Metabolic Gene Signature for Predicting Breast Cancer Recurrence Using Transcriptome Analysis

Abstract

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.

Keywords::

• breast cancer
• metabolism
• prognosis
• recurrence
• risk signature

Author contributions

J Feng, Y Gong and S Sun designed the experiment. J Feng, J Ren, Q Yang and Y Gong undertook the data acquisition. J Feng, L Liao, L Cui and Y Gong were involved in the interpretation of data. J Feng and S Sun analyzed and visualized the data. All authors drafted and revised the manuscript. The final manuscript was read and approved by all authors.

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pendingor royalties.

The authors acknowledge Editage for English-language editing of this paper.

Ethical conduct of research

The authors state that ethics approval was not required for the study as only data downloaded from public databases were used.

Data sharing statement

All data analyzed during the current study are available from the TCGA database (https://tcga-data.nci.nih.gov/tcga/) and GEO database (https://www.ncbi.nlm.nih.gov/geo/).