Oscillatory Hypoxia Induced Unfolded Protein Folding Response Gene Expression Predicts Low Survival in Human Breast Cancer Patients.
Yasir SuhailYamin LiuWenqiang DuJunaid AfzalXihua QiuAmina AtiqPaola Vera-LiconaEran AgmonKshitiz KshitizPublished in: bioRxiv : the preprint server for biology (2024)
Hypoxia is one of the key factors in the tumor microenvironment regulating nearly all steps in the metastatic cascade in many cancers, including in breast cancer. The hypoxic regions can however be dynamic with the availability of oxygen fluctuating or oscillating. The canonical response to hypoxia is relayed by transcription factor HIF-1, which is stabilized in hypoxia and acts as the master regulator of a large number of downstream genes. However, HIF-1 transcriptional activity can also fluctuate either due to unstable hypoxia, or by lactate mediated non-canonical degradation of HIF-1. Our understanding of how oscillatory hypoxia or HIF-1 activity specifically influence cancer malignancy is very limited. Here, using MDA-MB-231 cells as a model of triple negative breast cancer characterized by severe hypoxia, we measured the gene expression changes induced specifically by oscillatory hypoxia. We found that oscillatory hypoxia can specifically regulate gene expression differently, and at times opposite to stable hypoxia. Using The Cancer Genome Atlas (TCGA) RNAseq data of human cancer samples, we show that the oscillatory specific gene expression signature in MDA-MB-231 is enriched in most human cancers, and prognosticate low survival in breast cancer patients. In particular, we found that oscillatory hypoxia, unlike stable hypoxia, induces unfolded protein folding response (UPR) in cells resulting in gene expression predicting reduced survival.
Keyphrases
- endothelial cells
- gene expression
- high glucose
- high frequency
- transcription factor
- dna methylation
- papillary thyroid
- squamous cell carcinoma
- small cell lung cancer
- cell cycle arrest
- genome wide
- induced pluripotent stem cells
- squamous cell
- cell death
- machine learning
- electronic health record
- breast cancer cells
- young adults
- pluripotent stem cells
- lymph node metastasis
- artificial intelligence
- heat stress
- drug induced
- stress induced