Identification of a ferroptosis- and oxidative stress-associated gene signature for prognostic stratification of ovarian cancer

Abstract

Background: Studies have shown that ferroptosis- and oxidative stress-related genes (FORGs) perform crosstalk in ovarian cancer (OC). The specific role of FORGs in OC, however, remains unclear. We aimed to develop a molecular subtype and prognostic model associated with FORGs that could predict OC prognosis and evaluate the infiltration of tumor-associated immune cells.

Methods: Gene expression samples were collected from the GEO (GSE53963) and Cancer Genome Atlas (TCGA) databases. Kaplan-Meier analysis was used to evaluate prognostic efficacy. Unsupervised clustering was applied to identify molecular subtypes, which was followed by tumor immune cell infiltration and functional enrichment analyses. Subtype-related differentially expressed genes (DEGs) were identified and used to establish prognostic models. Associations between the model and immune checkpoint expression, stromal scores, and chemotherapy were investigated.

Results: OC patients were categorized into two FORG subtypes based on the expression characteristics of 19 FORGs. Molecular subtypes associated with patient prognosis, immune activity, and energy metabolism pathways were identified. Subsequently, DEGs in the two FORG subtypes were identified and used in prognostic models. We identified six signature genes (MEGF8, ECE1, SASH1, ARHGEF16, PLXNA1, and FCGBP) with LASSO analysis to assess the risk of OC. Patients in the high-risk group had poor prognoses and immunosuppression, while the risk scores were significantly associated with immune checkpoint expression, stromal scores, and chemotherapy sensitivity.

Conclusions: Our novel clustering algorithm was used to create distinct clusters of OC patients and a prognostic model was developed that accurately predicted patient outcomes and chemotherapy responses. This approach offers effective precision medicine for OC patients.

Keywords: Ferroptosis; ovarian cancer; oxidative stress; prognosis.

Figure 1

Genetic alterations and prognostic role of ferroptosis- and oxidative stress-related genes (FORGs) in ovarian cancer (OC). A, B. Kaplan-Meier analysis revealed that SNCA and IFNG were closely related to the prognosis of OC. C. Frequencies of copy number variation (CNV) increases and deletions and non-CNV among the FORGs. D. Locations of FORG CNV alterations on 23 chromosomes. E. Mutation frequencies of FORGs. F. Interactions among FORGs in OC. The lines represent interactions.

Figure 2

Identification of ferroptosis- and oxidative stress-related genes (FORGs) subtypes. A. Two FORG clusters were identified using consensus clustering analyses. B. Kaplan-Meier analysis indicated that the prognosis of cluster A was significantly better than that of cluster B. C. The expression pattern of FORGs and clinicopathologic factors in the two FORG subtypes are shown in the heatmap.

Figure 3

Functional analyses and tumor microenvironment in ferroptosis- and oxidative stress-related genes (FORGs) clusters. A. GSVA analysis showed the enriched pathways in two FORG clusters. B. GO and KEGG analyses. C. ssGSEA analysis was performed to assess the immune infiltration scores of the FORG subtypes.

Figure 4

Construction of the ferroptosis- and oxidative stress-related genes (FORGs) prognostic signature. A. Cross-validation for tuning parameter selection in the LASSO model. B. Kaplan-Meier survival analysis demonstrated that low-risk patients had a better prognosis than high-risk patients. C. Correlation between the immune cells, prognostic signature genes, and risk scores.

Figure 5

Evaluation of immune cell infiltration in high- and low-risk groups. A, C, E. Resting CD4 memory T cells, resting natural killer (NK) cells, and M2 macrophages were positively correlated with risk scores. B, D, F. Activated CD4 memory T cells, activated NK cells, and M1 macrophages were negatively correlated with risk scores.

Figure 6

Comparison analysis of immune checkpoint expression, stromal scores, and chemotherapy effects between the two groups. A, B. The immune checkpoint gene expressions were different in the high-risk and low-risk groups. C. Stromal scores and risk groups. D. IC₅₀ values of cisplatin were significantly increased in the high-risk group. E. IC₅₀ values of pazopanib were decreased in the high-risk group.