Identification of novel molecular subtypes in ovarian cancer

Ovarian cancer (OV) remains a leading cause of gynecologic cancer mortality, this is primarily attributed to the absence of early symptoms and reliable diagnostic biomarkers. Recent studies suggest that zinc dysregulation reshapes the tumor microenvironment, impairs immune surveillance, and promotes tumor progression. However, the prognostic implications of zinc homeostasis-related genes in OV remain poorly understood. Patients with OV were stratified into molecular subtypes based on the expression profiles of prognostic zinc homeostasis-related genes. Differential gene expression analysis was conducted using the limma package. Subsequently, we constructed a zinc homeostasis-based risk score model employing univariate Cox regression, least absolute shrinkage and selection operator regression, and multivariate Cox regression analyses. The prognostic model was validated using external datasets. Additionally, immune cell infiltration and drug sensitivity analyses were conducted to evaluate the clinical relevance of the model. Two molecular subtypes of OV were identified, each associated with distinct biological pathways. A prognostic model comprising four zinc homeostasis-related genes was developed, demonstrating robust predictive capability for overall survival and significant correlation with immune cell infiltration patterns. Drug sensitivity analysis revealed potential therapeutic targets and candidate drugs, offering insights for OV treatment strategies. This study identifies novel OV subtypes driven by zinc homeostasisrelated genes, providing insights into the genetic heterogeneity, immune landscape, and therapeutic strategies of OV. The developed prognostic model and identified candidate therapeutic agents offer valuable references for personalized treatment approaches in OV.