Abstract
Background Ovarian cancer (OC) is the eighth most common cause of cancer death and the second cause of gynecologic cancer death in women around the world. Ferroptosis, an iron-dependent regulated cell death, plays a vital role in the development of many cancers. Applying expression of ferroptosis-related gene to forecast the cancer progression is helpful for cancer treatment. However, the relationship between ferroptosis-related genes and OC patient prognosis is still vastly unknown, making it still a challenge for developing ferroptosis therapy for OC. Methods The Cancer Genome Atlas (TCGA) data of OC were obtained and the datasets were randomly divided into training and test datasets. A novel ferroptosis-related gene signature associated with overall survival (OS) was constructed according to the training cohort. The test dataset and ICGC dataset were used to validate this signature. Results We constructed a model containing nine ferroptosis-related genes, namely, LPCAT3, ACSL3, CRYAB, PTGS2, ALOX12, HSBP1, SLC1A5, SLC7A11, and ZEB1, and predicted the OS of OC in TCGA. At a suitable cutoff, patients were divided into low risk and high risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics (ROCs) were as high as 0.664, respectively. Then, the test dataset and the ICGC dataset were used to evaluate our model, and the ROCs of test dataset were 0.667 and 0.777, respectively. In addition, functional analysis and correlation analysis showed that immune-related pathways were significantly enriched. Meanwhile, we also integrated with other clinical factors and we found the synthesized clinical factors and ferroptosis-related gene signature improved prognostic accuracy relative to the ferroptosis-related gene signature alone. Conclusion The ferroptosis-related gene signature could predict the OS of OC patients and improve therapeutic decision-making.
Highlights
Ovarian cancer (OC) is the eighth most common cause of cancer death and the second cause of gynecology cancer death in women around the world [1]
All datasets used in this study were publicly available and the workflow of this work is shown in Figure 1. e count data of OC were obtained from e Cancer Genome Atlas (TCGA)
We first transformed the Ensembl IDs to gene symbols and protein-coding gene was selected for this research. en, we computed the transcripts per kilobase million (TPM) values, which were more comparable between samples
Summary
Ovarian cancer (OC) is the eighth most common cause of cancer death and the second cause of gynecology cancer death in women around the world [1]. 70% of OCs are diagnosed at an advanced stage and have a relatively low 5-year survival rate of 30% [7]. Uncertain etiologic factors and low survival rate of OC make the finding of novel therapeutic strategies and models urgent. The relationship between ferroptosis-related genes and OC patient prognosis is still vastly unknown, making it still a challenge for developing ferroptosis therapy for OC. A novel ferroptosis-related gene signature associated with overall survival (OS) was constructed according to the training cohort. We constructed a model containing nine ferroptosis-related genes, namely, LPCAT3, ACSL3, CRYAB, PTGS2, ALOX12, HSBP1, SLC1A5, SLC7A11, and ZEB1, and predicted the OS of OC in TCGA. E ferroptosis-related gene signature could predict the OS of OC patients and improve therapeutic decision-making Conclusion. e ferroptosis-related gene signature could predict the OS of OC patients and improve therapeutic decision-making
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