An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer.

Abstract

Background: This study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction.

Methods: Oral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.

Results: GC patients with <3 years of survival showed a higher abundance of Aggregatibacter and diminished abundances of Filifactor and Moryella than those who survived ≥3 years. The Boruta algorithm unearthed Leptotrichia as another significant marker for GC prognosis. Consequently, a DNN model was constructed based on the relative abundances of these bacteria, predicting 3-year and 5-year survival in GC patients with Area Under Curve of 0.814 and 0.912, respectively. Notably, the DNN model outperformed the TNM staging system, SVM and RF models. The prognostic value of these bacterial markers was further reinforced by external validation.

Conclusion: The oral microbiota-based DNN model may advance GC prognosis. The biological functions of these oral bacterial markers warrant further investigation from the perspective of GC progression.