Abstract
INTRODUCTION: Aberrant fatty acid (FA) metabolism is increasingly recognized as a significant factor in ovarian cancer (OC) progression, although the comprehensive metabolic alterations across different body tissues remain unclear. METHODS: In this study, sixteen OC patients and twenty-nine non-cancer (NC) patients were recruited for metabolic profiling using a global and targeted metabolomic strategy based on a gas chromatography-hydrogen flame ionization detector (GC-FID). The patient survival was followed up to 3 years, and PFS was calculated. RESULTS: Our findings revealed distinct metabolite profiles that differentiate OC from NC groups across all sample types. We found seven, nine, and thirteen significant metabolites in subcutaneous fat, plasma, and ovarian tissue respectively. In particular, docosahexaenoic acid (DHA) and arachidonic acid (AA) levels were notably elevated in all sample types of OC patients. Furthermore, receiver operating characteristic (ROC) analysis highlight that three plasma FA showed the best specificity and sensitivity in differentiating the OC group from the NC group (Area Under The Curve, AUC > 0.89), including caprylic acid, myristoleic acid, and tetracosaenoic acid. Most of the significant FA in subcutaneous fat and ovarian tissue showed a high risk of OC. However, caprylic acid and tetracosanoic acid were identified as protective factors in the plasma sample. We also found that high levels of linoelaidic acid in subcutaneous fat and palmitelaidic acid in ovarian tissue were associated with poor prognosis. Pathway analysis indicated upregulation of fatty acid synthesis, inflammatory signaling, and ferroptosis pathways in OC patients. DISCUSSION: This study reveals a coordinated reprogramming of FA metabolism across multiple biospecimens in OC patients. Our results suggest that specific fatty acids may contribute to OC progression through dysregulation of fatty acid synthesis, inflammatory signaling, and ferroptosis. These findings offer mechanistic insights into OC progression and highlighting potential biomarkers and targeted therapeutic interventions.