Prospective analysis of circulating metabolites and endometrial cancer risk

Dossus, Laure; Kouloura, Eirini; Biessy, Carine; Viallon, Vivian; Siskos, Alexandros P.; Dimou, Niki; Rinaldi, Sabina; Merritt, Melissa A.; Allen, Naomi E.; Fortner, Renée T.; Kaaks, Rudolf; Weiderpass, Elisabete; Gram, Inger Torhild; Rothwell, Joseph A.; Lécuyer, Lucie; Severi, Gianluca; Schulze, Matthias B.; Nøst, Therese Haugdahl; Crous‐Bou, Marta; Sánchez, María‐José; Amiano, Pilar; Colorado‐Yohar, Sandra; Gurrea, Aurelio Barricarte; Schmidt, Julie A.; Palli, Domenico; Agnoli, Claudia; Tumino, Rosario; Sacerdote, Carlotta; Mattiello, Amalia; Vermeulen, Roel; Heath, Alicia K.; Christakoudi, Sofia; Tsilidis, Konstantinos K.; Travis, Ruth C.; Gunter, Marc J.; Keun, Hector C.

doi:10.1016/j.ygyno.2021.06.001

Cited by 31 publications

(38 citation statements)

References 53 publications

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“…This study included all female EPIC participants (1) who provided a blood sample; (2) who were previously included in one of six case-control studies on cancer etiology nested within the EPIC cohort (on breast [ 1 ], endometrial [ 11 ], colorectal [ 12 ], kidney [ 13 ], liver [ 14 ], and gallbladder cancers) with available blood concentrations of acetylcarnitine, arginine, asparagine, PCs aa C36:3, ae C34:2, ae C36:2, ae C36:3, and ae C38:2 measured by the same targeted metabolomics approach; (3) who were included as control participants in these studies (i.e., free of cancer (except non-melanoma skin cancer) at the time of the diagnosis of the cases, using incidence-density sampling, and matched to cases by age, sex, study center, time of blood collection, fasting status at blood collection (except for kidney cancer study), menopausal status and exogenous hormone use at blood collection (for breast, endometrial, liver, and gallbladder studies), and phase of menstrual cycle (for breast and endometrial cancer studies)); and (4) whose samples were included in an analytical batch including at least 10 samples, to ensure proper normalization of metabolite concentrations (see the “Statistical analyses” section) ( N = 3163).…”

Section: Methodsmentioning

confidence: 99%

Lifestyle correlates of eight breast cancer-related metabolites: a cross-sectional study within the EPIC cohort

His

Viallon

Dossus

et al. 2021

BMC Med

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Background Metabolomics is a promising molecular tool for identifying novel etiological pathways leading to cancer. In an earlier prospective study among pre- and postmenopausal women not using exogenous hormones, we observed a higher risk of breast cancer associated with higher blood concentrations of one metabolite (acetylcarnitine) and a lower risk associated with higher blood concentrations of seven others (arginine, asparagine, phosphatidylcholines (PCs) aa C36:3, ae C34:2, ae C36:2, ae C36:3, and ae C38:2). Methods To identify determinants of these breast cancer-related metabolites, we conducted a cross-sectional analysis to identify their lifestyle and anthropometric correlates in 2358 women, who were previously included as controls in case-control studies nested within the European Prospective Investigation into Cancer and Nutrition cohort and not using exogenous hormones at blood collection. Associations of each metabolite concentration with 42 variables were assessed using linear regression models in a discovery set of 1572 participants. Significant associations were evaluated in a validation set (n = 786). Results For the metabolites previously associated with a lower risk of breast cancer, concentrations of PCs ae C34:2, C36:2, C36:3, and C38:2 were negatively associated with adiposity and positively associated with total and saturated fat intakes. PC ae C36:2 was also negatively associated with alcohol consumption and positively associated with two scores reflecting adherence to a healthy lifestyle. Asparagine concentration was negatively associated with adiposity. Arginine and PC aa C36:3 concentrations were not associated to any of the factors examined. For the metabolite previously associated with a higher risk of breast cancer, acetylcarnitine, a positive association with age was observed. Conclusions These associations may indicate possible mechanisms underlying associations between lifestyle and anthropometric factors, and risk of breast cancer. Further research is needed to identify potential non-lifestyle correlates of the metabolites investigated.

