Metabolic gene signature for predicting breast cancer recurrence using transcriptome analysis.
Juan FengJun RenQingfeng YangLingxia LiaoLe CuiYiping GongSheng-Rong SunPublished in: Future oncology (London, England) (2021)
Background: The study aimed at identifying a metabolic gene signature for stratifying the risk of recurrence in breast cancer. Materials & methods: 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. Results: 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. Conclusions: The proposed metabolic gene signature and nomogram have a significant prognostic value and may improve the recurrence risk stratification for breast cancer patients.
Keyphrases
- free survival
- genome wide
- genome wide identification
- copy number
- gene expression
- dna methylation
- end stage renal disease
- chronic kidney disease
- machine learning
- emergency department
- newly diagnosed
- young adults
- single cell
- deep learning
- papillary thyroid
- wastewater treatment
- big data
- artificial intelligence
- breast cancer risk