A ferroptosis prognosis model constructed for urological tumors based on bioinformatics analysis
Objective To construct and validate a prognosis model related to ferroptosis in urinary tract tumors u-sing bioinformatics methods.Methods RNA-seq and clinical data from TCGA's BLCA and KIRC datasets were analyzed to establish the prognostic model,and then were validated using ICGC and GEO data.Prognostic genes associated with ferroptosis were identified through univariate Cox,LASSO-Cox,and multivariate Cox regression an-alyses.Co-expression and protein-protein interaction(PPI)network analyses determined the relationships among these genes.Immune infiltration analysis explored the association between ferroptosis-related prognostic genes and the immune microenvironment.Functional enrichment analysis of differentially expressed genes between high and low-risk groups in BLCA and KIRC prognostic models was conducted to investigate potential mechanisms by which ferroptosis-related genes regulate BLCA and KIRC prognosis.Results Significant prognostic gene signatures asso-ciated with ferroptosis were identified in BLCA and KIRC.For BLCA,the genes EGR1,ZEB1,P4HB,WWTR1,JUN,CDO1,SCD,SREBF1,CAV1,and GALNT14 were significant.For KIRC,the genes ASMTL-AS1,CHAC1,MT1G,RRM2,TIMP1,DPEP1,GLRX5,and NDRG1 were significant.Ferroptosis-related miRNAs linked to the prognosis of both cancers were also identified.The constructed risk models based on these genes and miRNAs pre-dicted patient prognosis in TCGA-BLCA and KIRC,with low-risk groups showing significantly higher overall surviv-al(P＜0.05).The hazard ratios for these models ranged from 2.54(95％CI:1.73-3.74)to 4.74(95％CI:3.47-6.47),with AUC values above 0.60.Co-expression analysis and PPI networks revealed high correlation levels between JUN and EGR1 in BLAC and between SCD and SREBF1.Immune infiltration analysis indicated positive correlations between EGR1,CAV1,JUN,and immune scores,while SREBF1 showed a negative correla-tion.Conclusion The prognosis model based on ferroptosis-related genes effectively predicts patient outcomes in BLCA and KIRC.This model can serve as a reference for targeting ferroptosis to assess the prognosis of BLCA and KIRC patients.
万方数据
维普
