims to differentiate characteristics of gut microbiota between CRC patients and healthy individuals, and employs machine-learning algorithms for predicting specific microbial taxa. Methods: We conducted a multicenter case-control study starting in 2020, used 16S rRNA gene sequencing to analyze the gut microbiota in newly diagnosed CRC patients and healthy individuals. We used Python (version 3.9) to develop predictive models based on machine-learning algorithms. Results : Our research indicates a significant abundance of Escherichia-Shigella and Bacteroides in CRC patients, while Blautia and Faecalibacterium notably increased in healthy individuals. Using the Lasso model, we identified eight specific microbial taxa associated with CRC patients and thirteen taxa associated with healthy individuals. Discussion : The research highlights significant increase of various microbial taxa associated among CRC patients and healthy individuals, and also some microbiota with contentious functionalities. Among the machine-learning algorithms tested, the Random Forest model proved most suitable for predictive modeling in this region.