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Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis

BACKGROUND: Patients with polycystic ovary syndrome (PCOS) exhibit a chronic inflammatory state, which is often accompanied by immune, endocrine, and metabolic disorders. Clarification of the pathogenesis of PCOS and exploration of specific biomarkers from the perspective of immunology by evaluating...

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Autores principales: Gao, Mengge, Liu, Xiaohua, Du, Mengxuan, Gu, Heng, Xu, Hang, Zhong, Xingming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207797/
https://www.ncbi.nlm.nih.gov/pubmed/37226082
http://dx.doi.org/10.1186/s12884-023-05693-4
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author Gao, Mengge
Liu, Xiaohua
Du, Mengxuan
Gu, Heng
Xu, Hang
Zhong, Xingming
author_facet Gao, Mengge
Liu, Xiaohua
Du, Mengxuan
Gu, Heng
Xu, Hang
Zhong, Xingming
author_sort Gao, Mengge
collection PubMed
description BACKGROUND: Patients with polycystic ovary syndrome (PCOS) exhibit a chronic inflammatory state, which is often accompanied by immune, endocrine, and metabolic disorders. Clarification of the pathogenesis of PCOS and exploration of specific biomarkers from the perspective of immunology by evaluating the local infiltration of immune cells in the follicular microenvironment may provide critical insights into disease pathogenesis. METHODS: In this study, we evaluated immune cell subsets and gene expression in patients with PCOS using data from the Gene Expression Omnibus database and single-sample gene set enrichment analysis. RESULTS: In total, 325 differentially expressed genes were identified, among which TMEM54 and PLCG2 (area under the curve = 0.922) were identified as PCOS biomarkers. Immune cell infiltration analysis showed that central memory CD4(+) T cells, central memory CD8(+) T cells, effector memory CD4(+) T cells, γδ T cells, and type 17 T helper cells may affect the occurrence of PCOS. In addition, PLCG2 was highly correlated with γδ T cells and central memory CD4(+) T cells. CONCLUSIONS: Overall, TMEM54 and PLCG2 were identified as potential PCOS biomarkers by bioinformatics analysis. These findings established a basis for further exploration of the immunological mechanisms of PCOS and the identification of therapeutic targets. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-05693-4.
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spelling pubmed-102077972023-05-25 Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis Gao, Mengge Liu, Xiaohua Du, Mengxuan Gu, Heng Xu, Hang Zhong, Xingming BMC Pregnancy Childbirth Research BACKGROUND: Patients with polycystic ovary syndrome (PCOS) exhibit a chronic inflammatory state, which is often accompanied by immune, endocrine, and metabolic disorders. Clarification of the pathogenesis of PCOS and exploration of specific biomarkers from the perspective of immunology by evaluating the local infiltration of immune cells in the follicular microenvironment may provide critical insights into disease pathogenesis. METHODS: In this study, we evaluated immune cell subsets and gene expression in patients with PCOS using data from the Gene Expression Omnibus database and single-sample gene set enrichment analysis. RESULTS: In total, 325 differentially expressed genes were identified, among which TMEM54 and PLCG2 (area under the curve = 0.922) were identified as PCOS biomarkers. Immune cell infiltration analysis showed that central memory CD4(+) T cells, central memory CD8(+) T cells, effector memory CD4(+) T cells, γδ T cells, and type 17 T helper cells may affect the occurrence of PCOS. In addition, PLCG2 was highly correlated with γδ T cells and central memory CD4(+) T cells. CONCLUSIONS: Overall, TMEM54 and PLCG2 were identified as potential PCOS biomarkers by bioinformatics analysis. These findings established a basis for further exploration of the immunological mechanisms of PCOS and the identification of therapeutic targets. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-05693-4. BioMed Central 2023-05-24 /pmc/articles/PMC10207797/ /pubmed/37226082 http://dx.doi.org/10.1186/s12884-023-05693-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Gao, Mengge
Liu, Xiaohua
Du, Mengxuan
Gu, Heng
Xu, Hang
Zhong, Xingming
Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis
title Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis
title_full Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis
title_fullStr Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis
title_full_unstemmed Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis
title_short Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis
title_sort identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207797/
https://www.ncbi.nlm.nih.gov/pubmed/37226082
http://dx.doi.org/10.1186/s12884-023-05693-4
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