Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning
ObjectiveEndometriosis (EMs) is a chronic inflammatory disease characterized by the presence of endometrial tissue in the non-uterine cavity, resulting in dysmenorrhea, pelvic pain, and infertility. Epidemiologic data have suggested the correlation between EMs and recurrent pregnancy loss (RPL), but...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1529507/full |
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| author | Jianhui Chen Qun Li Xiaofang Liu Fang Lin Yaling Jing Jiayan Yang Lianfang Zhao |
| author_facet | Jianhui Chen Qun Li Xiaofang Liu Fang Lin Yaling Jing Jiayan Yang Lianfang Zhao |
| author_sort | Jianhui Chen |
| collection | DOAJ |
| description | ObjectiveEndometriosis (EMs) is a chronic inflammatory disease characterized by the presence of endometrial tissue in the non-uterine cavity, resulting in dysmenorrhea, pelvic pain, and infertility. Epidemiologic data have suggested the correlation between EMs and recurrent pregnancy loss (RPL), but the pathological mechanism is unclear. This study aims to investigate the potential biomarkers and immune infiltration in EMs and RPL, providing a basis for early detection and treatment of the two diseases.MethodsTwo RPL and six EMs transcriptomic datasets from the Gene Expression Omnibus (GEO) database were used for differential analysis via limma package, followed by weighted gene co-expression network analysis (WGCNA) for key modules screening. Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. The diagnostic capabilities of the core genes were assessed by receiver operating characteristic (ROC) curves. Moreover, immune cell infiltration was estimated using CIBERSORTx, and the Cancer Genome Atlas (TCGA) database was employed to elucidate the role of key genes in endometrial carcinoma (EC).Results26 common differentially expressed genes (DEGs) were screened in both diseases, three of which were identified as common core genes (MAN2A1, PAPSS1, RIBC2) through the combination of WGCNA, PPI network, and machine learning-based feature selection. The area under the curve (AUC) values generated by the ROC indicates excellent diagnostic powers in both EMs and RPL. The key genes were found to be significantly associated with the infiltration of several immune cells. Interestingly, MAN2A1 and RIBC2 may play a predominant role in the development and prognostic stratification of EC.ConclusionWe identified three key genes linking EMs and RPL, emphasizing the heterogeneity of immune infiltration in the occurrence of both diseases. These findings may provide new mechanistic insights or therapeutic targets for further research of EMs and RPL. |
| format | Article |
| id | doaj-art-7f7ed40c08e149b3a065621ecd073978 |
| institution | Kabale University |
| issn | 2296-889X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj-art-7f7ed40c08e149b3a065621ecd0739782025-02-03T05:12:02ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2025-02-011210.3389/fmolb.2025.15295071529507Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learningJianhui Chen0Qun Li1Xiaofang Liu2Fang Lin3Yaling Jing4Jiayan Yang5Lianfang Zhao6Prenatal Diagnosis Center, Center of Reproductive Medicine, Suining Central Hospital, Suining, Sichuan, ChinaDepartment of Radiology, Suining Central Hospital, Suining, Sichuan, ChinaPrenatal Diagnosis Center, Center of Reproductive Medicine, Suining Central Hospital, Suining, Sichuan, ChinaPrenatal Diagnosis Center, Center of Reproductive Medicine, Suining Central Hospital, Suining, Sichuan, ChinaPrenatal Diagnosis Center, Center of Reproductive Medicine, Suining Central Hospital, Suining, Sichuan, ChinaPrenatal Diagnosis Center, Center of Reproductive Medicine, Suining Central Hospital, Suining, Sichuan, ChinaPrenatal Diagnosis Center, Center of Reproductive Medicine, Suining Central Hospital, Suining, Sichuan, ChinaObjectiveEndometriosis (EMs) is a chronic inflammatory disease characterized by the presence of endometrial tissue in the non-uterine cavity, resulting in dysmenorrhea, pelvic pain, and infertility. Epidemiologic data have suggested the correlation between EMs and recurrent pregnancy loss (RPL), but the pathological mechanism is unclear. This study aims to investigate the potential biomarkers and immune infiltration in EMs and RPL, providing a basis for early detection and treatment of the two diseases.MethodsTwo RPL and six EMs transcriptomic datasets from the Gene Expression Omnibus (GEO) database were used for differential analysis via limma package, followed by weighted gene co-expression network analysis (WGCNA) for key modules screening. Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. The diagnostic capabilities of the core genes were assessed by receiver operating characteristic (ROC) curves. Moreover, immune cell infiltration was estimated using CIBERSORTx, and the Cancer Genome Atlas (TCGA) database was employed to elucidate the role of key genes in endometrial carcinoma (EC).Results26 common differentially expressed genes (DEGs) were screened in both diseases, three of which were identified as common core genes (MAN2A1, PAPSS1, RIBC2) through the combination of WGCNA, PPI network, and machine learning-based feature selection. The area under the curve (AUC) values generated by the ROC indicates excellent diagnostic powers in both EMs and RPL. The key genes were found to be significantly associated with the infiltration of several immune cells. Interestingly, MAN2A1 and RIBC2 may play a predominant role in the development and prognostic stratification of EC.ConclusionWe identified three key genes linking EMs and RPL, emphasizing the heterogeneity of immune infiltration in the occurrence of both diseases. These findings may provide new mechanistic insights or therapeutic targets for further research of EMs and RPL.https://www.frontiersin.org/articles/10.3389/fmolb.2025.1529507/fullendometriosisimmune infiltrationrecurrent pregnancy lossbiomarkerendometrial cancer |
| spellingShingle | Jianhui Chen Qun Li Xiaofang Liu Fang Lin Yaling Jing Jiayan Yang Lianfang Zhao Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning Frontiers in Molecular Biosciences endometriosis immune infiltration recurrent pregnancy loss biomarker endometrial cancer |
| title | Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning |
| title_full | Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning |
| title_fullStr | Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning |
| title_full_unstemmed | Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning |
| title_short | Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning |
| title_sort | potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning |
| topic | endometriosis immune infiltration recurrent pregnancy loss biomarker endometrial cancer |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1529507/full |
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