Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysis
BackgroundPolycystic ovary syndrome (PCOS) is a common endocrine and metabolic condition impacting millions of women worldwide. This study sought to identify granulosa cell endoplasmic reticulum stress (GCERS)-related differentially expressed genes (DEGs) between women with PCOS and those without PC...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2024.1504015/full |
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| author | Yan Zhang Xiujuan Chen Yuan Lin Xiaoqing Liu Xiumei Xiong |
| author_facet | Yan Zhang Xiujuan Chen Yuan Lin Xiaoqing Liu Xiumei Xiong |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | BackgroundPolycystic ovary syndrome (PCOS) is a common endocrine and metabolic condition impacting millions of women worldwide. This study sought to identify granulosa cell endoplasmic reticulum stress (GCERS)-related differentially expressed genes (DEGs) between women with PCOS and those without PCOS using bioinformatics and to investigate the related molecular mechanisms.MethodsTwo datasets were downloaded from GEO and analysed using the limma package to identify DEGs in two groups—PCOS and normal granulosa cells. Enrichment analyses, including GO, KEGG, and GSEA, were then conducted on the DEGs. Differential immune infiltration was assessed using CIBERSORT and correlations with immune cell biomarkers were evaluated. Networks for protein-protein interactions, transcription factor-target genes, miRNA-target genes, and drug-target genes were constructed and visualized using Cytoscape to identify key hub gene nodes. Finally, key genes were analysed for differential expression and correlated.ResultsOverall, 127 co-DEGs were identified in the two datasets. Our study revealed that these DEGs were primarily associated with cell cycle arrest, p53-mediated signal transduction, drug response, and gland development, with molecular functions enriched in growth factor binding, collagen binding, and receptor protein kinase activity. GSEA revealed that the co-DEGs were primarily associated with immune and inflammatory pathways. Eleven hub genes—MMP9, SPI1, IGF2R, GPBAR1, PDGFA, BMPR1A, LIFR, PRKAA1, MSH2, CDC25C, and KCNH2—were identified through the PPI, TF target genes, miRNA target genes, and drug target gene networks.ConclusionWe identified several crucial genes and pathways linked to the onset and development of PCOS. Our findings offer a clear connection between PCOS and GCERS, clarify the molecular mechanisms driving PCOS progression, and offer new perspectives for discovering valuable therapeutic targets and potential biomarkers for the condition. |
| format | Article |
| id | doaj-art-14b5003873cf46ff9dd08d7825b90a08 |
| institution | Kabale University |
| issn | 2296-889X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Molecular Biosciences |
| spelling | doaj-art-14b5003873cf46ff9dd08d7825b90a082025-01-09T05:10:18ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2025-01-011110.3389/fmolb.2024.15040151504015Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysisYan ZhangXiujuan ChenYuan LinXiaoqing LiuXiumei XiongBackgroundPolycystic ovary syndrome (PCOS) is a common endocrine and metabolic condition impacting millions of women worldwide. This study sought to identify granulosa cell endoplasmic reticulum stress (GCERS)-related differentially expressed genes (DEGs) between women with PCOS and those without PCOS using bioinformatics and to investigate the related molecular mechanisms.MethodsTwo datasets were downloaded from GEO and analysed using the limma package to identify DEGs in two groups—PCOS and normal granulosa cells. Enrichment analyses, including GO, KEGG, and GSEA, were then conducted on the DEGs. Differential immune infiltration was assessed using CIBERSORT and correlations with immune cell biomarkers were evaluated. Networks for protein-protein interactions, transcription factor-target genes, miRNA-target genes, and drug-target genes were constructed and visualized using Cytoscape to identify key hub gene nodes. Finally, key genes were analysed for differential expression and correlated.ResultsOverall, 127 co-DEGs were identified in the two datasets. Our study revealed that these DEGs were primarily associated with cell cycle arrest, p53-mediated signal transduction, drug response, and gland development, with molecular functions enriched in growth factor binding, collagen binding, and receptor protein kinase activity. GSEA revealed that the co-DEGs were primarily associated with immune and inflammatory pathways. Eleven hub genes—MMP9, SPI1, IGF2R, GPBAR1, PDGFA, BMPR1A, LIFR, PRKAA1, MSH2, CDC25C, and KCNH2—were identified through the PPI, TF target genes, miRNA target genes, and drug target gene networks.ConclusionWe identified several crucial genes and pathways linked to the onset and development of PCOS. Our findings offer a clear connection between PCOS and GCERS, clarify the molecular mechanisms driving PCOS progression, and offer new perspectives for discovering valuable therapeutic targets and potential biomarkers for the condition.https://www.frontiersin.org/articles/10.3389/fmolb.2024.1504015/fullPCOSERSbioinformatic analysisdifferentially expressed geneshub genes |
| spellingShingle | Yan Zhang Xiujuan Chen Yuan Lin Xiaoqing Liu Xiumei Xiong Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysis Frontiers in Molecular Biosciences PCOS ERS bioinformatic analysis differentially expressed genes hub genes |
| title | Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysis |
| title_full | Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysis |
| title_fullStr | Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysis |
| title_full_unstemmed | Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysis |
| title_short | Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysis |
| title_sort | identification of crucial pathways and genes linked to endoplasmic reticulum stress in pcos through combined bioinformatic analysis |
| topic | PCOS ERS bioinformatic analysis differentially expressed genes hub genes |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2024.1504015/full |
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