Abstract
Ferroptosis is an iron-dependent, regulated form of cell death, and the process is complex, consisting of a variety of metabolites and biological molecules. Ovarian cancer (OC) is a highly malignant gynecologic tumor with a poor survival rate. However, the predictive role of ferroptosis-related genes in ovarian cancer prognosis remains unknown. In this study, we demonstrated that the 57 ferroptosis-related genes were expressed differently between ovarian cancer and normal ovarian tissue, and based on these genes, all OC cases can be well divided into 2 subgroups by applying consensus clustering. We utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to develop a multigene risk signature from the TCGA cohort and then validated it in an OC cohort from the GEO database. A 5-gene signature was built and reveals a favorable predictive efficacy in both TCGA and GEO cohort (P < 0.001 and P = 0.03). The GO and KEGG analysis revealed that the differentially expressed genes (DEGs) between the low- and high-risk subgroup divided by our risk model were associated with tumor immunity, and lower immune status in the high-risk group was discovered. In conclusion, ferroptosis-related genes are vital factors predicting the prognosis of OC and could be a novel potential treatment target.
Highlights
Ovarian cancer (OC) is the most lethal malignancy among gynecological tumors and causes ∼150,000 women to death every year (Lheureux et al, 2019)
Our studies presented a 5-ferroptosisrelated signature (ALOX12, ACACA, SLC7A11, FTH1, and CD44) and found it could well predict the prognosis of OC patients
Our study indicated that ferroptosis is correlated to the development and the progress of OC, since most of the ferroptosis-related genes were expressed differently between normal and OC tissues
Summary
Ovarian cancer (OC) is the most lethal malignancy among gynecological tumors and causes ∼150,000 women to death every year (Lheureux et al, 2019). The therapeutic drugs against OC were quickly progressed in the past 20 years, the overall survival (OS) was still poorly increased in most countries (Lee et al, 2018). As the current treatment measures are not promising, identifying reliable prognostic biomarkers is important to prolong the survival time of OC patients. CA-125 and Human epididymis protein 4 (HE4) were the most commonly used predictive markers. As the molecular mechanisms affecting the prognosis of ovarian cancer are complex, single gene/factor prediction models are often with low accuracy. Multiple-gene-based models often showed better efficacies in predicting the prognosis of various tumors
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