Gene Expression Signature in Women with Endometriosis: An In Silico Approach

Endometriosis is a chronic and multifactorial disease that poses a growing public health challenge, affecting approximately 10315% of women of reproductive age. It is defined by the presence of endometrial-like tissue outside the uterine cavity and can manifest in three main phenotypes: superficial peritoneal endometriosis, ovarian endometrioma, and deep infiltrating endometriosis. Clinical presentation varies depending on the phenotype, ranging from asymptomatic cases to chronic pelvic pain and infertility, which are among the most prevalent and debilitating outcomes. Despite considerable research efforts, the molecular mechanisms underlying endometriosis remain incompletely understood, and a comprehensive genetic profile has yet to be fully established. The pathogenesis is thought to involve complex gene3environment interactions, with numerous genes contributing to disease susceptibility, symptom severity, and treatment response. This study aimed to identify a consensual gene expression signature associated with endometriosis. Using an in silico approach, we conducted a meta-analysis of seven publicly available transcriptomic datasets 4 five based on microarranjo platforms and two on bulk RNA sequencing 4 encompassing various phenotypic presentations of the disease. The analyzed subtypes included the eutopic endometrium of women with endometriosis during the early secretory phase (ESPEM), compared to healthy controls (ESPN), and ovarian endometriosis (ODEM), characterized by the presence of endometriomas. Ectopic lesions (EEM) were compared to normal eutopic endometrium (EN), and non-location-specific endometriosis cases (EM) were also included. Deep infiltrating endometriosis phenotypes comprised lesions affecting the bladder (DiEB) and intestine (DiEIn), both of which are associated with increased clinical severity. Additionally, peritoneal lesions were morphologically classified as red (PeLR), black (PeLB), or white (PeLW), reflecting different stages of disease progression 4 from active vascularized lesions to fibrotic, scarred tissue. The meta-analysis of microarranjo data identified 205 differentially expressed genes (DEGs) across nine distinct group comparisons. Subsequent integration with RNA-seq datasets validated 57 of these DEGs. Functional enrichment analyses were performed separately for upregulated and downregulated genes in the endometriosis group. The top five upregulated genes were CFH, GAS6, TSPAN4, WNT2B, and PLSCR4, while PIGN, ITGB8, PFAS, KLHL13, and PARP1 were among the most significantly downregulated. Upregulated genes were enriched in pathways related to complement activation, cell signaling, proliferation, inflammation, angiogenesis, and estrogen biosynthesis. Conversely, downregulated genes were associated with DNA repair mechanisms, regulation of the major histocompatibility complex, and immune evasion processes. These findings demonstrate that the integration of publicly available transcriptomic data enables the identification of a robust and consistent gene expression signature in endometriosis. This signature provides valuable insights into the molecular landscape of the disease and may contribute to the development of novel diagnostic biomarkers and therapeutic targets.