Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome
Abstract 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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | Journal of Ovarian Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13048-022-01013-0 |
| _version_ | 1797969366428418048 |
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| author | Zhijing Na Wen Guo Jiahui Song Di Feng Yuanyuan Fang Da Li |
| author_facet | Zhijing Na Wen Guo Jiahui Song Di Feng Yuanyuan Fang Da Li |
| author_sort | Zhijing Na |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-11T03:01:07Z |
| format | Article |
| id | doaj.art-d9af2d9b1931480d82cd3f73df3ab1c0 |
| institution | Directory Open Access Journal |
| issn | 1757-2215 |
| language | English |
| last_indexed | 2024-04-11T03:01:07Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Ovarian Research |
| spelling | doaj.art-d9af2d9b1931480d82cd3f73df3ab1c02023-01-02T14:15:39ZengBMCJournal of Ovarian Research1757-22152022-07-0115111310.1186/s13048-022-01013-0Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndromeZhijing Na0Wen Guo1Jiahui Song2Di Feng3Yuanyuan Fang4Da Li5Center of Reproductive Medicine, Shengjing Hospital of China Medical UniversityDepartment of Neurology, The First Hospital of China Medical UniversityCenter of Reproductive Medicine, Shengjing Hospital of China Medical UniversityEducation Center for Clinical Skills Practice of China Medical UniversityCenter of Reproductive Medicine, Shengjing Hospital of China Medical UniversityCenter of Reproductive Medicine, Shengjing Hospital of China Medical UniversityAbstract 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.https://doi.org/10.1186/s13048-022-01013-0Polycystic ovary syndromeBiomarkersImmune infiltrationMachine learning algorithmCIBERSORT |
| spellingShingle | Zhijing Na Wen Guo Jiahui Song Di Feng Yuanyuan Fang Da Li Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome Journal of Ovarian Research Polycystic ovary syndrome Biomarkers Immune infiltration Machine learning algorithm CIBERSORT |
| 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 | Polycystic ovary syndrome Biomarkers Immune infiltration Machine learning algorithm CIBERSORT |
| url | https://doi.org/10.1186/s13048-022-01013-0 |
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