Classification and prognosis of ovarian cancer based on platinum resistance related genes
Objective This study aimed to use platinum resistance-related(PRR)genes to classify ovarian cancer(OV)patients and establish a prognostic risk model,in order to provide reference for individualized clinical treatment and related mechanism research of OV patients.Methods The univariate Cox regression was used to screen PRR genes with prognostic value,unsupervised consensus clustering was used for subtyping,and the prognostic differences were compared between subtypes.LASSO regression analy-sis was used to further screen prognosis related PRR genes and construct platinum resistance related gene scores(PRR-GS),com-bined with clinical information,constructed a prognostic risk model for OV patients,and validated it.Results Univariate Cox regres-sion analysis identified 68 platinum resistance related genes that were associated with patient prognosis.Unsupervised consensus clus-tering identified 2 subtypes of OV,with C1 group patients having a better prognosis.Twelve genes were used to construct prognostic markers for PRR genes score,and the area under the ROC curves at 1,3,and 5 years were all above 0.700.Age,stage,and PRR-GS were independent factors affecting prognosis in multivariate Cox regression analysis.The model's C-index was 0.719,and the areas under the ROC curves for 1-year,3-year,and 5-year overall survival rates were 0.774,0.758,and 0.768,respectively.The calibra-tion curve and decision curve analysis showed that the model had good predictive performance.Conclusion Two subtypes of OV pa-tients were identified,each with differences in immune microenvironment,prognosis,and other aspects.The platinum resistance gene prognostic score is an independent risk factor,and when combined with clinical variables,it can effectively predict patient survival out-comes.
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