Identification of a ferroptosis-related gene signature (FRGS) for predicting clinical outcome in lung adenocarcinoma

2021 
Background Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer. Ferroptosis, an oxidative, iron-dependent form of necrotic cell death, is highly associated with tumorigenesis and cancer progression. However, the prognostic value of ferroptosis progress in LUAD was still rarely be investigated. Methods Herein, we collected three mRNA expression profiles and 85 ferroptosis-related genes from public databases. The "limma" package was used to identify ferroptosis-related differentially expressed genes (DEGs). Univariate Cox regression analysis and LASSO regression analysis were applied to screen and develop a ferroptosis-related gene signature (FRGS) and a formula to calculate the risk score. Multivariate Cox regression analysis was implemented to determine independent prognostic predictors of overall survival (OS). The area under the receiver operating characteristic curve (AUC) and calibration plot were used to evaluate the predictive accuracy of the FRGS and nomogram. Results We developed a FRGS with five genes (CYBB, CISD1, FADD, SAT2, VDAC2). The AUC of the FRGS in TCGA cohort was 0.777 at 1-year, 0.721 at 3-year and 0.725 at 5-year, significantly superior to the AUC of TNM stage (1-year: 0.701, 3-year: 0.691, 5-year: 0.686). A similar phenomenon was observed in GEO cohort 1 and 2. Multivariate Cox regression analysis indicted TNM stage and risk score were independent prognostic predictors. Finally, we built a nomogram with TNM stage and FRGS, the AUCs of which markedly higher than that of FRGS or TNM stage alone. Conclusion We constructed a prognostic FRGS with five ferroptosis-related genes and a nomogram for predicting the 1-, 3- and 5-year survival rate of LUAD patients, which may provide a new understanding of the prognostic value of ferroptosis progress in LUAD and will benefit prognosis assessment of LUAD patients.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    36
    References
    3
    Citations
    NaN
    KQI
    []