Center of Reproductive Medicine, Shengjing Hospital of China Medical University, No.36, SanHao Street, Shenyang, 110004, China.
Department of Neurology, The First Hospital of China Medical University, Shenyang, 110002, China.
J Ovarian Res. 2022 Jul 6;15(1):80. doi: 10.1186/s13048-022-01013-0.
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.
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.
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.
HDDC3 and SDC2 can serve as candidate biomarkers of PCOS and provide new insights into the molecular mechanisms of immune regulation in PCOS.
本研究旨在鉴定多囊卵巢综合征（PCOS）的新型生物标志物，并分析其在 PCOS 发病机制中免疫浸润的潜在作用。
从基因表达综合数据库中获取了五个数据集，即 GSE137684、GSE80432、GSE114419、GSE138518 和 GSE155489。从训练数据集中选择差异表达基因（DEGs）。组合最小绝对值收缩和选择算子逻辑回归模型和支持向量机递归特征消除算法筛选潜在的生物标志物。使用测试数据集验证这些生物标志物的表达水平，并计算曲线下面积（AUC）以分析其诊断价值。进行实时定量 PCR 以验证临床样本中生物标志物的表达。使用 CIBERSORT 评估差异免疫浸润，并评估生物标志物与浸润免疫细胞的相关性。
在此，在 PCOS 组和对照组之间鉴定出 1265 个 DEGs。PCOS 中与免疫反应和适应性免疫反应相关的基因集差异激活。我们鉴定的 PCOS 的两个诊断生物标志物为 HD 结构域包含 3（HDDC3）和 syndecan 2（SDC2；AUC 分别为 0.918 和 0.816）。使用 RT-qPCR 对临床样本中枢纽生物标志物的验证与生物信息学结果一致。免疫浸润分析表明，减少激活的肥大细胞（P=0.033）和增加嗜酸性粒细胞（P=0.040）可能是 PCOS 发病机制的一部分。HDDC3 与调节性 T 细胞（P=0.0064）、激活的肥大细胞（P=0.014）和单核细胞（P=0.024）呈正相关，但与 PCOS 中激活的记忆 CD4 T 细胞（P=0.016）呈负相关。此外，SDC2 与激活的肥大细胞（P=0.0021）、浆细胞（P=0.0051）和 M2 巨噬细胞（P=0.038）呈正相关，但与嗜酸性粒细胞（P=0.01）和中性粒细胞（P=0.031）呈负相关在 PCOS 中。
HDDC3 和 SDC2 可以作为 PCOS 的候选生物标志物，并为 PCOS 中免疫调节的分子机制提供新的见解。
