Ovarian cancer (OC) is
one of the deadliest cancers affecting the female reproductive system.
It may present little or no symptoms at the early stages and typically
unspecific symptoms at later stages. High-grade serous ovarian cancer
(HGSC) is the subtype responsible for most ovarian cancer deaths.
However, very little is known about the metabolic course of this disease,
particularly in its early stages. In this longitudinal study, we examined
the temporal course of serum lipidome changes using a robust HGSC
mouse model and machine learning data analysis. Early progression
of HGSC was marked by increased levels of phosphatidylcholines and
phosphatidylethanolamines. In contrast, later stages featured more
diverse lipid alterations, including fatty acids and their derivatives,
triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines,
and phosphatidylinositols. These alterations underscored unique perturbations
in cell membrane stability, proliferation, and survival during cancer
development and progression, offering potential targets for early
detection and prognosis of human ovarian cancer.