MARK2 potentiate aerobic glycolysis-mediated cell growth in breast cancer through regulating mTOR/HIF-1α and p53 pathways.
Lavanya PonnusamySathan Raj NatarajanRavi ManoharanPublished in: Journal of cellular biochemistry (2022)
The microtubule-affinity regulating kinases (MARKs) family plays a crucial role in regulating breast cancer development and progression. However, its precise function and the relevant molecular mechanism in breast cancer have not yet been elucidated. In this study, analysis of The Cancer Genome Atlas (TCGA) data revealed that MARK2 expression was markedly upregulated in breast cancer tissues, and high expression of MARK2 was correlated with poor survival. Functional assays showed that MARK2 deletion or inhibition suppressed aerobic glycolysis and cell growth as well as induced cell cycle arrest and apoptosis in breast cancer cells. Mechanistically, MARK2 stimulates mTOR-mediated hypoxia-inducible factor 1 alpha (HIF-1α) transcription activity and represses p53-transcription activity in breast cancer cells. TCGA data revealed that MARK2 expression was positively correlated with mTOR, Raptor, S6K1, glucose transporter 1, lactate dehydrogenase, HIF-1α, and 4E-BP1 expression, whereas negatively correlated with p53, p21, and Bax in breast cancer tissue. Conclusively, our study demonstrated that MARK2 promotes breast cancer aerobic glycolysis and cell proliferation, and inhibits apoptosis, in part, through regulating mTOR/HIF-1α and p53 signaling pathways. Overall, these findings point to the potential of targeting MARK2 for breast cancer treatment.
Keyphrases
- cell proliferation
- cell cycle arrest
- poor prognosis
- breast cancer cells
- cell death
- pi k akt
- oxidative stress
- endothelial cells
- signaling pathway
- single cell
- gene expression
- type diabetes
- metabolic syndrome
- high intensity
- electronic health record
- machine learning
- transcription factor
- cell cycle
- insulin resistance
- childhood cancer
- artificial intelligence
- big data
- cancer therapy
- blood glucose
- papillary thyroid
- bioinformatics analysis