Introduction:
Ovarian Cancer (OC) is a heterogeneous malignancy with poor outcomes. Oxidative stress plays a crucial role in developing drug resistance. However, the relationships between Oxidative Stress-related Genes (OSRGs) and the prognosis of platinum-resistant
OC remain unclear. This study aimed to develop an OSRGs-based prognostic risk model for platinum-resistant OC patients.
Methods:
Gene Set Enrichment Analysis (GSEA) was performed to determine the expression difference of OSRGs between platinum-resistant and -sensitive OC patients. Cox regression analyses
were used to identify the prognostic OSRGs and establish a risk score model. The model was validated by using an external dataset. Machine learning was used to determine the prognostic OSRGs
associated with platinum resistance. Finally, the biological functions of selected OSRG were determined via in vitro cellular experiments.
Results:
Three gene sets associated with oxidative stress-related pathways were enriched (p <
0.05), and 105 OSRGs were found to be differentially expressed between platinum-resistant and -
sensitive OC (p < 0.05). Twenty prognosis-associated OSRGs were identified (HR: 0:562-5.437;
95% CI: 0.319-20.148; p < 0.005), and seven independent OSRGs were used to construct a prognostic risk score model, which accurately predicted the survival of OC patients (1-, 3-, and 5-year
AUC=0.69, 0.75, and 0.67, respectively). The prognostic potential of this model was confirmed in
the validation cohort. Machine learning showed five prognostic OSRGs (SPHK1, PXDNL,
C1QA, WRN, and SETX) to be strongly correlated with platinum resistance in OC patients. Cellular experiments showed that WRN significantly promoted the malignancy and platinum resistance
of OC cells.
Conclusion:
The OSRGs-based risk score model can efficiently predict the prognosis and platinum resistance of OC patients. This model may improve the risk stratification of OC patients in
the clinic.
