Background: Distinguishing ovarian cancer (OC) from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of OC pathogenesis. Accumulating evidence suggests a link between OC and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved OC diagnosis and prevention.
Methods: Here, we profiled the serum lipidome of 208 OC, including 93 patients with early-stage OC, and 117 non-OC (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning approaches.
Results: We found lipidome alterations unique to OC were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in OC in comparison to other gynecological diseases. Machine learning methods selected a panel of 17 lipids that discriminated OC from non-OC cases with an AUC of 0.85 for an independent test set.
Conclusions: This study provides a systemic analysis of lipidome alterations in human OC, specifically in Korean women.
Impact: Here, we show the potential of circulating lipids in distinguishing OC from non-OC conditions.