Development And Validation Of A Prognosis Model Based On Ferroptosis Related Genes In Lung Adenocarcinoma

Background and objective:Lung adenocarcinoma is the most common subtype of non-small cell lung cancer,and accounts for more than 500,000 deaths annually worldwide.Despite some advances in the molecular diagnosis and targeted therapy at present,the overall survival has not improved significantly of patients with lung adenocarcinoma,and the majority of patients with lung adenocarcinoma have locally advanced disease or distant metastases at the time of initial diagnosis.Ferroptosis is a type of regulatory cell death,which is different from autophagy and apoptosis,first proposed by Dixon et al.Ferroptosis was mainly characterized as an iron-and reactive oxygen species dependent cellular changes,including mitochondria crista vanishes,mitochondrial membrane rupture and condensed mitochondrial membrane densities,and its mechanism primarily is lipid peroxidation that causes the loss of membrane selective permeability.In recent years,ferroptosis have gradually been recognized as a self-adaptation function to remove malignant cells,and serve an important role in the occurrence and development of tumors.This study analyzed the expression of ferroptosis related genes by bioinformatics in lung adenocarcinoma,and constructed a nomogram based on these genes which can predict prognosis for patients with lung adenocarcinoma,and contributed new approaches and perspectives for prognostic stratification and individualized therapy.Methods:The gene expression profiles,survival data and clinical data of lung adenocarcinoma were downloaded from The Cancer Genome Atlas(TCGA)public database.Compared the expression of ferroptosis genes in tumor tissue and tissue adjacent to tumor,used differentially expressed genes as the input of residual network for training,and determine the formula of risk score based on 10 ferroptosis related genes.Classified samples into high risk group and low risk group depending on their risk score.Kaplan-Meier survival curves were used to compare survival differences between two groups.Using univariate Cox regression and multivariate Cox regression evaluated the hazard ration between two risk groups and the independence of the risk group model as a prognostic predictor.Constructed the nomogram based on the regression result,the prognostic efficiency and stability of the nomogram were assessed by receiver operating characteristic curve(ROC),C-index,calibration curve and decision curve analysis.Performing gene functional enrichment analysis to further explore the biological differences between two risk groups.Results:(1)The formular of risk score was obtained in the training set which contained CYBB,FURIN,DPP4,ETV4,RRM2,NR4A1,EPAS1,GCLC,TNFAIP3 and AKR1C1.Based on the risk score,divided samples into high risk group and low risk group,and the prognosis of these tow groups was significant different(p<0.0001)and the difference was validated in the validation set(p=0.004).(2)Utilized patient risk group and clinical information for univariate cox regression analysis and multivariate cox regression analysis,and low risk group compared with high risk group had a lower risk,the hazard ratio was 0.23 in univariate cox regression analysis(p<0.0001),and 0.26 in multivariate cox regression analysis(p<0.0001).(3)Using risk group,p T stage,p N stage and tumor stage to construct nomogram.In the training set,the area under curve(AUC)was 0.81 at 1year,0.82 at 3 years and0.78 at 5 years.In the validation set,the AUC was 0.77 at 1year,0.73 at 3 years and0.70 at 5 years.(4)The gene functional enrichment analysis revealed that the signal pathways of cell proliferation and invasion were more active in high risk groups,and the signal pathways of cell clearance and immune responses were more active.The tumor purity was much higher in high risk group then low risk group(p<0.001).There were significant differences of several immune cell infiltration and tumor microenvironment between two risk groups.Conclusions:(1)This study using bioinformatics analysis developed a risk group model for lung adenocarcinoma,which was an independent prognosis factor for patients with lung adenocarcinoma.(2)According the results of gene functional enrichment analysis,the high risk group had a higher tendency to be aggressive than the low risk group,which provide a molecular biology theoretical basis for the risk group model.(3)The nomogram which combing the risk group and clinical data had a pivotal prognostic value,and was able to identify the risk and predict prognosis for patients with lung adenocarcinoma.