BACKGROUND: This study aimed to explore the prognostic role of DNA methylation pattern in endometrial cancer (EC) patients. METHODS: Differentially methylated genes (DMGs) of EC patients with distinct survival from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylated genes as biomarkers for EC prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to construct a risk score model. A nomogram was built based on analysis combining the risk score model with clinicopathological signatures together, and then verified in the validation cohort and patients in our own center. RESULTS: In total, 157 DMGs were identified between different prognostic groups. Based on the LASSO analysis, five genes (GBP4, OR8K3, GABRA2, RIPPLY2, and TRBV5-7) were screened for the establishment of risk score model. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.824, 5 years: 0.926, and 7 years: 0.853). Multivariate analysis identified four independent risk factors including menopausal status (HR = 3.006, 95%CI: 1.062-8.511, p = 0.038), recurrence (HR = 2.116, 95%CI: 1.061-4.379, p = 0.046), lymph node metastasis (LNM, HR = 3.465, 95%CI: 1.225-9.807, p = 0.019), and five-DNA methylation risk model (HR = 3.654, 95%CI: 1.458-9.161, p = 0.006) in training cohort. The performance of the nomogram was good in the training (AUC = 0.828), validation (AUC = 0.866) and the whole cohorts (AUC = 0.843). Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan-Meier survival curve also proved to be significantly different (p < 0.01). The subgroup analysis in different stratifications indicated that the accuracy was high in different subgroups for age, histological type, tumor grade, and clinical stage (all p < 0.01). CONCLUSIONS: Briefly, our work established and verified a five-DNA methylation risk model, and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of EC patients for clinicians.
BACKGROUND: This study aimed to explore the prognostic role of DNA methylation pattern in endometrial cancer (EC) patients. METHODS: Differentially methylated genes (DMGs) of EC patients with distinct survival from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylated genes as biomarkers for EC prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to construct a risk score model. A nomogram was built based on analysis combining the risk score model with clinicopathological signatures together, and then verified in the validation cohort and patients in our own center. RESULTS: In total, 157 DMGs were identified between different prognostic groups. Based on the LASSO analysis, five genes (GBP4, OR8K3, GABRA2, RIPPLY2, and TRBV5-7) were screened for the establishment of risk score model. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.824, 5 years: 0.926, and 7 years: 0.853). Multivariate analysis identified four independent risk factors including menopausal status (HR = 3.006, 95%CI: 1.062-8.511, p = 0.038), recurrence (HR = 2.116, 95%CI: 1.061-4.379, p = 0.046), lymph node metastasis (LNM, HR = 3.465, 95%CI: 1.225-9.807, p = 0.019), and five-DNA methylation risk model (HR = 3.654, 95%CI: 1.458-9.161, p = 0.006) in training cohort. The performance of the nomogram was good in the training (AUC = 0.828), validation (AUC = 0.866) and the whole cohorts (AUC = 0.843). Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan-Meier survival curve also proved to be significantly different (p < 0.01). The subgroup analysis in different stratifications indicated that the accuracy was high in different subgroups for age, histological type, tumor grade, and clinical stage (all p < 0.01). CONCLUSIONS: Briefly, our work established and verified a five-DNA methylation risk model, and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of EC patients for clinicians.
Authors: Pan Du; Xiao Zhang; Chiang-Ching Huang; Nadereh Jafari; Warren A Kibbe; Lifang Hou; Simon M Lin Journal: BMC Bioinformatics Date: 2010-11-30 Impact factor: 3.169
Authors: Pietro Boccaletto; Magdalena A Machnicka; Elzbieta Purta; Pawel Piatkowski; Blazej Baginski; Tomasz K Wirecki; Valérie de Crécy-Lagard; Robert Ross; Patrick A Limbach; Annika Kotter; Mark Helm; Janusz M Bujnicki Journal: Nucleic Acids Res Date: 2018-01-04 Impact factor: 16.971