Discovery of immune-related diagnostic biomarkers and construction of diagnostic model in varies polycystic ovary syndrome.

Aims: The various diagnostic criteria for polycystic ovary syndrome (PCOS) raised problem for PCOS research worldwide. PCOS has been demonstrated to be significantly associated with immune response. We aimed to identify several immune-related biomarkers and construct a nomogram model for diagnosis in PCOS.

Methods: The mRNA expression data were downloaded from Gene Expression Omnibus (GEO) database. Significant immune-related genes were identified to be the biomarkers for the diagnosis of PCOS using random forest model (RF), support vector machine model (SVM) and generalized linear model (GLM). The key biomarkers were selected from the optimal model and were utilized to construct a diagnostic nomogram. Receiver operating characteristic (ROC) curves was used to evaluate diagnostic ability of nomogram. Moreover, the relative proportion of 22 immune cell types was calculated by CIBERSORT algorithm.

Results: Four immune-related biomarkers (cAMP, S100A9, TLR8 and IL6R) were demonstrated to be highly expressed in PCOS. The nomogram constructed on the ground of the four key biomarkers showed perfect performance in diagnosis of PCOS, whose AUC were greater than 0.7. Higher infiltrating abundance of neutrophils, resting NK cells and activated dendritic cells were observed in PCOS and were tightly associated with the four key biomarkers.

Conclusions: This study identified several immune-related diagnostic biomarkers for PCOS patients. The diagnostic nomogram constructed based the biomarkers provide a theory foundation for clinical application. Multiple immune cells were associated with the expression of these four biomarkers and might played a vital role in the procession of PCOS.