Metabolic gene signature for predicting breast cancer recurrence using transcriptome analysis.

The study aimed at identifying a metabolic gene signature for stratifying the risk of recurrence in breast cancer. The data of patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The limma package was used to identify differentially expressed metabolic genes, and a metabolic gene signature was constructed. A five-gene metabolic signature was established that demonstrated satisfactory accuracy and predictive power in both training and validation cohorts. Also, a nomogram for predicting recurrence-free survival was established using a combination of the metabolism gene risk score and the clinicopathological features. The proposed metabolic gene signature and nomogram have a significant prognostic value and may improve the recurrence risk stratification for breast cancer patients.