Molecular characteristics of early- and late-onset ovarian cancer: insights from multidimensional evidence

Ovarian cancer (OC) is among the most lethal gynecologic malignancies, characterized by poor prognosis. While aging is a well-established risk factor, the underlying mechanisms distinguishing early- and late-onset ovarian cancer remain poorly understood.

This study analyzed the global burden and age-related trends of ovarian cancer using the GBD database. A cut-off age of 55 years was used to differentiate between early and late onset ovarian cancer, and a Mendelian randomization method was also used to investigate the causal relationship between aging and ovarian cancer. Machine learning was applied to identify tumor-specific age-associated genes, followed by bioinformatics analyses and single-cell sequencing to explore the roles of these genes and immune profile alterations in ovarian cancer. Additionally, models were constructed, and drug sensitivity analyses performed to evaluate their potential as diagnostic markers or therapeutic targets.

Ovarian cancer incidence and mortality exhibit age-related trends, with telomere length positively associated with increased risk (OR = 1.27, 95% CI: 1.01-1.60, P = 3.90 × 10⁻²). Older patients with OC have a worse prognosis. PRKCD and UCP2 were significantly upregulated in ovarian cancer. PRKCD facilitates epithelial-mesenchymal transition (EMT), contributing to ovarian cancer progression, while UCP2 modulates ROS dynamics, influencing chemoresistance. Immune microenvironment analysis revealed differences between high- and low-expression groups, particularly in T cells, macrophages, and other immune cells. Both genes are sensitive to a varity of drugs, including dasatinib, fluvastatin, highlighting their potential as therapeutic targets.

Aging is a significant risk factor for ovarian cancer, with PRKCD and UCP2 closely linked to its onset and progression. These genes show promise as novel biomarkers and therapeutic targets for ovarian cancer.