Construction And Verification For A Prognostic Model Of Ferroptosis From Colorectal Cancer Based On Bioinformatics

Background:Colorectal cancer(CRC)is a malignant tumor disease of digestive tract with extremely high incidence.Although the early screening for CRC such as colonoscopy has been continuously carried out and the long-term prognosis of CRC has been effectively improved,the latest data shows that the incidence of CRC are still increasing.In recent years,ferroptosis,a form of programmed cell death,is newly discovered and its main feature is the accumulation of lipid peroxides on the iron ion-dependent cell membrane,which causes a series of cell physiological dysfunctions such as cell oxidation stress and metabolic disorders,and ultimately leads to the death of the cell.According to the most recent research and studies,scientists have explained and proved that ferroptosis plays a key role in killing CRC cells and inhibiting their occurrence and development.Purpose and Method:Colorectal cancer data from The Cancer Genome Atlas(TCGA)database was used to analyze the differential genes between tumor and normal paired samples.These genes were compared with ferroptosis-related genes to filter out a set of candidate genes,which was then subjected to univariate Cox regression analysis to select for the appropriate genes based on univariate p-value to perform LASSO regression analysis.The genes that show statistically significance were used to construct a survival-related linear analysis assessment model,that calculated the risk value of each sample with its median as the cutoff value for dividing the sample into high and low risk groups.The time-dependent ROC curve was used to evaluate the predictive ability of the model for 1,3,and 5 years of survival,and to analyze the survival curve of the high and low risk groups.The GSE143985 and GSE39582 data sets in the GEO database were used as verification for the prognosis model.The clinical data was used to draw a nomogram to show the mutational differences of ferroptosis factors in the high and low risk groups.The TCGA data set was subjected to differential gene reanalysis and pathway enrichment analysis according to the high and low risk standards.Finally,univariate enrichment analysis was used to evaluate the differences in risk levels of immune cells and their functions,and to predict the impact of the risk and the influence of immunotherapy.Result:In this study,634 samples were downloaded from the TCGA database and found to have differentially expressed genes between tumor and normal tissues.These genes were then subjected to compare with the 268 ferroptosis-related genes obtained from the ferroptosis database.A total of 133 candidate ferroptosis differential genes were obtained,in which 77 genes result in up-regulated expressions and 56 genes result in down-regulated expressions.By univariate Cox regression analysis,a total of 16 genes were obtained.From their expression and survival analysis,the survival curve of 11 genes had statistical significance.After LASSO-Cox regression analysis,a survival-related risk assessment model composed of genes SLC2A3,DRD4,CXCL2,TFAP2C,DUOX1 and DDIT3 was constructed.After verification and analysis,the model can effectively distinguish high and low risk patients,being capable to predict their survivability,and rate immune cells and immune pathways.The analytical results showed that patients in the high-risk group responded better to immunotherapy.Conclusion:Through screening and verification,this study constructed a risk assessment model,based on the 6 differentially expressed genes related to ferroptosis,for predicting the prognosis of colorectal cancer.The model showed that the high-risk group had a better response to immunotherapy,revealing the potential relationship between ferroptosis and immune infiltration therapy.