Identification and validation of risk score model based on gene set activity as a diagnostic biomarker for endometriosis

• Published in: Heliyon Vol. 9; no. 7; p. e18277
• Main Authors: Zhang, Yi, Wu, Lulu, Wen, Xiang, Lv, Xiuwei
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2023; Elsevier
• Subjects: Diagnostic biomarker; Endometriosis; Gene set; Gene set variation analysis; Gene set variation analysis; Diagnostic biomarker; Endometriosis; Gene set
• DOI: 10.1016/j.heliyon.2023.e18277
• ISSN: 2405-8440

The enigmatic nature of Endometriosis (EMS) pathogenesis necessitates investigating alterations in signaling pathway activity to enhance our comprehension of the disease's characteristics. Three published gene expression profiles (GSE11691, GSE25628, and GSE7305 datasets) were downloaded, and the “combat” algorithm was employed for batch correction, gene expression difference analysis, and pathway enrichment difference analysis. The protein-protein interaction (PPI) network was constructed to identify core genes, and the relative enrichment degree of gene sets was evaluated. The Lasso regression model identified candidate gene sets with diagnostic value, and a risk scoring diagnostic model was constructed for further validation on the GSE86534 and GSE5108 datasets. CIBERSORT was used to assess the composition of immune cells in EMS, and the correlation between EMS diagnostic value gene sets and immune cells was evaluated. A total of 568 differentially expressed genes were identified between eutopic and ectopic endometrium, with 10 core genes in the PPI network associated with cell cycle regulation. Inflammation-related pathways, including cytokine-receptor signaling and chemokine signaling pathways, were significantly more active in ectopic endometrium compared to eutopic endometrium. Diagnostic gene sets for EMS, such as homologous recombination, base excision repair, DNA replication, P53 signaling pathway, adherens junction, and SNARE interactions in vesicular transport, were identified. The risk score's area under the curve (AUC) was 0.854, as indicated by the receiver operating characteristic (ROC) curve, and the risk score's diagnostic value was validated by the validation cohort. Immune cell infiltration analysis revealed correlations between the risk score and Macrophages M2, Plasma cells, resting NK cells, activated NK cells, and regulatory T cells. The risk scoring diagnostic model, based on pathway activity, demonstrates high diagnostic value and offers novel insights and strategies for the clinical diagnosis and treatment of Endometriosis.