EXPLORING PROGNOSTIC TARGETS IN THE IMMUNE MICROENVIRONMENT OF ENDOMETRIOSIS BASED ON BIOINFORMATICS

Abstract

Background: It has been shown that immunity has an important influence on the development of endometriosis (EM), but its specific mechanism of action in EM remains to be investigated. We aim to find a set of new biomarkers associated with immunity in EM. Methods: In GSE7305, DEG screening was followed by GSVA to assess immune cell variation. WGCNA identified a key module based on immune cell scores. Overlapping key module and DEG genes yielded immune cell-related DEGs. A PPI network was created and hub genes were identified using the Cytohubba plugin. Hub gene diagnostic efficacy was assessed, and expression validation was completed in GSE25628. Additionally, drug prediction and qRT-PCR verified hub gene expressions. Results: From GSE7305, 1,238 DEGs were identified, with MEturquoise (13,523 genes) being the most relevant for immune cell scores. Subsequently, 1,197 immune cell-related DEGs were obtained by overlapping these sets. Following this, a PPI network yielded five hub genes (CDK1, CCNA2, ASPM, TOP2A, KIF11). Furthermore, CD56 bright NK cells showed significant negative correlation with CDK1, CCNA2, ASPM, and TOP2A. Memory B cells were positively correlated with CCNA2 and TOP2A. These hub genes were expressed at significantly lower levels in EM samples than controls (P < 0.0001), verified in GSE25628 and qRT-PCR. Additionally, hub genes exhibited good diagnostic efficacy (AUC ≥ 0.881). Conclusion: CDK1, CCNA2, ASPM, TOP2A, and KIF11 were identified as hub genes, which provided a new perspective to study the relationship between immunity and EM.