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Metabotyping human endometrioid endometrial adenocarcinoma reveals an implication of endocannabinoid metabolism.

ABSTRACT: Metabolomics, an essential technique in precision medicine, contributes to the molecular fingerprinting of tumours, further helping to understand their pathogenesis. In this work, using a LC-ESI-QTOF-MS/MS platform, we demonstrated the existence of a specific metabolomic signature which could define endometrioid endometrial carcinoma (EEC), arising the endocannabinoid system as a potential pathway involved in EC pathogenesis. Metabolomics could also shed light in the processes involved in myometrial invasion, proposing new targets for possible therapeutic intervention. Consequently, we also described a different metabolomic profile in surface endometrioid carcinoma and myometrial invasive front. We validated pathways disclosed by metabolomics by immunohistochemistry. Specifically, endocannabinoid and purine metabolism could be involved in tumor myometrial invasion.

SUBMITTER: Jove M

PROVIDER: S-EPMC5239558 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Metabotyping human endometrioid endometrial adenocarcinoma reveals an implication of endocannabinoid metabolism.

Jové Mariona M Gatius Sònia S Yeramian Andree A Portero-Otin Manuel M Eritja Núria N Santacana Maria M Colas Eva E Ruiz Maria M Pamplona Reinald R Matias-Guiu Xavier X

Oncotarget 20160801 32

Metabolomics, an essential technique in precision medicine, contributes to the molecular fingerprinting of tumours, further helping to understand their pathogenesis. In this work, using a LC-ESI-QTOF-MS/MS platform, we demonstrated the existence of a specific metabolomic signature which could define endometrioid endometrial carcinoma (EEC), arising the endocannabinoid system as a potential pathway involved in EC pathogenesis. Metabolomics could also shed light in the processes involved in myomet ...[more]

PMID: 27429042

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Project description:The objective of this study was to determine whether lower expression levels of DICER1 are associated with disease recurrence in patients with endometrioid endometrial cancer. The authors also explored DNA methylation and haploinsufficiency as potential mechanisms related to altered DICER1 expression in these tumors.DICER1 expression was assessed by quantitative polymerase chain reaction in a selected cohort of endometrioid endometrial tumors (N = 169). Loss of heterozygosity analyses were conducted using 2 single nucleotide polymorphisms, and combined bisulfate restriction analysis was used to assess methylation in the 5'-untranslated region of DICER1 in representative tumors. The correlations between DICER1 expression and clinicopathologic variables, including overall survival (OS) and disease-free survival (DFS), were assessed using nonparametric rank-sum tests and Cox proportional hazard models as appropriate. Survival distributions were described using the Kaplan-Meier method. A nested case-control analysis was conducted to confirm the association between transcript levels and disease recurrence.Lower DICER1 expression (hazard ratio [HR], 1.36; 95% confidence interval [CI], 1.05-1.75; P = .02) and advanced disease stage (HR, 2.79; 95%CI, 1.59-4.90; P < .001) were associated with worse DFS. Three variables were associated significantly with reduced OS: age (HR, 1.04; 95%CI, 1.02-1.06; P < .0001), advanced disease stage (HR, 6.41; 95%CI, 3.57-11.52; P < .0001), and high tumor grade (HR, 2.96; 95%CI, 1.46-5.99; P = .003). Nested case-control analyses confirmed that there were lower DICER1 transcript levels in patients who had recurrent disease (P = .01). Deletion of DICER1 sequences was an infrequent event (5% of analyzed patients), and no methylation was observed in the 5' DICER1 regulatory region.Lower DICER1 transcript levels were correlated with disease recurrence and worse DFS survival in patients with endometrioid endometrial cancer. The factors that influence DICER1 transcript levels in primary endometrial cancers remain unknown.

Project description:BackgroundLymph node metastasis (LNM) is a critical unfavorable prognostic factor in endometrial cancer (EC). At present, models involving molecular indicators that accurately predict LNM are still uncommon. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group for LNM.MethodsIn all, 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into a training cohort (used for building the model) and a validation cohort (used for validating the model) according to a predefined ratio of 7:3. Logistics regression analysis was used in the training cohort to screen out predictors related to LNM, after which a nomogram was developed to predict LNM in patients with EC. A calibration curve and consistency index (C-index) were used to estimate the performance of the model. A receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of the risk probability of LNM predicted by the model proposed in this study. Then, the prediction performance of different models and their discrimination abilities for identifying low-risk patients were compared.ResultLNM occurred in 87 and 42 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis showed that histological grade (P=0.022), myometrial invasion (P=0.002), lymphovascular space invasion (LVSI) (P=0.001), serum CA125 (P=0.008), Ki67 (P=0.012), estrogen receptor (ER) (0.009), and P53 (P=0.003) were associated with LNM; a nomogram was then successfully established on this basis. The internal and external calibration curves showed that the model fits well, and the C-index showed that the prediction accuracy of the model proposed in this study was better than that of the other models (the C-index of the training and validation cohorts was 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. Based on this threshold, the model showed good discrimination for identifying low-risk patients.ConclusionCombining molecular indicators based on classical clinical parameters can predict LNM of patients with EC more accurately. The nomogram proposed in this study showed good discrimination for identifying low-risk patients with LNM.

Dataset Information

Metabotyping human endometrioid endometrial adenocarcinoma reveals an implication of endocannabinoid metabolism.

Publications

Metabotyping human endometrioid endometrial adenocarcinoma reveals an implication of endocannabinoid metabolism.

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