Abstract
Endometriosis (EM) is a chronic disease that can cause pain and infertility in patients. As is well known, immune cell infiltrations (ICIs) play important roles in the pathogenesis of EM. However, the pathogenesis and biomarkers of EM that can be used in clinical practice and their relationship with ICIs still need to be elucidated. The gene expression datasets of EM and the healthy control were obtained from the Gene Expression Omnibus (GEO). To identify the central modules and explore the correlation between the gene network and EM, weighted gene co-expression network analysis (WGCNA) was executed. The hub genes were screened using machine learning. The qRT-PCR results showed that only CHMP4C and KAT2B differentially expressed in ectopic tissues compared to the normal. Subsequently, the samples were clustered based on the expression of CHMP4C and KAT2B. Depending on the differential expression genes of the two 2rG Clusters, the samples were divided into two gene Clusters. Significant differences in immune cell infiltrations were observed among the two 2rG Clusters and the two gene Clusters. Furthermore, varied immune checkpoint genes were shown to be correlated with EM. The qRT-PCR results showed that the two genes were significantly related to the ICI genes in EM. Hub genes CHMP4C and KAT2B are involved in the pathogenesis of EM by regulating ICI.