Cancer Cell Acid Adaptation Gene Expression Response Is Correlated to Tumor-Specific Tissue Expression Profiles and Patient Survival.
Jiayi YaoDominika CzaplinskaRenata IalchinaJulie SchnipperBin LiuAlbin SandelinStine Helene Falsig PedersenPublished in: Cancers (2020)
The acidic pH of the tumor microenvironment plays a critical role in driving cancer development toward a more aggressive phenotype, but the underlying mechanisms are unclear. To this end, phenotypic and genotypic changes induced by adaptation of cancer cells to chronic acidosis have been studied. However, the generality of acid adaptation patterns across cell models and their correlation to the molecular phenotypes and aggressiveness of human cancers are essentially unknown. Here, we define an acid adaptation expression response shared across three cancer cell models, dominated by metabolic rewiring, extracellular matrix remodeling, and altered cell cycle regulation and DNA damage response. We find that many genes which are upregulated by acid adaptation are significantly correlated to patient survival, and more generally, that there are clear correlations between acid adaptation expression response and gene expression change between normal and tumor tissues, for a large subset of cancer patients. Our data support the notion that tumor microenvironment acidity is one of the key factors driving the selection of aggressive cancer cells in human patient tumors, yet it also induces a growth-limiting genotype that likely limits cancer cell growth until the cells are released from acidosis, for instance during invasion.
Keyphrases
- gene expression
- cell cycle
- extracellular matrix
- endothelial cells
- dna damage response
- poor prognosis
- case report
- dna methylation
- papillary thyroid
- induced apoptosis
- stem cells
- squamous cell carcinoma
- cell proliferation
- squamous cell
- signaling pathway
- cell death
- machine learning
- young adults
- oxidative stress
- bone marrow
- induced pluripotent stem cells
- long non coding rna
- artificial intelligence
- ionic liquid
- childhood cancer
- big data
- single molecule
- drug induced