Identification and immune features of cuproptosis-related molecular clusters in polycystic ovary syndrome

Abstract

Polycystic ovary syndrome (PCOS), a common reproductive endocrine disease, has clinically heterogeneous characteristics. Recently, cuproptosis causes several diseases by killing cells. Hence, we aimed to explore cuproptosis-related molecular clusters in PCOS and construct a prediction model. Based on the GSE5090, GSE43264, GSE98421, and GSE124226 datasets, an analysis of cuproptosis regulators and immune features in PCOS was conducted. In 25 cases of PCOS, the molecular clusters of cuproptosis-related genes and the immune cell infiltration associated with PCOS were investigated. Weighted gene co-expression network analysis was used to identify differentially expressed genes within clusters. Next, we compared the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting for deciding the optimum machine model. Validation of the predictive effectiveness was accomplished through nomogram, calibration curve, decision curve analysis, and using other two datasets. PCOS and non-PCOS controls differed in the dysregulation of cuproptosis-related genes and the activation of immunoreaction. Two cuproptosis-related molecular clusters associated with PCOS were identified. Significant heterogeneity was noted in immunity between the two clusters based on the analysis of immune infiltration. The immune-related pathways related to cluster-specific differentially expressed genes in Cluster1 were revealed by functional analysis. With a relatively low residual error and root mean square error and a higher area under the curve (1.000), the support vector machine model demonstrated optimal discriminative performance. An ultimate 5-gene-based support vector machine model was noted to perform satisfactorily in the other two validation datasets (area under the curve = 1.000 for both). Moreover, the nomogram, calibration curve, and decision curve analysis showed that PCOS subtypes can be accurately predicted. Our study results helped demonstrate a comprehensive understanding of the complex relationship between cuproptosis and PCOS and establish a promising prediction model for assessing the risk of cuproptosis in patients with PCOS.

Validation of mitochondrial biomarkers and immune dynamics in polycystic ovary syndrome

“…39 Several works have turned their attention toward the exploration of PCOS biomarkers through diverse machine learning algorithms. 13,40,41 For example, Gao et al integrated the BORUTA algorithm with LASSO regression analysis and identified TMEM54 and PLCG2 as potential PCOS biomarkers. 40 In a similar vein, Na et al utilized the LASSO and the SVM-RFE algorithm, identifying HDDC3 and SDC2 as hub genes.…”

“…Machine learning strategies have evolved into potent tools for probing intricate relationships within high‐dimensional datasets 39 . Several works have turned their attention toward the exploration of PCOS biomarkers through diverse machine learning algorithms 13,40,41 . For example, Gao et al.…”

Female Autoimmune Disorders with Infertility: A Narrative Review

“…Researchers had observed a physiological occurrence of antibodies directed at FSH in the serum of healthy non-pregnant women [50]. The production of auto-antibodies can be enhanced when an elevated level of auto-antigen is present, such as elevated FSH levels and AOA in cases of premature menopause [13]. Therefore, anti-FSH antibodies primarily tend to be naturally occurring antibodies rather than markers for autoimmunity against the FSH.…”

“…An autoimmune mechanism has been implicated in several cases of PCOS. The prevalence of AOA is increased in PCOS together with some organ and non-organ-specific auto-antibodies [13]. Additionally, a link between PCOS, autoimmune oophoritis, and premature ovarian failure (POF) has been established [14,15].…”