The stem-cell-like behavior of cancer cells plays a central role in tumor heterogeneity and invasion and correlates closely with drug resistance and unfavorable clinical outcomes. However, the molecular underpinnings of cancer cell stemness remain incompletely defined. Here, we show that SNHG1 , a long non-coding RNA that is over-expressed in ~95% of human muscle-invasive bladder cancers (MIBCs), induces stem-cell-like sphere formation and the invasion of cultured bladder cancer cells by upregulating Rho GTPase, Rac1. We further show that SNHG1 binds to DNA methylation transferase 3A protein (DNMT3A), and tethers DNMT3A to the promoter of miR-129-2 , thus hyper-methylating and repressing miR-129-2-5p transcription. The reduced binding of miR-129-2 to the 3'-UTR of Rac1 mRNA leads to the stabilization of Rac1 mRNA and increased levels of Rac1 protein, which then stimulates MIBC cell sphere formation and invasion. Analysis of the Human Protein Atlas shows that a high expression of Rac1 is strongly associated with poor survival in patients with MIBC. Our data strongly suggest that the SNHG1 /DNMT3A/ miR-129-2-5p /Rac1 effector pathway drives stem-cell-like and invasive behaviors in MIBC, a deadly form of bladder cancer. Targeting this pathway, alone or in combination with platinum-based therapy, may reduce chemoresistance and improve longer-term outcomes in MIBC patients.
Keyphrases
- long non coding rna
- stem cells
- poor prognosis
- dna methylation
- cell migration
- endothelial cells
- binding protein
- cell therapy
- gene expression
- genome wide
- cell proliferation
- single cell
- end stage renal disease
- regulatory t cells
- long noncoding rna
- electronic health record
- transcription factor
- prognostic factors
- skeletal muscle
- protein protein
- epithelial mesenchymal transition
- metabolic syndrome
- amino acid
- deep learning
- drug delivery
- patient reported outcomes
- immune response
- free survival
- preterm infants
- newly diagnosed
- cancer therapy
- machine learning
- mesenchymal stem cells
- artificial intelligence
- young adults
- bone marrow
- peritoneal dialysis
- patient reported