Butyricimonas is a key gut microbiome component for predicting postoperative recurrence of esophageal cancer

Abstract

Background: Recently, intestinal bacteria have attracted attention as factors affecting the prognosis of patients with cancer. However, the intestinal microbiome is composed of several hundred types of bacteria, necessitating the development of an analytical method that can allow the use of this information as a highly accurate biomarker. In this study, we investigated whether the preoperative intestinal bacterial profile in patients with esophageal cancer who underwent surgery after preoperative chemotherapy could be used as a biomarker of postoperative recurrence of esophageal cancer.

Methods: We determined the gut microbiome of the patients using 16S rRNA metagenome sequencing, followed by statistical analysis. Simultaneously, we performed a machine learning analysis using a random forest model with hyperparameter tuning and compared the data obtained.

Results: Statistical and machine learning analyses revealed two common bacterial genera, Butyricimonas and Actinomyces, which were abundant in cases with recurrent esophageal cancer. Butyricimonas primarily produces butyrate, whereas Actinomyces are oral bacteria whose function in the gut is unknown.

Conclusion: Our results indicate that Butyricimonas spp. may be a biomarker of postoperative recurrence of esophageal cancer. Although the extent of the involvement of these bacteria in immune regulation remains unknown, future research should investigate their presence in other pathological conditions. Such research could potentially lead to a better understanding of the immunological impact of these bacteria on patients with cancer and their application as biomarkers.

Keywords: Actinomyces; Butyricimonas; Clinical efficacy; Esophageal cancer; Gut microbiome; Machine learning analysis.

Fig. 1

Percentage composition of microbiota profile in patients with esophageal cancer. A Relative abundance (%, composition) of bacteria at the species level between the non-recurrence and recurrence groups. Bacteria that were found in more than 0.1% of the cases were summed to 100%. B Relative abundance (%, composition) of bacteria at the genus level between the non-recurrence and recurrence groups. Bacteria that were found in more than 0.1% of the cases were summed to 100%

Fig. 2

Differences in gut microbiome composition between non-recurrence and recurrence group. We compared the relative abundance difference in bacteria between non-recurrence and recurrence group. A Top 7 bacteria by treatment effect at the species level. B Top 7 bacteria by treatment effect at the genus level

Fig. 3

Bacterial tree diagram between non-recurrence and recurrence group. A Bacterial tree diagram, with the dark gray and light gray lines indicating the bacteria at the species level between non-recurrence and recurrence group, respectively. B Bacterial tree diagram, with the dark gray and light gray lines indicating the bacteria at the genus level between non-recurrence and recurrence group, respectively

Fig. 4

Statistically significant differences in intestinal bacteria. We compared the statistical significance of differences in bacteria between non-recurrence and recurrence group, using the Mann–Whitney U test. The red line indicates a p-value of 0.05. A Top 10 bacteria at the species level. B Top 10 bacteria at genus level

Fig. 5

Top 10 gut microbiomes from random forest analysis with hyperparameter tuning. A Top 10 bacteria at the species level. B Top 10 bacteria at genus level

Fig. 6

Relative abundance of Butyricimonas is associated with differences in survival rate. A ROC according to patient with esophageal cancer calculated by cross-validated random forest models. B Kaplan–Meier estimates for survival probability based on the abundance levels of Butyricimonas in patient with esophageal cancer after surgery. C Kaplan–Meier estimates for survival probability based on the abundance levels of Butyricimonas in the postoperative recurrence group