Gene signature links lipid metabolism to ovarian cancer outcomes

A machine-learning analysis of ovarian cancer patients has produced a five-gene prognostic signature that sorts patients into risk groups with significantly different overall survival, according to a study published in Cellular Oncology. The signature combines ACSM3, GRB7, DPM3, TRPV4 and DNAJA1, and the study describes it as a palmitoylation-related prognostic signature. Palmitoylation-related genes have been implicated in tumor progression, but their prognostic and therapeutic value in ovarian cancer had remained unclear, which motivated the research team to build a predictive model from this gene class.

To identify which palmitoylation-related genes mattered most, the researchers analyzed gene expression data from TCGA and GTEx to find differentially expressed genes within this category. They then screened 101 machine learning algorithm combinations across multiple cohorts to construct the prognostic signature. The resulting five-gene model stratified ovarian cancer patients into high- and low-risk groups with significantly different overall survival, offering a data-driven tool for outcome prediction. Among the five genes, ACSM3 emerged as the core gene, and the team went on to investigate its function and mechanism in ovarian cancer progression in detail.

The study found that ACSM3 was downregulated in ovarian cancer tissues and cells compared with normal samples. The study found that ACSM3 was downregulated in ovarian cancer tissues and cells, and that low expression of the gene correlated with poor prognosis. In other words, patients whose tumors expressed less ACSM3 tended to have poorer prognosis.

The researchers then investigated the function and mechanism of ACSM3 in ovarian cancer progression. When the researchers forced ACSM3 overexpression in ovarian cancer cells, the cells showed suppressed proliferation, arrest in the G1 phase of the cell cycle, and increased apoptosis, the programmed cell death pathway that cancer cells often evade. In animal models, ACSM3 overexpression also inhibited tumor growth. Together these findings show that ACSM3 overexpression suppressed proliferation, induced G1 arrest, promoted apoptosis and inhibited tumor growth in the studied models.

The mechanism behind these effects appears to involve lipid metabolism. ACSM3 reduced lipid accumulation in the cancer cells, and knockdown of ACSM3 upregulated ferroptosis-negative regulators. The knockdown also upregulated ferroptosis-negative regulators, proteins that suppress ferroptosis, a form of cell death driven by lipid peroxidation.

The study also uncovered a protein interaction that may control ACSM3 itself. ACSM3 interacted with HSPD1, a molecular chaperone protein, and the authors propose that HSPD1 may regulate ACSM3 at the post-translational level, meaning after the ACSM3 protein has been made. When the team knocked down HSPD1, the anti-proliferative and lipid-reducing effects of ACSM3 were attenuated. This places HSPD1 knockdown as attenuating the anti-proliferative and lipid-reducing effects of ACSM3 observed in the study.

Because ACSM3 was identified among differentially expressed palmitoylation-related genes, its interaction with HSPD1 and its influence on lipid metabolism connect to the palmitoylation-related focus of the study. The authors describe the five-gene model as a robust palmitoylation-related prognostic signature for ovarian cancer, constructed using 101 machine learning algorithm combinations across multiple cohorts.

The researchers conclude that their work establishes the prognostic signature and identifies ACSM3 as a tumor suppressor that inhibits ovarian cancer progression by limiting lipid accumulation in coordination with HSPD1. They present this as a foundation for future therapeutic strategies, though the study is at the preclinical stage and the mechanisms described are based on cell and animal experiments alongside patient data analysis. The authors present the work as a foundation for future therapeutic strategies, while noting that the study is at a preclinical stage.

Subject of Research: A palmitoylation-related gene signature and the tumor suppressor ACSM3 in ovarian cancer progression and lipid metabolism

Article Title: Palmitoylation-related ACSM3 suppresses ovarian cancer progression via interacting with HSPD1 and regulating lipid metabolism

Article References: Ling, Q., Xu, H., Chen, Y., Yang, S., Tan, H., Zhang, Z., Sun, T., Kang, J., Lai, H., Chen, J., Wu, L., & Yang, G. (2026). Palmitoylation-related ACSM3 suppresses ovarian cancer progression via interacting with HSPD1 and regulating lipid metabolism. Cellular Oncology. https://doi.org/10.1007/s13402-026-01318-y

Image Credits: AI Generated

DOI: 10.1007/s13402-026-01318-y

Keywords: ovarian cancer, ACSM3, HSPD1, palmitoylation, lipid metabolism, prognostic signature, machine learning, ferroptosis, tumor suppressor, TCGA, GTEx, Cellular Oncology