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Annals of Surgical Treatment and Research ; : 153-161, 2019.
Article Dans Anglais | WPRIM | ID: wpr-739584

Résumé

PURPOSE: In intrahepatic cholangiocarcinoma (iCCA), genetic characteristics on ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)-PET scans are not yet clarified. If specific genetic characteristics were found to be related to FDG uptake in iCCA, we can predict molecular features based on the FDG uptake patterns and to distinguish different types of treatments. In this purpose, we analyzed RNA sequencing in iCCA patients to evaluate gene expression signatures associated with FDG uptake patterns. METHODS: We performed RNA sequencing of 22 cases iCCA who underwent preoperative ¹⁸F-FDG-PET, and analyzed the clinical and molecular features according to the maximum standard uptake value (SUVmax). Genes and biological pathway which are associated with SUVmax were analyzed. RESULTS: Patients with SUVmax higher than 9.0 (n = 9) had poorer disease-free survival than those with lower SUVmax (n = 13, P = 0.035). Genes related to glycolysis and gluconeogenesis, phosphorylation and cell cycle were significantly correlated with SUVmax (r ≥ 0.5). RRM2, which is related to the toxicity of Gemcitabine was positively correlated with SUVmax, and SLC27A2 which is associated with Cisplastin response was negatively correlated with SUVmax. According to the pathway analysis, cell cycle, cell division, hypoxia, inflammatory, and metabolism-related pathways were enriched in high SUVmax patients. CONCLUSION: The genomic features of gene expression and pathways can be predicted by FDG uptake features in iCCA. Patients with high FDG uptake have enriched cell cycle, metabolism and hypoxic pathways, which may lead to a more rational targeted treatment approach.


Sujets)
Humains , Hypoxie , Cycle cellulaire , Division cellulaire , Cholangiocarcinome , Survie sans rechute , Fluorodésoxyglucose F18 , Expression des gènes , Néoglucogenèse , Glycolyse , Métabolisme , Phosphorylation , Tomographie par émission de positons , Analyse de séquence d'ARN , Transcriptome
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