Identification and Validation of Key m7G-Related Genes and Cuproptosis-Related Genes Related to Immunity in Polycystic Ovary Syndrome (PCOS) by Comprehensive Bioinformatics Analysis

Abstract Background Polycystic ovary syndrome (PCOS) is a complex endocrine disease that is one of the most common disease of reproductive age and is one of the important factors leading to infertility in young women, but the potential molecular mechanism remains unclear. In this study, we identified previously unrecognized m7G-related and cuproptosis-related genes that are significantly correlated with PCOS via bioinformatics. Materials and methods Three transcriptome profiles of granulosa cells of PCOS sample and normal samples were obtained from online databases. Comprehensive bioinformatic analyses, such as differential expression analysis, functional and pathway enrichment analysis, immune infiltration analysis, were utilized. Results Three PCOS datasets (GSE34526, GSE80432, and GSE137684) from the Gene Expression Omnibus (GEO) database were integrated and the background correction and normalization were performed by R language. The corrected gene cuproptosis-related genes and m7G-related genes respectively to obtain intersection genes. There were 12 intersection genes relate to cuproptosis-related genes and 21 intersection genes relate to m7G-related genes in comparison between PCOS group and normal group. After the correction gene crossed with the immune infiltrating gene, it crossed with 12 intersection genes relate to cuproptosis-related genes and 21 intersection genes relate to m7G-related genes respectively. Ultimately, it was identified that 8 genes related to cuproptosis are related to immune infiltration (CIGs), among which DLAT was the most significant (P = 0.049); 5 m7G related genes are associated with immune infiltration (MIGs), among which NUDT16 was the most significant (P = 0.0246). There were significant differences in clinicopathological characteristics between the PCOS group and the normal group by ROC curve analysis. Conclusions This study addresses the information gap in PCOS research about the interaction between immunology, m7G, and copper metabolism. According to the PCOS prediction model, the genes m7G and those associated to copper metabolism are crucial in the onset and progression of the condition. This study has further explored the microenvironment of ovarian granulosa cell of PCOS development and immune factor related genes. This work will support future research.