Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Authors

Olatomiwa O Bifarin
Samyukta Sah
David A Gaul
Samuel G Moore
Ruihong Chen
Murugesan Palaniappan
Jaeyeon Kim
Martin M Matzuk
Facundo M Fernández

Publication Date

6-2-2023

Journal

Journal of Proteome Research

DOI

10.1021/acs.jproteome.3c00226

PMID

37220064

PMCID

PMC10243112

PubMedCentral® Posted Date

5-23-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Mice, Animals, Female, Humans, Lipidomics, Longitudinal Studies, Ovarian Neoplasms, Sphingomyelins, Cystadenocarcinoma, Serous, metabolomics, lipidomics, mass spectrometry, bioinformatics, machine learning

Abstract

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.