Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning
Abstract Premature ovarian insufficiency (POI) is a reproductive endocrine disorder characterized by infertility and perimenopausal syndrome, with a highly heterogeneous genetic etiology and its mechanism is not fully understood. Therefore, we utilized Oxford Nanopore Technology (ONT) for the first...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-38754-x |
| _version_ | 1797576767132663808 |
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| author | Zhaoyang Yu Mujun Li Weilong Peng |
| author_facet | Zhaoyang Yu Mujun Li Weilong Peng |
| author_sort | Zhaoyang Yu |
| collection | DOAJ |
| description | Abstract Premature ovarian insufficiency (POI) is a reproductive endocrine disorder characterized by infertility and perimenopausal syndrome, with a highly heterogeneous genetic etiology and its mechanism is not fully understood. Therefore, we utilized Oxford Nanopore Technology (ONT) for the first time to characterize the full-length transcript profile, and revealed biomarkers, pathway and molecular mechanisms for POI by bioinformatics analysis and machine learning. Ultimately, we identified 272 differentially expressed genes, 858 core genes, and 25 hub genes by analysis of differential expression, gene set enrichment, and protein–protein interactions. Seven candidate genes were identified based on the intersection features of the random forest and Boruta algorithm. qRT-PCR results indicated that COX5A, UQCRFS1, LCK, RPS2 and EIF5A exhibited consistent expression trends with sequencing data and have potential as biomarkers. Additionally, GSEA analysis revealed that the pathophysiology of POI is closely associated with inhibition of the PI3K-AKT pathway, oxidative phosphorylation and DNA damage repair, as well as activation of inflammatory and apoptotic pathways. Furthermore, we emphasize that downregulation of respiratory chain enzyme complex subunits and inhibition of oxidative phosphorylation pathways play crucial roles in the pathophysiology of POI. In conclusion, our utilization of long-read sequencing has refined the annotation information within the POI transcriptional profile. This valuable data provides novel insights for further exploration into molecular regulatory networks and potential biomarkers associated with POI. |
| first_indexed | 2024-03-10T21:58:26Z |
| format | Article |
| id | doaj.art-5f296f7b71a14b7da537bb5e9cf2df84 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T21:58:26Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5f296f7b71a14b7da537bb5e9cf2df842023-11-19T13:02:29ZengNature PortfolioScientific Reports2045-23222023-07-0113111010.1038/s41598-023-38754-xExploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learningZhaoyang Yu0Mujun Li1Weilong Peng2The First Affiliated Clinical College of Guangxi Medical UniversityReproductive Medicine Research Center, The First Affiliated Hospital of Guangxi Medical UniversitySchool of Computer Science and Cyber Engineering, Guangzhou UniversityAbstract Premature ovarian insufficiency (POI) is a reproductive endocrine disorder characterized by infertility and perimenopausal syndrome, with a highly heterogeneous genetic etiology and its mechanism is not fully understood. Therefore, we utilized Oxford Nanopore Technology (ONT) for the first time to characterize the full-length transcript profile, and revealed biomarkers, pathway and molecular mechanisms for POI by bioinformatics analysis and machine learning. Ultimately, we identified 272 differentially expressed genes, 858 core genes, and 25 hub genes by analysis of differential expression, gene set enrichment, and protein–protein interactions. Seven candidate genes were identified based on the intersection features of the random forest and Boruta algorithm. qRT-PCR results indicated that COX5A, UQCRFS1, LCK, RPS2 and EIF5A exhibited consistent expression trends with sequencing data and have potential as biomarkers. Additionally, GSEA analysis revealed that the pathophysiology of POI is closely associated with inhibition of the PI3K-AKT pathway, oxidative phosphorylation and DNA damage repair, as well as activation of inflammatory and apoptotic pathways. Furthermore, we emphasize that downregulation of respiratory chain enzyme complex subunits and inhibition of oxidative phosphorylation pathways play crucial roles in the pathophysiology of POI. In conclusion, our utilization of long-read sequencing has refined the annotation information within the POI transcriptional profile. This valuable data provides novel insights for further exploration into molecular regulatory networks and potential biomarkers associated with POI.https://doi.org/10.1038/s41598-023-38754-x |
| spellingShingle | Zhaoyang Yu Mujun Li Weilong Peng Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning Scientific Reports |
| title | Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning |
| title_full | Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning |
| title_fullStr | Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning |
| title_full_unstemmed | Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning |
| title_short | Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning |
| title_sort | exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning |
| url | https://doi.org/10.1038/s41598-023-38754-x |
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