Circulating proteins and metabolites panel for noninvasive preoperative diagnosis of epithelial ovarian cancer.

Background: Existing biomarkers for epithelial ovarian cancer (EOC) have demonstrated limited sensitivity and specificity. This study aimed to investigate plasma protein and metabolite characteristics of EOC and identify novel biomarker candidates for noninvasive diagnosis and differential diagnosis.

Methods: In this prospective diagnostic cohort study, plasma was preoperatively collected from 536 consecutive patients presenting with imaging-suspected adnexal masses, uterine fibroids, or pelvic organ prolapse. After exclusions, the final cohort comprised 251 participants: EOC (n = 97), borderline ovarian tumors (n = 38), benign ovarian tumors (n = 54), and healthy controls (n = 62). Proteomic and metabolomic profiling was performed. A machine learning model was trained on a training cohort (34 EOC patients and 62 non-OC individuals [borderline, benign, and healthy controls]) to distinguish EOC from other groups. The model was validated in two independent cohorts: validation cohort 1 (n = 25) and validation cohort 2 (n = 130) using targeted proteomics and untargeted metabolomics. External transcriptomic datasets (TCGA-OV, GTEx bulk RNA-seq; GSE180661 scRNA-seq) were leveraged to validate TDO2 upregulation in ovarian cancer tissues, particularly in fibroblasts. This TDO2 upregulation were experimentally confirmed through quantitative PCR, immunohistochemistry, and immunofluorescence using clinical specimens.

Results: We identified significant protein alterations in EOC patients' plasma, implicating dysregulated metabolic and PI3K-Akt signaling pathways. Metabolite analysis further revealed aberrant sphingolipid metabolism, steroid hormone biosynthesis, and tryptophan metabolism in EOC patients' plasma. A diagnostic panel comprising 4 proteins (LRG1, ITIH3, PDIA4, and PON1) and 3 metabolites (kynurenine, indole, and 3-hydroxybutyrate) achieved an AUC of 0.975 (95% CI 0.943-0.997) with 95.2% sensitivity and 91.2% specificity in the training cohort. Critically, the model demonstrated robust generalizability in two independent validation cohorts: validation cohort 1 (AUC = 0.962, 95% CI 0.878-1.000) and validation cohort 2 (AUC = 0.965, 95% CI 0.921-0.995). Furthermore, fibroblasts with high expression of tryptophan 2,3-dioxygenase are contributing factors to elevated levels of kynurenine.

Conclusions: Our findings provide novel insights into the EOC metabolic and protein landscape. We developed and validated a plasma classifier demonstrating high sensitivity and specificity, which effectively distinguishes EOC patients from non-OC individuals. This classifier could enhance preoperative diagnostic accuracy and aid in differential diagnosis.