Identification of potential biomarkers and metabolic pathways based on integration of metabolomic and transcriptomic data in the development of breast cancer.

Abstract

Breast cancer (BC) is the most common type of malignant tumor and the most common cause of cancer-related mortality among women. Metabolic reprogramming is considered a hallmark of cancer, and the study of BC metabolism may be the key to the development of new strategies for diagnosis and treatment. In this study, we aimed to explore thepotential metabolites and gene biomarkers for BC through the integration of metabolomics and transcriptomic data, which could further understand BC tumor biology. Transcriptome dataset GSE139038 was downloaded to explore the differentially expressed genes (DEGs) between BC and normal control (NC) samples. Metabolomics dataset MTBLS326 was downloaded and preprocessed to obtain altered metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEGs-metabolites relations. Finally, the pathway enrichment analysis of altered metabolites was performed. A total of 280 DEGs and eight metabolites were explored between BC and NC samples. The liner module analysis investigated 28 DEGs-metabolites interactions including WASP family member 3 (WASF3)-lactate, ras-related protein Rab-7B (RAB7B)-lactate, and methyltransferase-like 7A (METTL7A)-pyruvate. Finally, pathways analysis showed that these metabolites (such as lactate and pyruvate) were mainly enriched in pathways like disorders of the Krebs cycle. Combining with the transcriptomic and metabolomics data, we found that lactate, pyruvate, WASF3, RAB7B, and METTL7A might be used as novel biomarkers and potential therapeutic targets for BC. In addition, the disorders of the Krebs cycle pathway might affect the progression of BC.