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A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs
BACKGROUND: Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517861/ https://www.ncbi.nlm.nih.gov/pubmed/37745699 http://dx.doi.org/10.3389/fendo.2023.1193992 |
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author | Wang, Mingming An, Ke Huang, Jing Mprah, Richard Ding, Huanhuan |
author_facet | Wang, Mingming An, Ke Huang, Jing Mprah, Richard Ding, Huanhuan |
author_sort | Wang, Mingming |
collection | PubMed |
description | BACKGROUND: Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs. METHODS: Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples. RESULTS: Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets. CONCLUSION: In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment. |
format | Online Article Text |
id | pubmed-10517861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105178612023-09-24 A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs Wang, Mingming An, Ke Huang, Jing Mprah, Richard Ding, Huanhuan Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs. METHODS: Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples. RESULTS: Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets. CONCLUSION: In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment. Frontiers Media S.A. 2023-09-07 /pmc/articles/PMC10517861/ /pubmed/37745699 http://dx.doi.org/10.3389/fendo.2023.1193992 Text en Copyright © 2023 Wang, An, Huang, Mprah and Ding https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Endocrinology Wang, Mingming An, Ke Huang, Jing Mprah, Richard Ding, Huanhuan A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs |
title | A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs |
title_full | A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs |
title_fullStr | A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs |
title_full_unstemmed | A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs |
title_short | A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs |
title_sort | novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517861/ https://www.ncbi.nlm.nih.gov/pubmed/37745699 http://dx.doi.org/10.3389/fendo.2023.1193992 |
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