Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis
Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduc...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-11-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1290036/full |
| _version_ | 1797403378438897664 |
|---|---|
| author | Haolong Zhang Haoling Zhang Huadi Yang Ahmad Naqib Shuid Doblin Sandai Xingbei Chen |
| author_facet | Haolong Zhang Haoling Zhang Huadi Yang Ahmad Naqib Shuid Doblin Sandai Xingbei Chen |
| author_sort | Haolong Zhang |
| collection | DOAJ |
| description | Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application.Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103.Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells.Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment. |
| first_indexed | 2024-03-09T02:38:17Z |
| format | Article |
| id | doaj.art-4ca0a131507f493a830907714a58e5d4 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-09T02:38:17Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-4ca0a131507f493a830907714a58e5d42023-12-06T07:58:56ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-11-011410.3389/fgene.2023.12900361290036Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosisHaolong Zhang0Haoling Zhang1Huadi Yang2Ahmad Naqib Shuid3Doblin Sandai4Xingbei Chen5Department of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, MalaysiaDepartment of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, MalaysiaThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, ChinaDepartment of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, MalaysiaDepartment of Community Health, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, MalaysiaThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, ChinaBackground: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application.Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103.Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells.Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.https://www.frontiersin.org/articles/10.3389/fgene.2023.1290036/fullendometriosiscombined biomarkersmachine learningpredictivediagnostic |
| spellingShingle | Haolong Zhang Haoling Zhang Huadi Yang Ahmad Naqib Shuid Doblin Sandai Xingbei Chen Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis Frontiers in Genetics endometriosis combined biomarkers machine learning predictive diagnostic |
| title | Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis |
| title_full | Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis |
| title_fullStr | Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis |
| title_full_unstemmed | Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis |
| title_short | Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis |
| title_sort | machine learning based integrated identification of predictive combined diagnostic biomarkers for endometriosis |
| topic | endometriosis combined biomarkers machine learning predictive diagnostic |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1290036/full |
| work_keys_str_mv | AT haolongzhang machinelearningbasedintegratedidentificationofpredictivecombineddiagnosticbiomarkersforendometriosis AT haolingzhang machinelearningbasedintegratedidentificationofpredictivecombineddiagnosticbiomarkersforendometriosis AT huadiyang machinelearningbasedintegratedidentificationofpredictivecombineddiagnosticbiomarkersforendometriosis AT ahmadnaqibshuid machinelearningbasedintegratedidentificationofpredictivecombineddiagnosticbiomarkersforendometriosis AT doblinsandai machinelearningbasedintegratedidentificationofpredictivecombineddiagnosticbiomarkersforendometriosis AT xingbeichen machinelearningbasedintegratedidentificationofpredictivecombineddiagnosticbiomarkersforendometriosis |