Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis
Abstract Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting women of childbearing age, and we aimed to reveal its underlying molecular mechanisms. Gene expression profiles from GSE138518 and GSE155489, and single-cell RNA sequencing (scRNA-seq) data from PRJNA600740 were coll...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81110-w |
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| author | Xiaolin Zhu Yanhua Han Yuenan Feng Yuanli Shan Chang Liu Kexin Wang Xiaoke Li Shidi Zhang Yaguang Han |
| author_facet | Xiaolin Zhu Yanhua Han Yuenan Feng Yuanli Shan Chang Liu Kexin Wang Xiaoke Li Shidi Zhang Yaguang Han |
| author_sort | Xiaolin Zhu |
| collection | DOAJ |
| description | Abstract Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting women of childbearing age, and we aimed to reveal its underlying molecular mechanisms. Gene expression profiles from GSE138518 and GSE155489, and single-cell RNA sequencing (scRNA-seq) data from PRJNA600740 were collected and subjected to bioinformatics analysis to identify the complex molecular mechanisms of PCOS. The expression of genes was detected by RT-qPCR. Through differential analysis, we identified 230 common differentially expressed genes (DEGs) in GSE138518 and GSE155489. GSEA results showed significant enrichment of purine metabolism and oocyte meiosis in the control group, while GSVA results indicated significant activation of ECM receptor interaction, and antigen processing and presentation in PCOS. Weighted gene co-expression network analysis revealed 7 co-expression modules, with the bisque4 module showing the highest positive correlation with PCOS. Enrichment analysis revealed that genes in the bisque4 module were mainly involved in the PI3K-Akt signaling pathway, calcium signaling pathway, and Ras signaling pathway. Pseudotime trajectory analysis of cell subpopulations revealed the potential developmental trajectory of PCOS. The gene expression consistent with the potential developmental trajectory was validated by RT-qPCR. Our study, by analyzing multiple datasets, has revealed the complex molecular network of PCOS, offering new insights into understanding its pathophysiological basis. |
| format | Article |
| id | doaj-art-219b0fecef2c4e5d9db0aea35be31a5c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-219b0fecef2c4e5d9db0aea35be31a5c2025-01-26T12:34:08ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-024-81110-wIdentification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysisXiaolin Zhu0Yanhua Han1Yuenan Feng2Yuanli Shan3Chang Liu4Kexin Wang5Xiaoke Li6Shidi Zhang7Yaguang Han8The Second Affiliated Hospital of Heilongjiang, University of Traditional Chinese MedicineThe First Affiliated Hospital of Heilongjiang University of Traditional Chinese MedicineHeilongjiang University of Traditional Chinese MedicineThe Second Affiliated Hospital of Heilongjiang, University of Traditional Chinese MedicineDepartment of Traditional Chinese Medicine, Beijing University Third HospitalHeilongjiang University of Traditional Chinese MedicineHeilongjiang University of Traditional Chinese MedicineHeilongjiang University of Traditional Chinese MedicineThe First Affiliated Hospital of Heilongjiang University of Traditional Chinese MedicineAbstract Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting women of childbearing age, and we aimed to reveal its underlying molecular mechanisms. Gene expression profiles from GSE138518 and GSE155489, and single-cell RNA sequencing (scRNA-seq) data from PRJNA600740 were collected and subjected to bioinformatics analysis to identify the complex molecular mechanisms of PCOS. The expression of genes was detected by RT-qPCR. Through differential analysis, we identified 230 common differentially expressed genes (DEGs) in GSE138518 and GSE155489. GSEA results showed significant enrichment of purine metabolism and oocyte meiosis in the control group, while GSVA results indicated significant activation of ECM receptor interaction, and antigen processing and presentation in PCOS. Weighted gene co-expression network analysis revealed 7 co-expression modules, with the bisque4 module showing the highest positive correlation with PCOS. Enrichment analysis revealed that genes in the bisque4 module were mainly involved in the PI3K-Akt signaling pathway, calcium signaling pathway, and Ras signaling pathway. Pseudotime trajectory analysis of cell subpopulations revealed the potential developmental trajectory of PCOS. The gene expression consistent with the potential developmental trajectory was validated by RT-qPCR. Our study, by analyzing multiple datasets, has revealed the complex molecular network of PCOS, offering new insights into understanding its pathophysiological basis.https://doi.org/10.1038/s41598-024-81110-wPolycystic ovary syndromePseudotime trajectoryWGCNABioinformatics |
| spellingShingle | Xiaolin Zhu Yanhua Han Yuenan Feng Yuanli Shan Chang Liu Kexin Wang Xiaoke Li Shidi Zhang Yaguang Han Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis Scientific Reports Polycystic ovary syndrome Pseudotime trajectory WGCNA Bioinformatics |
| title | Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis |
| title_full | Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis |
| title_fullStr | Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis |
| title_full_unstemmed | Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis |
| title_short | Identification of molecular characteristics in polycystic ovary syndrome using single-cell and transcriptome analysis |
| title_sort | identification of molecular characteristics in polycystic ovary syndrome using single cell and transcriptome analysis |
| topic | Polycystic ovary syndrome Pseudotime trajectory WGCNA Bioinformatics |
| url | https://doi.org/10.1038/s41598-024-81110-w |
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