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Section: Methodsmentioning

confidence: 99%

Lifestyle correlates of eight breast cancer-related metabolites: a cross-sectional study within the EPIC cohort

His

Viallon

Dossus

et al. 2021

BMC Med

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“…Targeted metabolomics data acquired within the EPIC study and centralized at the International Agency for Research on Cancer (IARC) included 16,060 pre-diagnostic blood samples originating from eight case-control studies nested within EPIC (details in Section 4.1 ) on seven types of cancer: breast cancer (one study denoted by BREA; n = 3172 samples) [ 8 ], endometrial cancer (ENDO; n = 1706) [ 24 ], gallbladder cancer (GLBD; n = 112), liver cancer (LIVE; n = 596) [ 25 ], kidney cancer (KIDN; n = 1213) [ 26 ], prostate cancer (PROS; n = 6020) [ 9 , 27 ], and colorectal cancer (two studies denoted by CLRT1 and CLRT2; n = 946 and n = 2295, respectively). As displayed in Table 1 , samples collected at recruitment were assayed at IARC for BREA, LIVE, KIDN, PROS, and CLRT1, at the Helmholtz Zentrum (München, Germany) for CLRT2 and GLBD, and at the Imperial College London (UK) for ENDO.…”

Section: Resultsmentioning

confidence: 99%

A New Pipeline for the Normalization and Pooling of Metabolomics Data

et al. 2021

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Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

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“…High levels of ROS can promote tumor development by activating abelson tyrosine‐protein kinase 1 34 . A multicenter cohort study of patients with EC and healthy subjects showed a positive correlation between levels of LPC(20:4) in EC plasma and EC risk 35 . However, there have been no reports of biomarkers associated with EP.…”

Section: Discussionmentioning

confidence: 99%

“…34 A multicenter cohort study of patients with EC and healthy subjects showed a positive correlation between levels of LPC (20:4) in EC plasma and EC risk. 35 However, there have been no reports of biomarkers associated with EP. Therefore, our results provide new serum biomarkers with potential for the clinical diagnosis of EP.…”

Section: Evaluation Of the Diagnostic Performance Of The Biomarkersmentioning

confidence: 99%

A serum lipidomics study for the identification of specific biomarkers for endometrial polyps to distinguish them from endometrial cancer or hyperplasia

Yan

Zhao

Wei

et al. 2022

Intl Journal of Cancer

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Endometrial diseases, including endometrial polyps (EP), endometrial cancer (EC) and endometrial hyperplasia (EH), are common gynecological diseases that affect women of childbearing and perimenopausal age. Clinically, biopsy or imaging methods are usually used to screen and diagnose these diseases; however, due to the invasiveness and heterogeneity of these tests, a noninvasive, convenient, objective and accurate biomarker is needed for the differential diagnosis of EP, EC or EH. In the present study, serum samples from 326 patients with endometrial diseases and 225 healthy volunteers were analyzed using nontargeted lipidomics. A combination of multivariate and univariate analyses was used to identify and qualify six, eight and seven potential biomarkers in the sera from patients with EP, EC and EH, respectively. Using a logistic regression algorithm and receiver operating characteristic (ROC) curve analysis, a biomarker panel including four specific EP biomarkers, 6‐keto‐PGF1α, PA(37:4), LysoPC(20:1) and PS(36:0), showed good classification and diagnostic ability in distinguishing EP from EC or EH. The biomarker panel for distinguishing EP from EC yielded an area under the curve (AUC) of 0.915, sensitivity of 100% and specificity of 72.41%, while that for distinguishing EP from EH yielded an AUC of 1.000, sensitivity of 100% and specificity of 100%. The two diagnostic models also showed good diagnostic abilities in the validation set. Therefore, this biomarker panel can be used as a rapid diagnostic method to assist in imaging examinations and provide a reference for clinicians in the identification and diagnosis of endometrial diseases.

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Prospective analysis of circulating metabolites and endometrial cancer risk

Cited by 31 publications

References 53 publications

Lifestyle correlates of eight breast cancer-related metabolites: a cross-sectional study within the EPIC cohort

Lifestyle correlates of eight breast cancer-related metabolites: a cross-sectional study within the EPIC cohort

A New Pipeline for the Normalization and Pooling of Metabolomics Data

A serum lipidomics study for the identification of specific biomarkers for endometrial polyps to distinguish them from endometrial cancer or hyperplasia

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