Identification and Validation of Prognostic Markers for Endometriosis-Associated Ovarian Cancer

Background: Growing evidence suggests that endometriosis (EMs) is a risk factor for endometriosis-associated ovarian cancer (EAOC). The aim was to identify and validate gene signatures associated with EMs that may serve as potential biomarkers for evaluating the prognosis of patients with EAOC. Methods: The data of EMs and control samples was obtained from GEO database. The weighted gene co-expression network analysis (WGCNA) identified modular genes significantly associated with EMs. The KEGG pathway and GO functional enrichment analyses were also performed. Univariate Cox regression analysis was conducted to screen marker genes associated with the prognosis of EAOC patients. Finally, RT-qPCR and immunohistochemical verified the expression of ADAMTS19 and TUBB in normal ovarian and EAOC tissues, and the biological functions of ADAMTS19 and TUBB were preliminarily explored by CCK8 and Transwell assays. Results: The WGCNA identified 2 co-expression modules, which in total included 615 genes, and 7642 differentially expressed genes (DEGs) were detected thorough analysis of the EAOC dataset. After taking the intersection of 615 modular genes and 7642 DEGs, 214 shared genes were obtained, and univariate COX regression analysis pointed 10 genes associated with the prognosis of EAOC. Moreover, it was demonstrated by RT-qPCR and immunohistochemical staining experiments that ADAMTS19 expression was elevated, while TUBB expression was reduced in EAOC compared with normal ovarian cells and tissues. Finally, cell experiments revealed that ADAMTS19 promoted the proliferation and invasion in EAOC cells, while overexpression of TUBB inhibited these processes. Conclusions: The present study identified and validated new EMs-associated gene markers, which could serve as potential biomarkers for assessing the prognostic risk of EAOC patients. In addition, some of these genes may have significance as novel therapeutic targets and could be used to guide clinical applications.