Identifying ovarian cancer with machine learning DNA methylation pattern analysis
Abstract The majority of patients with epithelial ovarian cancer (EOC) continue to be diagnosed at an advanced stage despite great advances in this disease treatment. To impact overall survival, we need better methods of EOC early diagnosis. We performed a case control study to predict high-grade se...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-05460-9 |
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| author | Jesus Gonzalez Bosquet Vincent M. Wagner Douglas Russo Henry D. Reyes Andreea M. Newtson David P. Bender Michael J. Goodheart |
| author_facet | Jesus Gonzalez Bosquet Vincent M. Wagner Douglas Russo Henry D. Reyes Andreea M. Newtson David P. Bender Michael J. Goodheart |
| author_sort | Jesus Gonzalez Bosquet |
| collection | DOAJ |
| description | Abstract The majority of patients with epithelial ovarian cancer (EOC) continue to be diagnosed at an advanced stage despite great advances in this disease treatment. To impact overall survival, we need better methods of EOC early diagnosis. We performed a case control study to predict high-grade serous cancer (HGSC) using artificial intelligence methodology and methylated DNA from surgical specimens. Initial prediction models with MethylNet were accurate but complex (AUC = 100%). We optimized these models by selecting the most informative probes with univariate ANOVA analyses first, and then multivariate lasso regression modelling. This step-wise approach resulted in 9 methylated probes predicting HGSC with an AUC of 100%. These models were validated with different analytics and with an independent DNA-methylation experiment with excellent performances. |
| format | Article |
| id | doaj-art-81cb02fe3a9f4cdc8189adaad7ef47d9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-81cb02fe3a9f4cdc8189adaad7ef47d92025-08-20T03:45:23ZengNature PortfolioScientific Reports2045-23222025-07-011511810.1038/s41598-025-05460-9Identifying ovarian cancer with machine learning DNA methylation pattern analysisJesus Gonzalez Bosquet0Vincent M. Wagner1Douglas Russo2Henry D. Reyes3Andreea M. Newtson4David P. Bender5Michael J. Goodheart6Department of Obstetrics and Gynecology, University of IowaDepartment of Obstetrics and Gynecology, University of IowaDivision of Urogynecology and Reconstructive Pelvic Surgery, Department of Obstetrics and Gynecology, University of ChicagoGPPC NetworkEndeavor HealthDepartment of Obstetrics and Gynecology, University of IowaDepartment of Obstetrics and Gynecology, University of IowaAbstract The majority of patients with epithelial ovarian cancer (EOC) continue to be diagnosed at an advanced stage despite great advances in this disease treatment. To impact overall survival, we need better methods of EOC early diagnosis. We performed a case control study to predict high-grade serous cancer (HGSC) using artificial intelligence methodology and methylated DNA from surgical specimens. Initial prediction models with MethylNet were accurate but complex (AUC = 100%). We optimized these models by selecting the most informative probes with univariate ANOVA analyses first, and then multivariate lasso regression modelling. This step-wise approach resulted in 9 methylated probes predicting HGSC with an AUC of 100%. These models were validated with different analytics and with an independent DNA-methylation experiment with excellent performances.https://doi.org/10.1038/s41598-025-05460-9 |
| spellingShingle | Jesus Gonzalez Bosquet Vincent M. Wagner Douglas Russo Henry D. Reyes Andreea M. Newtson David P. Bender Michael J. Goodheart Identifying ovarian cancer with machine learning DNA methylation pattern analysis Scientific Reports |
| title | Identifying ovarian cancer with machine learning DNA methylation pattern analysis |
| title_full | Identifying ovarian cancer with machine learning DNA methylation pattern analysis |
| title_fullStr | Identifying ovarian cancer with machine learning DNA methylation pattern analysis |
| title_full_unstemmed | Identifying ovarian cancer with machine learning DNA methylation pattern analysis |
| title_short | Identifying ovarian cancer with machine learning DNA methylation pattern analysis |
| title_sort | identifying ovarian cancer with machine learning dna methylation pattern analysis |
| url | https://doi.org/10.1038/s41598-025-05460-9 |
| work_keys_str_mv | AT jesusgonzalezbosquet identifyingovariancancerwithmachinelearningdnamethylationpatternanalysis AT vincentmwagner identifyingovariancancerwithmachinelearningdnamethylationpatternanalysis AT douglasrusso identifyingovariancancerwithmachinelearningdnamethylationpatternanalysis AT henrydreyes identifyingovariancancerwithmachinelearningdnamethylationpatternanalysis AT andreeamnewtson identifyingovariancancerwithmachinelearningdnamethylationpatternanalysis AT davidpbender identifyingovariancancerwithmachinelearningdnamethylationpatternanalysis AT michaeljgoodheart identifyingovariancancerwithmachinelearningdnamethylationpatternanalysis |