Prognostic Value of a Glycolytic Signature and Its Regulation by Y-Box-Binding Protein 1 in Triple-Negative Breast Cancer.
Yi-Wen LaiWen-Jing HsuWen-Ying LeeCheng-Hsun ChenYing-Huei TsaiJia-Zih DaiChing-Chieh YangCheng-Hsun ChenPublished in: Cells (2021)
Triple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer as it shows a high capacity for metastasis and poor prognoses. Metabolic reprogramming is one of the hallmarks of cancer, and aberrant glycolysis was reported to be upregulated in TNBC. Thus, identifying metabolic biomarkers for diagnoses and investigating cross-talk between glycolysis and invasiveness could potentially enable the development of therapeutics for patients with TNBC. In order to determine novel and reliable metabolic biomarkers for predicting clinical outcomes of TNBC, we analyzed transcriptome levels of glycolysis-related genes in various subtypes of breast cancer from public databases and identified a distinct glycolysis gene signature, which included ENO1, SLC2A6, LDHA, PFKP, PGAM1, and GPI, that was elevated and associated with poorer prognoses of TNBC patients. Notably, we found a transcription factor named Y-box-binding protein 1 (YBX1) to be strongly associated with this glycolysis gene signature, and it was overexpressed in TNBC. A mechanistic study further validated that YBX1 was upregulated in TNBC cell lines, and knockdown of YBX1 suppressed expression of those glycolytic genes. Moreover, YBX1 expression was positively associated with epithelial-to-mesenchymal transition (EMT) genes in breast cancer patients, and suppression of YBX1 downregulated expressions of EMT-related genes and tumor migration and invasion in MDA-MB-231 and BT549 TNBC cells. Our data revealed an YBX1-glycolysis-EMT network as an attractive diagnostic marker and metabolic target in TNBC patients.
Keyphrases
- binding protein
- transcription factor
- genome wide
- end stage renal disease
- epithelial mesenchymal transition
- ejection fraction
- genome wide identification
- newly diagnosed
- poor prognosis
- chronic kidney disease
- healthcare
- prognostic factors
- gene expression
- copy number
- single cell
- patient reported outcomes
- big data
- machine learning
- cell death
- cell cycle arrest
- small molecule
- deep learning
- cell proliferation
- oxidative stress
- lymph node metastasis
- patient reported
- childhood cancer