부산대학교 도서관

Aim: To monitor prospectively the occurrence of colorectal anastomotic leakage (CAL) in patients with colon cancer undergoing resectional surgery, characterizing the microbiota in both faeces and mucosal biopsies of anastomosis. In a second stage, we investigated the ability to predict CAL using machine learning models based on clinical data and microbiota composition. Method: A total of 111 patients were included, from whom a faecal sample was obtained, as well as biopsy samples from proximal and distal sites in the healthy margins of the tumour piece. The microorganisms present in the samples were investigated using microbial culture and 16S rDNA massive sequencing. Collagenase and protease production was determined, as well as the presence of genes responsible for expressing enzymes with these activities. Machine learning analyses were developed using clinical and microbiological data. Results: The incidence of CAL was 9.0%, and CAL was associated with collagenase/protease‐producing Enterococcus. Significant differences were found in the microbiota composition of proximal and distal biopsy samples, but not in faecal samples, among patients who developed CAL. Clinical predictors of CAL were 5‐day C‐reactive protein and heart disease, whereas 3‐day C‐reactive protein and diabetes were negative predictors. Conclusion: Biopsy samples from surgical margins, rather than faecal samples, are the most appropriate samples for exploring the contribution of the intestinal microbiota to CAL. Enterococci are only enriched in the anastomosis after surgery, and their collagenases and proteases are involved in the degradation of the anastomotic scar. [ABSTRACT FROM AUTHOR]