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KIF1A promotes neuroendocrine differentiation in prostate cancer by regulating the OGT-mediated O-GlcNAcylation

GSE266283 Homo sapiens Expression profiling by high throughput sequencing 6 samples Submitted 2024/10/23 Platform GPL24676
Summary
Background: Treatment-related neuroendocrine prostate cancer (t-NEPC) is a highly aggressive form of prostate cancer (PCa). Understanding the molecular mechanisms driving t-NEPC may provide valuable therapeutic strategies. Methods: we performed a pan-cancer differential mRNA abundance analysis of neuroendocrine tumors (NET) and non-NETs using integrated datasets to identify potential driver genes in NEPC. Gain- and loss-of-function studies were performed in PCa cell lines and subcutaneous xenografts in mouse to characterize the role of Kinesin-like protein (KIF1A) in neuroendocrine (NE) differentiation. Western blot, qRT-PCR, immunoprecipitation (IP), nuclear and cytoplasmic protein extraction, CO-IP and immunofluorescence were performed to study the regulation of KIF1A to OGT. Results: We identified that KIF1A overexpression is highly correlated to NE differentiation. NE differentiation features in PCa, including NE marker gene expression, stemness and EMT, were impaired by KIF1A knockdown and promoted by KIF1A overexpression. Targeting KIF1A inhibited the growth of NE-differentiated PCa cells in vitro and in vivo. Mechanistically, KIF1A bound with OGT and regulated its protein expression and O-linked N-acetylglucosamine transferase activity. OGT nuclear translocation induced by elevated KIF1A inhibited OGT ubiquitin-proteasome pathway degradation in the cytoplasm and promoted intranuclear O-GlcNAcylation of β-catenin and OCT4. More importantly, our data revealed that OGT is critical for KIF1A induced NE differentiation and aggressive growth. Conclusion: KIF1A promotes NE differentiation through modeling OGT O-GlcNAcylation in PCa. Targeting KIF1A - OGT may impede the development of NEPC for a group of PCa patients with elevated KIF1A expression.
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Also filed as BioProject PRJNA1106499 and SRA study SRP504978. Searching any of these in the dataset finder brings you back here.

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