Abstract
BACKGROUND: Ovarian cancer is a type of gynecological cancer with extremely high fatality rate. Ferroptosis, an iron-dependent regulated cell death, inhibits the immune infiltration of tumor cells. Therefore, it is worthwhile to explore the effects of ferroptosis-related gene signatures and immune infiltration patterns on the clinical prognosis of ovarian cancer. METHODS: In this study, we used the mRNA expression matrix and related medical information of those who suffer from ovarian cancer in the TCGA database. After that, we established a ferroptosis-related gene signature based on LASSO Cox regression model, and employed several specific enrichment analyses to explore the bioinformatics functions of differentially expressed genes (DEGs). Additionally, we analyzed the link between ferroptosis and immune cells by single-sample gene set enrichment analysis (ssGSEA) to create a heatmap of gene-immune cell correlation. We then examined the expression of immune checkpoints and verified the gene expression in ovarian cancer tissues by qPCR assays. Finally, we induced ferroptosis in ovarian cancer cells using drugs and analyzed their migration, invasion and gene expression. RESULTS: According to LASSO Cox regression analysis, 9 prognostic DEGs were in association with overall survival (OS), which was utilized to construct a 9-gene signature for patients. Patients were divided into two groups, in which high-risk group's OS was markedly shorter than that of low-risk group (Log-rank p<0.001). KEGG enrichment analysis showed that these DEGs were linked to human cytomegalovirus (HCMV) infection. The ssGSEA analysis revealed significant differences in immune cell type and expression between ALOX12 and GLRX5 groups (p<0.05). Heatmap showed high correlation of prognostic genes with various immune cells. qPCR assay confirmed the 9 gene expression signature in ovarian cancer tissues. The ovarian cancer cell invasion and migration were significantly inhibited after induction of ferroptosis. CONCLUSION: We decoded the ferroptosis-related gene signatures and immune infiltration patterns that can be used to predict the prognosis of ovarian cancer patients.