Multi-omics profiling identifies an immunometabolic signature associated with endometriosis
Wenwei Pan1,2, Guizhen Lyu3, Yikang Wang1,2
1Department of Gynecology, Dongguan Hospital Affiliated To Shenzhen University, Dongguan, Guangdong, China.
Background:
Endometriosis (EM) is a chronic inflammatory gynaecological disorder affecting approximately 10% of women of reproductive age. Early diagnosis remains challenging because definitive diagnosis still relies on invasive surgical confirmation. This study aimed to characterize systemic immune-metabolic alterations in EM and identify candidate peripheral blood biomarkers through integrated multi-omics profiling.
Methods:
Peripheral blood samples were collected from 88 participants, including 44 patients with EM T, 22 patients with benign ovarian cysts, and 22 healthy controls. Inflammatory proteomic profiling was performed using the Olink Target 96 Inflammation panel, and untargeted metabolomic analysis was conducted using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). Differential expression, pathway enrichment, integrative network analysis, and receiver operating characteristic (ROC) analyses with bootstrap resampling and false discovery rate (FDR) correction were performed.
Results:
Proteomic analysis revealed significant dysregulation of immune-inflammatory pathways in EM, characterized by enhanced chemokine signaling, cytokine-receptor interactions, and apoptotic activation. Metabolomic profiling identified substantial disturbances in energy metabolism, particularly involving fatty acid β-oxidation, acylcarnitine transport, and amino acid-carbon metabolic reprogramming. Integrated network analysis revealed a candidate immune-metabolic network comprising FGF21, CCL23, CDCP1, and CD8A. Correlation analysis demonstrated that FGF21 was positively associated with lipid oxidation-related metabolites, whereas CCL23 correlated with acylcarnitine species, indicating coordinated immune-metabolic interactions. ROC analyses showed that selected metabolite markers demonstrated moderate discriminatory performance between EM and healthy controls, while combined proteomic-metabolomic models achieved superior diagnostic performance compared with individual markers.
Conclusion:
Multi-omics profiling revealed coordinated immunometabolic alterations associated with EM, characterized by interconnected inflammatory and metabolic alterations in peripheral blood. These findings provide evidence that systemic immune-metabolic dysregulation is associated with EM and support further evaluation of blood-based biomarker strategies for non-invasive detection. However, these findings are based on a single-center cohort and require validation in independent populations and targeted assays.