# Unique Microbial Characterisation of Oesophageal Squamous Cell Carcinoma Patients with Different Dietary Habits Based on Light Gradient Boosting Machine Learning Classifier

## TL;DR

This study explores how different dietary habits affect the microbiome in oesophageal cancer patients, finding distinct microbial patterns linked to specific diets.

## Contribution

The study introduces a novel approach combining 16S rRNA sequencing and LightGBM machine learning to identify diet-specific microbial signatures in ESCC patients.

## Key findings

- ESCC patients with different dietary habits show significant differences in microbial composition.
- Eubacterium_B sulci and undefined Fusobacterium_C are linked to specific metabolic pathways in diet groups.
- Microbial network analysis reveals unique microbial interactions associated with distinct dietary patterns.

## Abstract

Objectives: The microbiome plays an important role in cancer, but the relationship between dietary habits and the microbiota in oesophageal squamous cell carcinoma (ESCC) is not clear. The aim of this study is to explore the complex relationship between the microbiota in oesophagal tissue and dietary habits in ESCC patients. Methods: 173 ESCC patients were included. The method of 16S rRNA sequencing was used to analyze microbial composition and diversity. The LEfSe and Boruta methods were used to screen important microbes, and the LightGBM algorithm distinguished microbes associated with different dietary habits. PICRUST2 and DESeq2 predicted microbial function and screened differential functions. The Pearson test was used to analyze correlations between microbes and functions, and SPARCC microbial symbiotic networks and Cytoscape were used to determine microbial interactions. Results: Significant differences in microbial composition were observed among ESCC patients with different dietary habits. LEfSe and Boruta identified three, six, and two significantly different bacteria in the FF/FP, FF/PF, and FF/PP groups, respectively, with AUC values of 0.683, 0.830, and 0.715. PICRUST2 and DESeq2 analysis revealed 3, 11, and 5 significantly different metabolic pathways in each group. Eubacterium_B sulci was positively correlated with PWY-6285, PWY-3801, and PWY-5823. PWY-6397 was positively correlated with undefinded (Fusobacterium_C). Microbial network analysis confirmed unique microbial characteristics in different diet groups. Conclusions: Different dietary habits lead to alterations in Eubacterium_B sulci and undefinded (Fusobacterium_C) and related functional pathways.