Identification of a noncanonical function for ribose-5-phosphate isomerase A promotes colorectal cancer formation by stabilizing and activating β-catenin via a novel C-terminal domain.
Yu-Ting ChouJeng-Kai JiangMuh-Hwa YangJeng-Wei LuHua-Kuo LinHorng-Dar WangChiou-Hwa YuhPublished in: PLoS biology (2018)
Altered metabolism is one of the hallmarks of cancers. Deregulation of ribose-5-phosphate isomerase A (RPIA) in the pentose phosphate pathway (PPP) is known to promote tumorigenesis in liver, lung, and breast tissues. Yet, the molecular mechanism of RPIA-mediated colorectal cancer (CRC) is unknown. Our study demonstrates a noncanonical function of RPIA in CRC. Data from the mRNAs of 80 patients' CRC tissues and paired nontumor tissues and protein levels, as well as a CRC tissue array, indicate RPIA is significantly elevated in CRC. RPIA modulates cell proliferation and oncogenicity via activation of β-catenin in colon cancer cell lines. Unlike its role in PPP in which RPIA functions within the cytosol, RPIA enters the nucleus to form a complex with the adenomatous polyposis coli (APC) and β-catenin. This association protects β-catenin by preventing its phosphorylation, ubiquitination, and subsequent degradation. The C-terminus of RPIA (amino acids 290 to 311), a region distinct from its enzymatic domain, is necessary for RPIA-mediated tumorigenesis. Consistent with results in vitro, RPIA increases the expression of β-catenin and its target genes, and induces tumorigenesis in gut-specific promotor-carrying RPIA transgenic zebrafish. Together, we demonstrate a novel function of RPIA in CRC formation in which RPIA enters the nucleus and stabilizes β-catenin activity and suggests that RPIA might be a biomarker for targeted therapy and prognosis.
Keyphrases
- cell proliferation
- epithelial mesenchymal transition
- gene expression
- signaling pathway
- cell cycle
- escherichia coli
- end stage renal disease
- machine learning
- ejection fraction
- nitric oxide
- transcription factor
- pi k akt
- peritoneal dialysis
- long non coding rna
- deep learning
- prognostic factors
- bioinformatics analysis
- childhood cancer