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N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS)
BACKGROUND: Polycystic ovary syndrome (PCOS) is a multisystem-related disease whose pathophysiology is still unclear. Several regulators of N6-methyladenosine (m6A) modification were confirmed to play a regulatory role in PCOS. Nonetheless, the roles of m6A regulators in PCOS are not fully demonstra...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10091541/ https://www.ncbi.nlm.nih.gov/pubmed/37046273 http://dx.doi.org/10.1186/s13048-023-01147-9 |
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author | Zhou, Sihan Hua, Rui Quan, Song |
author_facet | Zhou, Sihan Hua, Rui Quan, Song |
author_sort | Zhou, Sihan |
collection | PubMed |
description | BACKGROUND: Polycystic ovary syndrome (PCOS) is a multisystem-related disease whose pathophysiology is still unclear. Several regulators of N6-methyladenosine (m6A) modification were confirmed to play a regulatory role in PCOS. Nonetheless, the roles of m6A regulators in PCOS are not fully demonstrated. MATERIALS AND METHODS: Four mRNA expression profiling microarrays were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed m6A regulators between PCOS and normal patients were identified by R software. A random forest modal and nomogram were developed to assess the relationship between m6A regulators and the occurrence risk of PCOS. A consensus clustering method was utilized to distinctly divide PCOS patients into two m6A subtypes (m6A cluster A/B). The patterns of differential expression and immune infiltration were explored between the two m6A clusters. RESULTS: In this study, 22 significant m6A regulators were identified between healthy controls and PCOS patients. The random forest model determined three optimal m6A regulators which are related to the occurrence risk of PCOS, including YTHDF1, RBM15 and METTL14. A nomogram was established based on these genes, and its predictive reliability was validated by decision curve analysis. The consensus clustering algorithm distinctly divided PCOS cases into two m6A subtypes. The ssGSEA algorithm found that the immune infiltration was markedly enriched in m6A cluster B than in cluster A. The m6A-pattern related differentially expressed genes (DEGs) of the two m6A subtypes were demonstrated by differential expression analysis. We found that they were enriched in immune-related genes and various infection pathways. Based on the m6A-pattern related DEGs, the PCOS patients were classified into two m6A-pattern related genomic subtypes (gene clusters A and B). CONCLUSIONS: The present study provided evidence concerning the different modification patterns of m6A regulators in PCOS compared with normal patients. This study will help clarify the overall impact of m6A modification patterns and related immune infiltration on PCOS. |
format | Online Article Text |
id | pubmed-10091541 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100915412023-04-13 N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS) Zhou, Sihan Hua, Rui Quan, Song J Ovarian Res Research BACKGROUND: Polycystic ovary syndrome (PCOS) is a multisystem-related disease whose pathophysiology is still unclear. Several regulators of N6-methyladenosine (m6A) modification were confirmed to play a regulatory role in PCOS. Nonetheless, the roles of m6A regulators in PCOS are not fully demonstrated. MATERIALS AND METHODS: Four mRNA expression profiling microarrays were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed m6A regulators between PCOS and normal patients were identified by R software. A random forest modal and nomogram were developed to assess the relationship between m6A regulators and the occurrence risk of PCOS. A consensus clustering method was utilized to distinctly divide PCOS patients into two m6A subtypes (m6A cluster A/B). The patterns of differential expression and immune infiltration were explored between the two m6A clusters. RESULTS: In this study, 22 significant m6A regulators were identified between healthy controls and PCOS patients. The random forest model determined three optimal m6A regulators which are related to the occurrence risk of PCOS, including YTHDF1, RBM15 and METTL14. A nomogram was established based on these genes, and its predictive reliability was validated by decision curve analysis. The consensus clustering algorithm distinctly divided PCOS cases into two m6A subtypes. The ssGSEA algorithm found that the immune infiltration was markedly enriched in m6A cluster B than in cluster A. The m6A-pattern related differentially expressed genes (DEGs) of the two m6A subtypes were demonstrated by differential expression analysis. We found that they were enriched in immune-related genes and various infection pathways. Based on the m6A-pattern related DEGs, the PCOS patients were classified into two m6A-pattern related genomic subtypes (gene clusters A and B). CONCLUSIONS: The present study provided evidence concerning the different modification patterns of m6A regulators in PCOS compared with normal patients. This study will help clarify the overall impact of m6A modification patterns and related immune infiltration on PCOS. BioMed Central 2023-04-12 /pmc/articles/PMC10091541/ /pubmed/37046273 http://dx.doi.org/10.1186/s13048-023-01147-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhou, Sihan Hua, Rui Quan, Song N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS) |
title | N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS) |
title_full | N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS) |
title_fullStr | N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS) |
title_full_unstemmed | N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS) |
title_short | N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS) |
title_sort | n6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in polycystic ovary syndrome (pcos) |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10091541/ https://www.ncbi.nlm.nih.gov/pubmed/37046273 http://dx.doi.org/10.1186/s13048-023-01147-9 |
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