Abstract 206P
Background
Endometrial cancer (EC) is most strongly associated with obesity compared to all other cancers. As the population with obesity and metabolic disorders rises globally, the incidence and mortality of EC increase remarkedly. Metabolic syndrome (MS) is not only related to the occurrence of EC, but in recent years, researchers have shown that MS also plays an important role in the progression and poor prognosis of EC. However, the mechanism is unclear.
Methods
Untargeted and targeted metabolomics, multiplex immune fluorescence staining, Raman spectroscopy, Co-IP, GST-pull down, surface plasmon resonance, thermal stability assay, cellular immunofluorescence, gene knockdown, gene overexpression, WB, CCK-8, wound healing, transwell, qPCR, half-life assay, in-situ and LNM xenograft mice model were used.
Results
We found that polyamine metabolites were significantly elevated in the serum of EC with MS (ECWMS). Hyperlipidemia is a key factor in promoting ECWMS, and oleic acid (OA), a very important monounsaturated fatty acid was screened out from 12 common fatty acids as a key factor in upregulating the Ornithine Decarboxylase 1 (ODC1), the rate-limiting enzyme in polyamine metabolism, and downstream polyamines in the EC cell lines. Mechanistically, OA directly binds to HOXB9 and stabilizes it by preventing its interaction with E3 ligases Praja2. HOXB9 further interacts with ODC1 and competes with the interaction between Antizyme 1 (OAZ1) and ODC1 for proteasomal degradation. Downstream accumulation of polyamine metabolites, especially putrescine, further in turn inhibits the degradation of HOXB9. Targeting feedback of the HOXB9-ODC1-polyamine axis decreases polyamines and inhibits tumor proliferation and metastasis in vitro and in vivo. Clinically, the combination of ODC1, HOXB9, and obesity could better differentiate the prognosis. Multiplex IHC and Ramen spectroscopy indicated that the lipid-HOXB9-ODC1 axis also exists in ECWMS.
Conclusions
This study links fatty acid levels to polyamine accumulation, ultimately promoting EC progression and unveiling the mechanism for MS promoting EC progression. Targeting HOXB9 or ODC1 is expected to be a potential therapeutic strategy to control patients with MS-related refractory or progressive EC.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
National Science Foundation of China.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
1182P - Determination of tumor PSMA expression in prostate cancer from blood using a novel epigenomic liquid biopsy platform
Presenter: Praful Ravi
Session: Poster session 09
1183P - Impact of multicancer early detection (MCED) test on participant-reported outcomes (PRO) and behavioral intentions by cancer risk
Presenter: Christina Dilaveri
Session: Poster session 09
1184P - Early real-world experience with positive multi-cancer early detection (MCED) test cases and negative initial diagnostic work-up
Presenter: Candace Westgate
Session: Poster session 09
1185P - Clinical applications of a novel blood-based fragmentomics assay for lung cancer detection
Presenter: Marc Siegel
Session: Poster session 09
1186P - SmartCS-LPLLM: Enhancing early cancer detection through ctDNA methylation analysis leveraging large language models
Presenter: Li Chao
Session: Poster session 09
1187P - Molecular diagnosis of lung cancer via ctDNA and ctRNA detection on bronchoscopic fluid specimens from 31 patients: A retrospective analysis
Presenter: Vincent Fallet
Session: Poster session 09
1188P - Modeled economic and clinical impact of a multi-cancer early detection (MCED) test in a population with hereditary cancer syndromes
Presenter: Sana Raoof
Session: Poster session 09
1189P - Cancer genome interpreter: A data-driven tool for tumor mutation interpretation
Presenter: Santiago Demajo
Session: Poster session 09
1190P - Circulating tumor DNA from the tumor-draining pulmonary vein as a biomarker in resected non-small cell lung cancer
Presenter: Raphael Werner
Session: Poster session 09
1191P - Efficient lung cancer stage prediction and outcome informatics with Bayesian deep learning and MCMC method
Presenter: Maria Gkotzamanidou
Session: Poster session 09