Spatial lipidomics in gynecologic malignancies: molecular identification, cellular attribution, and mechanistic validation

Spatial lipidomics can place lipid identities within histopathologic compartments, but ion maps are frequently interpreted beyond what their analytical and biological validation can support. Here, we critically synthesize direct spatial lipid evidence in ovarian, endometrial, and cervical cancers and use spatial metabolomics, mass spectrometry imaging, spatial transcriptomics or proteomics, and non-spatial lipid biology only as explicitly graded support. We organize inference as an eight-gate chain: pathology-defined sampling, analytical quality control, structural identification, cellular attribution, metabolic flux, perturbation and rescue, patient-level validation, and prospective clinical utility. Within this framework, lipid uptake, de novo synthesis, desaturation, fatty-acid oxidation, membrane remodeling, prostaglandin E2 signaling, and ferroptosis are interpreted as mechanistic programs downstream of spatially anchored observations. Disease ecology determines which programs are plausible: omental adipocyte-tumor exchange dominates ovarian cancer, obesity-estrogen-insulin and PI3K signaling frame endometrial cancer, and persistent high-risk human papillomavirus infection shapes membrane and inflammatory remodeling in cervical cancer. Evidence remains highly asymmetric. Ovarian cancer has the broadest spatial literature; endometrial evidence relies principally on one 64-patient DESI-MSI cohort; and cervical evidence is limited to very small phospholipid and oleate studies. Spatial lipidomics is therefore ready to localize disease-specific niches, prioritize causal experiments, and define testable patient subsets, but not yet to support routine screening, diagnosis, treatment selection, or surveillance. Closing the gap requires orthogonal structural confirmation, cell-resolved source assignment, isotope tracing, functional rescue, and locked multicenter validation.