Palmitoylation-related genes play a critical role in tumor progression, yet their prognostic and therapeutic implications in ovarian cancer (OC) remain unclear. Leveraging machine learning, this study aims to develop a palmitoylation-related gene signature to predict OC outcomes and guide therapeutic strategies. Gene expression data from TCGA and GTEx were analyzed to identify differentially expressed genes palmitoylation-related genes. Using 101 machine learning algorithm combinations across multiple cohorts, we constructed a prognostic signature. The core gene ACSM3 was investigated for the functions and mechanisms in OC progression. A five-gene prognostic model (ACSM3, GRB7, DPM3, TRPV4, DNAJA1) stratified OC patients into high- and low-risk groups with significantly different overall survival. ACSM3 was downregulated in OC tissues and cells, and its low expression correlated with poor prognosis. ACSM3 overexpression suppressed proliferation, induced G1 arrest, promoted apoptosis, and inhibited tumor growth. Mechanistically, ACSM3 reduced lipid accumulation, while its knockdown upregulated ferroptosis-negative regulators. ACSM3 interacted with HSPD1, which may regulate ACSM3 at the post-translational level. HSPD1 knockdown attenuated the anti-proliferative and lipid-reducing effects of ACSM3. Our study established a robust palmitoylation-related prognostic signature for OC and identified ACSM3 as a tumor suppressor that inhibits OC progression by limiting lipid accumulation in coordination with HSPD1, providing a foundation for future therapeutic strategies.
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Ling et al. (2026) studied this question.
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