Study on the relationship between Ferroptosis gene and immune microenvironment in ovarian cancer and related prognostic model construction

Abstract

Ovarian cancer (OC) has a high recurrence rate and insufficient early screening methods, with a 5-year survival rate of less than 50% However, in many cases, simple standard treatment results are not satisfactory. Therefore, more effective treatments are being sought Ferroptosis is a recently discovered form of cell death associated with loss of glutathione peroxidase 4 (GPX 4) activity and accumulation of reactive lipid oxygen species (ROS), which shows great potential for anticancer therapy[1]. The results of immunotherapy for ovarian cancer are not ideal, but some studies have shown that the results of immunotherapy are significantly improved when combined with other treatments. Gene expression profiling allows researchers to explore and elucidate the molecular mechanisms underlying disease at the genomic and transcriptomic levels. integration of genomic technology and bioinformatics analysis Although these tools allow researchers to screen for tumor-associated genes and identify key prognostic factors, single-gene biomarkers that provide effective survival predictions are limited. Multigene-based risk models may provide better prognostic factors to predict patient survival[2]. identify multigene risk score characteristics by bioinformatic analysis and find more potential biomarkers for effective diagnosis and prognosis assessment of ovarian cancer patients. Based on TCGA and GEO transcript expression data and associated clinical data, a scoring system was developed using consensus cluster analysis, univariate Cox regression analysis, and lasso regression and validated against GEO database data. The results of univariate and multivariate risk analysis showed that the score can be used as an independent prognostic marker of ovarian cancer.