Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve
Background During fertility treatment, diminished ovarian reserve (DOR) is a challenge that can seriously affect a patient’s reproductive potential. However, the pathogenesis of DOR is still unclear and its treatment options are limited. This study aimed to explore DOR’s molecular mechanisms. Method...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2020-08-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/9812.pdf |
| _version_ | 1797419514170703872 |
|---|---|
| author | Ruifen He Zhongying Zhao Yongxiu Yang Xiaolei Liang |
| author_facet | Ruifen He Zhongying Zhao Yongxiu Yang Xiaolei Liang |
| author_sort | Ruifen He |
| collection | DOAJ |
| description | Background During fertility treatment, diminished ovarian reserve (DOR) is a challenge that can seriously affect a patient’s reproductive potential. However, the pathogenesis of DOR is still unclear and its treatment options are limited. This study aimed to explore DOR’s molecular mechanisms. Methods We used R software to analyze the mRNA microarray dataset E-MTAB-391 downloaded from ArrayExpress, screen for differentially expressed genes (DEGs), and perform functional enrichment analyses. We also constructed the protein-protein interaction (PPI) and miRNA-mRNA networks. Ovarian granulosa cells (GCs) from women with DOR and the control group were collected to perform untargeted metabolomics analyses. Additionally, small molecule drugs were identified using the Connectivity Map database. Results We ultimately identified 138 DEGs. Our gene ontology (GO) analysis indicated that DEGs were mainly enriched in cytokine and steroid biosynthetic processes. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG), the DEGs were mainly enriched in the AGE (advanced glycation end-product)-RAGE (receptor for AGE) signaling pathway in diabetic complications and steroid biosynthesis. In the PPI network, we determined that JUN, EGR1, HMGCR, ATF3, and SQLE were hub genes that may be involved in steroid biosynthesis and inflammation. miRNAs also played a role in DOR development by regulating target genes. We validated the differences in steroid metabolism across GCs using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We selected 31 small molecules with potentially positive or negative influences on DOR development. Conclusion We found that steroidogenesis and inflammation played critical roles in DOR development, and our results provide promising insights for predicting and treating DOR. |
| first_indexed | 2024-03-09T06:48:24Z |
| format | Article |
| id | doaj.art-932d637e4dff48e9938757242a0a611c |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:48:24Z |
| publishDate | 2020-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-932d637e4dff48e9938757242a0a611c2023-12-03T10:32:00ZengPeerJ Inc.PeerJ2167-83592020-08-018e981210.7717/peerj.9812Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserveRuifen He0Zhongying Zhao1Yongxiu Yang2Xiaolei Liang3The First Clinical Medical College of Lanzhou University, Lanzhou, ChinaThe First Clinical Medical College of Lanzhou University, Lanzhou, ChinaDepartment of Obstetrics and Gynecology, The First Hospital of Lanzhou University, Key Laboratory for Gynecologic Oncology Gansu Province, Lanzhou, ChinaDepartment of Obstetrics and Gynecology, The First Hospital of Lanzhou University, Key Laboratory for Gynecologic Oncology Gansu Province, Lanzhou, ChinaBackground During fertility treatment, diminished ovarian reserve (DOR) is a challenge that can seriously affect a patient’s reproductive potential. However, the pathogenesis of DOR is still unclear and its treatment options are limited. This study aimed to explore DOR’s molecular mechanisms. Methods We used R software to analyze the mRNA microarray dataset E-MTAB-391 downloaded from ArrayExpress, screen for differentially expressed genes (DEGs), and perform functional enrichment analyses. We also constructed the protein-protein interaction (PPI) and miRNA-mRNA networks. Ovarian granulosa cells (GCs) from women with DOR and the control group were collected to perform untargeted metabolomics analyses. Additionally, small molecule drugs were identified using the Connectivity Map database. Results We ultimately identified 138 DEGs. Our gene ontology (GO) analysis indicated that DEGs were mainly enriched in cytokine and steroid biosynthetic processes. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG), the DEGs were mainly enriched in the AGE (advanced glycation end-product)-RAGE (receptor for AGE) signaling pathway in diabetic complications and steroid biosynthesis. In the PPI network, we determined that JUN, EGR1, HMGCR, ATF3, and SQLE were hub genes that may be involved in steroid biosynthesis and inflammation. miRNAs also played a role in DOR development by regulating target genes. We validated the differences in steroid metabolism across GCs using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We selected 31 small molecules with potentially positive or negative influences on DOR development. Conclusion We found that steroidogenesis and inflammation played critical roles in DOR development, and our results provide promising insights for predicting and treating DOR.https://peerj.com/articles/9812.pdfDiminished ovarian reserveBioinformatics analysisSteroidInflammationMetabolomics |
| spellingShingle | Ruifen He Zhongying Zhao Yongxiu Yang Xiaolei Liang Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve PeerJ Diminished ovarian reserve Bioinformatics analysis Steroid Inflammation Metabolomics |
| title | Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve |
| title_full | Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve |
| title_fullStr | Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve |
| title_full_unstemmed | Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve |
| title_short | Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve |
| title_sort | using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve |
| topic | Diminished ovarian reserve Bioinformatics analysis Steroid Inflammation Metabolomics |
| url | https://peerj.com/articles/9812.pdf |
| work_keys_str_mv | AT ruifenhe usingbioinformaticsandmetabolomicstoidentifyalteredgranulosacellsinpatientswithdiminishedovarianreserve AT zhongyingzhao usingbioinformaticsandmetabolomicstoidentifyalteredgranulosacellsinpatientswithdiminishedovarianreserve AT yongxiuyang usingbioinformaticsandmetabolomicstoidentifyalteredgranulosacellsinpatientswithdiminishedovarianreserve AT xiaoleiliang usingbioinformaticsandmetabolomicstoidentifyalteredgranulosacellsinpatientswithdiminishedovarianreserve |