SMOTE-augmented machine learning model predicts recurrent and metastatic breast cancer from microbiome analysis.

ABSTRACT: Recurrence and metastasis of breast cancer (RMBC) have a decisive impact on patient survival, necessitating reliable biomarkers for its early prediction. This study used machine learning to evaluate blood microbiome profiles as predictive biomarkers of RMBC. A retrospective predictive analysis was conducted on 288 participants, including 96 patients with breast cancer and 192 healthy controls. After 7 years of follow-up, patients were classified into disease-free survival (DFS, n = 88) and RMBC (n = 8) groups. Blood microbiome composition was analysed using 16S rRNA sequencing, followed by quality control. The Synthetic Minority Oversampling Technique (SMOTE) was employed to address class imbalance. Eleven machine learning models were trained using leave-one-out cross-validation (LOOCV) an

SUBMITTER: Hong JE 

PROVIDER: S-EPMC12475220 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature