Dataset Information

Identification of Metabolomic Biomarkers for Endometrial Cancer and Its Recurrence after Surgery in Postmenopausal Women.

ABSTRACT: Endometrial cancer (EC) is the most frequent gynecological cancer in developed countries. Most EC occurs after menopause and is diagnosed as endometrioid (type I) carcinomas, which exhibit a favorable prognosis. In contrast, non-endometrioid (type II) carcinomas such as serous tumors have a poor prognosis. Our goal was to identify novel blood-based markers associated with EC subtypes and recurrence after surgery in postmenopausal women. Using mass spectrometry-based untargeted metabolomics, we examined preoperative serum metabolites among control women (n?=?18) and those with non-recurrent (NR) and recurrent (R) cases of type I endometrioid (n?=?24) and type II serous (n?=?12) carcinomas. R and NR cases were similar with respect to pathological characteristics, body mass index, and age. A total of 1,592 compounds were analyzed including 14 different lipid classes. When we compared EC cases with controls, 137 metabolites were significantly different. A combination of spermine and isovalerate resulted in an age-adjusted area under the receiver-operating characteristic curve (AUC_adj) of 0.914 (P?adj of 0.901 (P?

SUBMITTER: Audet-Delage Y

PROVIDER: S-EPMC5857535 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identification of Metabolomic Biomarkers for Endometrial Cancer and Its Recurrence after Surgery in Postmenopausal Women.

Audet-Delage Yannick Y Villeneuve Lyne L Grégoire Jean J Plante Marie M Guillemette Chantal C

Frontiers in endocrinology 20180312

Endometrial cancer (EC) is the most frequent gynecological cancer in developed countries. Most EC occurs after menopause and is diagnosed as endometrioid (type I) carcinomas, which exhibit a favorable prognosis. In contrast, non-endometrioid (type II) carcinomas such as serous tumors have a poor prognosis. Our goal was to identify novel blood-based markers associated with EC subtypes and recurrence after surgery in postmenopausal women. Using mass spectrometry-based untargeted metabolomics, we e ...[more]

PMID: 29593653

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Project description:ImportanceEndometrial carcinoma (EC) is the most commonly diagnosed gynecologic cancer. Its early detection is advisable because 20% of women have advanced disease at the time of diagnosis.ObjectiveTo clinically validate a metabolomics-based classification algorithm as a screening test for EC.Design, setting, and participantsThis diagnostic study enrolled 2 cohorts. A multicenter prospective cohort, with 50 cases (postmenopausal women with EC; International Federation of Gynecology and Obstetrics stage I-III and grade G1-G3) and 70 controls (no EC but matched on age, years from menopause, tobacco use, and comorbidities), was used to train multiple classification models. The accuracy of each trained model was then used as a statistical weight to produce an ensemble machine learning algorithm for testing, which was validated with a subsequent prospective cohort of 1430 postmenopausal women. The study was conducted at the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy) and Lega Italiana per la Lotta contro i Tumori clinic in Avellino (Italy). Data collection was conducted from January 2018 to February 2019, and analysis was conducted from January to March 2019.Main outcomes and measuresThe presence or absence of EC based on evaluation of the blood metabolome. Metabolites were extracted from dried blood samples from all participants and analyzed by gas chromatography-mass spectrometry. A confusion matrix was used to summarize test results. Performance indices included sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy. Confirmation or exclusion of EC in women with a positive test result was by means of hysteroscopy. Participants with negative results were followed up 1 year after enrollment to investigate the appearance of EC signs.ResultsThe study population consisted of 1550 postmenopausal women. The mean (SD) age was 68.2 (11.7) years for participants with no EC in the training cohort, 69.4 (13.8) years for women with EC in the training cohort, and 59.7 (7.7) years for women in the validation cohort. Application of the ensemble machine learning to the validation cohort resulted in 16 true-positives, 2 false-positives, and 0 false-negatives, and it correctly classified more than 99% of samples. Disease prevalence was 1.12% (16 of 1430).Conclusions and relevanceIn this study, dried blood metabolomic profile was used to assess the presence or absence of EC in postmenopausal women not receiving hormonal therapy with greater than 99% accuracy.

Dataset Information

Identification of Metabolomic Biomarkers for Endometrial Cancer and Its Recurrence after Surgery in Postmenopausal Women.

Publications

Identification of Metabolomic Biomarkers for Endometrial Cancer and Its Recurrence after Surgery in Postmenopausal Women.

Similar Datasets

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