In Vivo Evidence for Voltage-Gated Sodium Channel Expression in Carcinomas and Potentiation of Metastasis.
Mustafa B A DjamgozScott P FraserWilliam John BrackenburyPublished in: Cancers (2019)
A wide body of evidence suggests that voltage-gated sodium channels (VGSCs) are expressed de novo in several human carcinomas where channel activity promotes a variety of cellular behaviours integral to the metastatic cascade. These include directional motility (including galvanotaxis), pH balance, extracellular proteolysis, and invasion. Contrary to the substantial in vitro data, however, evidence for VGSC involvement in the cancer process in vivo is limited. Here, we critically assess, for the first time, the available in vivo evidence, hierarchically from mRNA level to emerging clinical aspects, including protein-level studies, electrolyte content, animal tests, and clinical imaging. The evidence strongly suggests that different VGSC subtypes (mainly Nav1.5 and Nav1.7) are expressed de novo in human carcinoma tissues and generally parallel the situation in vitro. Consistent with this, tissue electrolyte (sodium) levels, quantified by clinical imaging, are significantly higher in cancer vs. matched non-cancer tissues. These are early events in the acquisition of metastatic potential by the cancer cells. Taken together, the multi-faceted evidence suggests that the VGSC expression has clinical (diagnostic and therapeutic) potential as a prognostic marker, as well as an anti-metastatic target. The distinct advantages offered by the VGSC include especially (1) its embryonic nature, demonstrated most clearly for the predominant neonatal Nav1.5 expression in breast and colon cancer, and (2) the specifically druggable persistent current that VGSCs develop under hypoxic conditions, as in growing tumours, which promotes invasiveness and metastasis.
Keyphrases
- poor prognosis
- papillary thyroid
- small cell lung cancer
- squamous cell carcinoma
- endothelial cells
- high resolution
- binding protein
- gene expression
- squamous cell
- long non coding rna
- machine learning
- escherichia coli
- small molecule
- staphylococcus aureus
- cell migration
- induced pluripotent stem cells
- childhood cancer
- data analysis
- pluripotent stem cells
- deep learning