Multi-omics profiling identifies an immunometabolic signature associated with endometriosis

Integrative serum proteomic and metabolomic profiling in ovarian endometrioma: an exploratory multi-omics study

It is suggested that OEM is associated with concurrent immune-related and metabolic alterations detectable in the circulation and provides limited independent support for a platelet-associated circulating signal, with PPBP showing the most consistent validation.

Discovery of novel protein biomarkers for the detection of polyendocrine metabolic ovarian syndrome phenotype A

Polyendocrine metabolic ovarian syndrome (PMOS) is a prevalent endocrine condition affecting women of reproductive age. PMOS is underrecognized/underdiagnosed and associated with secondary comorbidities that may result from delayed diagnosis/management. Novel biomarkers are needed to improve timely diagnosis. This retrospective, case-control study used biobanked serum samples from women with PMOS phenotype A ( n  = 51) and healthy controls ( n  = 37). We performed protein biomarker discovery using Olink Proximity Extension Assay and receiver operating characteristic-area under the curve (ROC-AUC) analysis. We conducted an overrepresentation pathway analysis against Reactome. We investigated 1,196 protein biomarkers and conducted a pathway analysis using the 145 top-performing biomarkers (AUC > 0.750). Top 10 performing biomarkers (AUC ≥ 0.961) were linked to relevant biological pathways, including immune system regulation (serum amyloid A4 and leukotriene A4 hydrolase), lipid, carbohydrate, and protein metabolism (fibroblast growth factor binding protein 1, carboxylesterase 2, inositol polyphosphate-1-phosphatase, inositol polyphosphate-1-phosphatase like, pro-glucagon, and leptin), nervous system development (semaphorin 4C and plexin B1), and cellular response (peroxiredoxin 1). These pathways/sub-pathways may contribute to PMOS pathophysiology and related comorbidities (insulin resistance, obesity, and fatty liver disease). We discovered novel circulating protein biomarkers that distinguish women with PMOS phenotype A from healthy controls, supporting diagnosis and enabling timely intervention/appropriate management.

Metabolomic signatures support the diagnostics of peritoneal endometriosis using generalised linear models

EGF as a non-invasive biomarker of endometriosis: a case control study

Endometriosis is a chronic inflammatory condition that affects an estimated 1 in 10 women but is often mis- and underdiagnosed due to its non-specific symptoms and the lack of a non-invasive diagnostic test. This study aimed to identify and validate a potential non-invasive biomarker for endometriosis. This study applied quantitative proteomics discovery approaches to identify and validate non-invasive biomarkers of endometriosis with a long-term goal of leveraging them for purposes of diagnosis and therapeutic monitoring. Isobaric tags for relative and absolute quantification (iTRAQ) combined with mass spectrometry were used to identify and quantitate proteins and peptides in urine samples from participants with surgically-confirmed endometriosis (n = 73) and 1:1 age-matched participants never diagnosed with endometriosis (n = 73). Among those aged 25–47 at urine collection, Epidermal Growth Factor (EGF) (validated using monospecific enzyme-linked immunosorbent assays (ELISA)) was present at significantly lower levels in the urine of participants with surgically-confirmed endometriosis compared to controls (P = 0.02) and had an excellent negative predictive value (NPV) of 94.3% with a cutoff of > 800 pg/ug as determined by Bayes’ formula. Urinary EGF levels were also significantly lower in the urine of participants aged 25–47 with endometriosis who experience acyclic pelvic pain compared to samples from age-matched controls who did not report acyclic pelvic pain (P = 0.007) such that the presence of acyclic pelvic pain increased the NPV of EGF to 96.8% as determined by Bayes’ formula. These data demonstrate that urinary EGF has potential as a novel, non-invasive, accurate and objective biomarker for endometriosis diagnosis and prognosis of this disease.

Multi-omics Investigations of Immune Microenvironment of Human Colorectal Cancer

Lipidomic Profiling Reveals Distinct Molecular Signatures Across Clinical Subtypes of Myasthenia Gravis

Background/Objectives: Myasthenia gravis (MG) is an immune-mediated neuromuscular disorder for which antibody-based assays have limited sensitivity, particularly in double-seronegative MG (dsNMG), highlighting the need for complementary biomarkers. Given their roles in immune regulation, membrane integrity, and metabolic stress responses, lipids represent promising candidates for biomarker discovery. Methods: We designed a prospective case–control study and systematically stratified 68 patients with myasthenia gravis (MG) according to clinical classification and autoantibody status. Using LC–MS/MS, we quantified 824 lipids in 136 serum samples collected from these patients and 68 healthy controls. The analyzed subtypes included ocular MG (OMG), generalized MG (GMG), acetylcholine receptor antibody-positive MG (AChR-MG), and dsNMG. Differential lipid analysis, correlation network construction, KEGG pathway enrichment, and multivariable logistic regression were performed. Diagnostic and subtype prediction models were developed using LASSO with 10 × 10 repeated cross-validation and interpreted using Shapley Additive exPlanations (SHAP) analysis. A longitudinal follow-up analysis was conducted to assess dynamic associations between lipid signatures and disease activity. Results: In total, 240 lipids were significantly altered in MG compared with controls. Lipids distinguishing GMG from OMG were enriched in ether lipid metabolism, necroptosis, and sphingolipid signaling pathways. AChR-MG and dsNMG shared lipid networks related to membrane remodeling and signaling regulation, whereas dsNMG exhibited marked elevations in acylcarnitines and bile acid-related metabolites, potentially reflecting a distinct phenotype characterized by altered energy metabolism. The lipid-based model achieved an AUC of 0.917 for distinguishing MG from controls, and AUCs of 0.77 and 0.71 for differentiating AChR-MG from dsNMG and GMG from OMG, respectively. Longitudinal analyses showed that SM(d18:1/23:0) and Cer(d24:1/18:0(2OH)) displayed dynamic changes consistent with disease activity. Conclusions: Serum lipidomics revealed subtype-specific metabolic features of MG, with stable disease-associated remodeling and dynamic sphingolipid changes potentially reflecting disease activity. By integrating systematic clinical and antibody-based subtype stratification with longitudinal follow-up, this study supports lipidomics as a complementary tool for precision diagnosis and disease stratification, particularly in antibody-negative dsNMG.