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Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome

BACKGROUND: In this study, we aimed to identify novel biomarkers for polycystic ovary syndrome (PCOS) and analyze their potential roles in immune infiltration during PCOS pathogenesis. METHODS: Five datasets, namely GSE137684, GSE80432, GSE114419, GSE138518, and GSE155489, were obtained from the Gen...

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Autores principales: Na, Zhijing, Guo, Wen, Song, Jiahui, Feng, Di, Fang, Yuanyuan, Li, Da
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258136/
https://www.ncbi.nlm.nih.gov/pubmed/35794640
http://dx.doi.org/10.1186/s13048-022-01013-0
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author Na, Zhijing
Guo, Wen
Song, Jiahui
Feng, Di
Fang, Yuanyuan
Li, Da
author_facet Na, Zhijing
Guo, Wen
Song, Jiahui
Feng, Di
Fang, Yuanyuan
Li, Da
author_sort Na, Zhijing
collection PubMed
description BACKGROUND: In this study, we aimed to identify novel biomarkers for polycystic ovary syndrome (PCOS) and analyze their potential roles in immune infiltration during PCOS pathogenesis. METHODS: Five datasets, namely GSE137684, GSE80432, GSE114419, GSE138518, and GSE155489, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were selected from the train datasets. The least absolute shrinkage and selection operator logistic regression model and support vector machine-recursive feature elimination algorithm were combined to screen potential biomarkers. The test datasets validated the expression levels of these biomarkers, and the area under the curve (AUC) was calculated to analyze their diagnostic value. Quantitative real-time PCR was conducted to verify biomarkers’ expression in clinical samples. CIBERSORT was used to assess differential immune infiltration, and the correlations of biomarkers with infiltrating immune cells were evaluated. RESULTS: Herein, 1265 DEGs were identified between PCOS and control groups. The gene sets related to immune response and adaptive immune response were differentially activated in PCOS. The two diagnostic biomarkers of PCOS identified by us were HD domain containing 3 (HDDC3) and syndecan 2 (SDC2; AUC, 0.918 and 0.816, respectively). The validation of hub biomarkers in clinical samples using RT-qPCR was consistent with bioinformatics results. Immune infiltration analysis indicated that decreased activated mast cells (P = 0.033) and increased eosinophils (P = 0.040) may be a part of the pathogenesis of PCOS. HDDC3 was positively correlated with T regulatory cells (P = 0.0064), activated mast cells (P = 0.014), and monocytes (P = 0.024) but negatively correlated with activated memory CD4 T cells (P = 0.016) in PCOS. In addition, SDC2 was positively correlated with activated mast cells (P = 0.0021), plasma cells (P = 0.0051), and M2 macrophages (P = 0.038) but negatively correlated with eosinophils (P = 0.01) and neutrophils (P = 0.031) in PCOS. CONCLUSION: HDDC3 and SDC2 can serve as candidate biomarkers of PCOS and provide new insights into the molecular mechanisms of immune regulation in PCOS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-022-01013-0.
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spelling pubmed-92581362022-07-07 Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome Na, Zhijing Guo, Wen Song, Jiahui Feng, Di Fang, Yuanyuan Li, Da J Ovarian Res Research BACKGROUND: In this study, we aimed to identify novel biomarkers for polycystic ovary syndrome (PCOS) and analyze their potential roles in immune infiltration during PCOS pathogenesis. METHODS: Five datasets, namely GSE137684, GSE80432, GSE114419, GSE138518, and GSE155489, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were selected from the train datasets. The least absolute shrinkage and selection operator logistic regression model and support vector machine-recursive feature elimination algorithm were combined to screen potential biomarkers. The test datasets validated the expression levels of these biomarkers, and the area under the curve (AUC) was calculated to analyze their diagnostic value. Quantitative real-time PCR was conducted to verify biomarkers’ expression in clinical samples. CIBERSORT was used to assess differential immune infiltration, and the correlations of biomarkers with infiltrating immune cells were evaluated. RESULTS: Herein, 1265 DEGs were identified between PCOS and control groups. The gene sets related to immune response and adaptive immune response were differentially activated in PCOS. The two diagnostic biomarkers of PCOS identified by us were HD domain containing 3 (HDDC3) and syndecan 2 (SDC2; AUC, 0.918 and 0.816, respectively). The validation of hub biomarkers in clinical samples using RT-qPCR was consistent with bioinformatics results. Immune infiltration analysis indicated that decreased activated mast cells (P = 0.033) and increased eosinophils (P = 0.040) may be a part of the pathogenesis of PCOS. HDDC3 was positively correlated with T regulatory cells (P = 0.0064), activated mast cells (P = 0.014), and monocytes (P = 0.024) but negatively correlated with activated memory CD4 T cells (P = 0.016) in PCOS. In addition, SDC2 was positively correlated with activated mast cells (P = 0.0021), plasma cells (P = 0.0051), and M2 macrophages (P = 0.038) but negatively correlated with eosinophils (P = 0.01) and neutrophils (P = 0.031) in PCOS. CONCLUSION: HDDC3 and SDC2 can serve as candidate biomarkers of PCOS and provide new insights into the molecular mechanisms of immune regulation in PCOS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-022-01013-0. BioMed Central 2022-07-06 /pmc/articles/PMC9258136/ /pubmed/35794640 http://dx.doi.org/10.1186/s13048-022-01013-0 Text en © The Author(s) 2022 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
Na, Zhijing
Guo, Wen
Song, Jiahui
Feng, Di
Fang, Yuanyuan
Li, Da
Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome
title Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome
title_full Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome
title_fullStr Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome
title_full_unstemmed Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome
title_short Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome
title_sort identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258136/
https://www.ncbi.nlm.nih.gov/pubmed/35794640
http://dx.doi.org/10.1186/s13048-022-01013-0
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