A novel prognostic model for ovarian cancer: construction, validation, and therapeutic insights

Ovarian cancer (OV) is the most lethal gynaecological malignancy worldwide. Palmitoylation, a reversible post-translational lipid modification, has been implicated in tumourigenesis, growth, metastasis and apoptosis across multiple cancers. However, its impact on immune infiltration, therapeutic response and clinical outcomes in ovarian cancer remains insufficiently explored.

We obtained transcriptome data and clinical information pertaining to ovarian cancer from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. A prognostic model based on palmitoylation-related genes was constructed using univariate Cox and Lasso-Cox regression for feature selection. The predictive performance of the model was assessed via Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curve evaluation.

We developed a five-gene prognostic prediction model utilizing palmitoylation-related genes derived from TCGA samples of epithelial ovarian cancer patients. The validity of this model was confirmed using patient samples from both TCGA and GEO datasets. KM analysis demonstrated that our prognostic model effectively distinguished between high-risk and low-risk groups, correlating with poorer or more favorable outcomes respectively. According to ROC curve analysis, our model exhibited superior predictive accuracy compared to traditional clinical factors alone. Additionally, analyses regarding immune cell infiltration, expression levels of immune checkpoints, as well as drug sensitivity further support potential treatment strategies for ovarian cancer.

The prognostic model developed in this study has the potential to enhance our understanding of the role of palmitic acid-related genes in ovarian cancer, providing new insights into prognosis prediction and treatment strategies for patients with ovarian cancer.