Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning
Polycystic ovary syndrome (PCOS) is a complex endocrine metabolic disorder that affects 5–10% of women of reproductive age. The endometrium of women with PCOS has altered immune cells resulting in chronic low-grade inflammation, which attribute to recurrent implantation failure (RIF). In this study,...
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| Format: | Article |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1175384/full |
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| author | Wenhui Chen Wenhui Chen Wenhui Chen Qingling Yang Qingling Yang Qingling Yang Linli Hu Linli Hu Linli Hu Mengchen Wang Mengchen Wang Mengchen Wang Ziyao Yang Ziyao Yang Ziyao Yang Xinxin Zeng Xinxin Zeng Xinxin Zeng Yingpu Sun Yingpu Sun Yingpu Sun |
| author_facet | Wenhui Chen Wenhui Chen Wenhui Chen Qingling Yang Qingling Yang Qingling Yang Linli Hu Linli Hu Linli Hu Mengchen Wang Mengchen Wang Mengchen Wang Ziyao Yang Ziyao Yang Ziyao Yang Xinxin Zeng Xinxin Zeng Xinxin Zeng Yingpu Sun Yingpu Sun Yingpu Sun |
| author_sort | Wenhui Chen |
| collection | DOAJ |
| description | Polycystic ovary syndrome (PCOS) is a complex endocrine metabolic disorder that affects 5–10% of women of reproductive age. The endometrium of women with PCOS has altered immune cells resulting in chronic low-grade inflammation, which attribute to recurrent implantation failure (RIF). In this study, we obtained three PCOS and RIF datasets respectively from the Gene Expression Omnibus (GEO) database. By analyzing differentially expressed genes (DEGs) and module genes using weighted gene co-expression networks (WGCNA), functional enrichment analysis, and three machine learning algorithms, we identified twelve diseases shared genes, and two diagnostic genes, including GLIPR1 and MAMLD1. PCOS and RIF validation datasets were assessed using the receiver operating characteristic (ROC) curve, and ideal area under the curve (AUC) values were obtained for each disease. Besides, we collected granulosa cells from healthy and PCOS infertile women, and endometrial tissues of healthy and RIF patients. RT-PCR was used to validate the reliability of GLIPR1 and MAMLD1. Furthermore, we performed gene set enrichment analysis (GSEA) and immune infiltration to explore the underlying mechanism of PCOS and RIF cooccurrence. Through the functional enrichment of twelve shared genes and two diagnostic genes, we found that both PCOS and RIF patients had disturbances in metabolites related to the TCA cycle, which eventually led to the massive activation of immune cells. |
| format | Article |
| id | doaj-art-59e04ba2124040ebbc021936f30a70e4 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-59e04ba2124040ebbc021936f30a70e42025-08-25T12:33:05ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-05-011410.3389/fimmu.2023.11753841175384Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learningWenhui Chen0Wenhui Chen1Wenhui Chen2Qingling Yang3Qingling Yang4Qingling Yang5Linli Hu6Linli Hu7Linli Hu8Mengchen Wang9Mengchen Wang10Mengchen Wang11Ziyao Yang12Ziyao Yang13Ziyao Yang14Xinxin Zeng15Xinxin Zeng16Xinxin Zeng17Yingpu Sun18Yingpu Sun19Yingpu Sun20Center for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Provincial Obstetrical and Gynecological Diseases (Reproductive Medicine) Clinical Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaCenter for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Provincial Obstetrical and Gynecological Diseases (Reproductive Medicine) Clinical Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaCenter for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Provincial Obstetrical and Gynecological Diseases (Reproductive Medicine) Clinical Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaCenter for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Provincial Obstetrical and Gynecological Diseases (Reproductive Medicine) Clinical Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaCenter for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Provincial Obstetrical and Gynecological Diseases (Reproductive Medicine) Clinical Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaCenter for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Provincial Obstetrical and Gynecological Diseases (Reproductive Medicine) Clinical Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaCenter for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaHenan Provincial Obstetrical and Gynecological Diseases (Reproductive Medicine) Clinical Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaPolycystic ovary syndrome (PCOS) is a complex endocrine metabolic disorder that affects 5–10% of women of reproductive age. The endometrium of women with PCOS has altered immune cells resulting in chronic low-grade inflammation, which attribute to recurrent implantation failure (RIF). In this study, we obtained three PCOS and RIF datasets respectively from the Gene Expression Omnibus (GEO) database. By analyzing differentially expressed genes (DEGs) and module genes using weighted gene co-expression networks (WGCNA), functional enrichment analysis, and three machine learning algorithms, we identified twelve diseases shared genes, and two diagnostic genes, including GLIPR1 and MAMLD1. PCOS and RIF validation datasets were assessed using the receiver operating characteristic (ROC) curve, and ideal area under the curve (AUC) values were obtained for each disease. Besides, we collected granulosa cells from healthy and PCOS infertile women, and endometrial tissues of healthy and RIF patients. RT-PCR was used to validate the reliability of GLIPR1 and MAMLD1. Furthermore, we performed gene set enrichment analysis (GSEA) and immune infiltration to explore the underlying mechanism of PCOS and RIF cooccurrence. Through the functional enrichment of twelve shared genes and two diagnostic genes, we found that both PCOS and RIF patients had disturbances in metabolites related to the TCA cycle, which eventually led to the massive activation of immune cells.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1175384/fullPCOSRIF (Recurrent Implantation Failure)integrated transcriptomic analysismachine learningTCA cycle |
| spellingShingle | Wenhui Chen Wenhui Chen Wenhui Chen Qingling Yang Qingling Yang Qingling Yang Linli Hu Linli Hu Linli Hu Mengchen Wang Mengchen Wang Mengchen Wang Ziyao Yang Ziyao Yang Ziyao Yang Xinxin Zeng Xinxin Zeng Xinxin Zeng Yingpu Sun Yingpu Sun Yingpu Sun Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning Frontiers in Immunology PCOS RIF (Recurrent Implantation Failure) integrated transcriptomic analysis machine learning TCA cycle |
| title | Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning |
| title_full | Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning |
| title_fullStr | Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning |
| title_full_unstemmed | Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning |
| title_short | Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning |
| title_sort | shared diagnostic genes and potential mechanism between pcos and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning |
| topic | PCOS RIF (Recurrent Implantation Failure) integrated transcriptomic analysis machine learning TCA cycle |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1175384/full |
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