Objective:To identify different molecular subtypes of ovarian cancer and to construct a risk pre-diction model.Methods:Ovarian cancer was classified into three subtypes based on the transcriptomic data from the TCGA database.GO/KEGG enrichment analysis was used to explore biological pro-cess differences across different subtypes.The prognostic model was constructed using the Cox pro-portional hazards model and Lasso regression methods.The receiver operating characteristic(ROC)curves and the Kaplan-Meier method were used to detect model accuracy.Results:This study found that the vast majority of immune-related prognosis genes showed low expression in the C1 subtype and opposite expression in the C3.Among them,patients with the C2 subtype have the best progno-sis.Clear differences in the biological processes and pathways enriched by the three ovarian cancer mo-lecular subtypes provide references for revealing the mechanisms of different prognoses of the three dif-ferent subtypes.GABRD was identified as a key gene in the development of ovarian cancer associated with Treg,CAFs,and TGFB,suggesting that it may function by suppressing immunity and remodel-ing the tumor microenvironment.In addition,a prognostic model including SIGLEC14,EREG,ATP2A3,INSC,NKAIN4,DACT1,and RYR2 was constructed,where the AUC values for 3-and 5-year OS were 77%and 86%,respectively.Similar results were also observed in the test set.Conclusion:This study identified and validated a new prognostic model for ovarian cancer,which may help to better understand its prognosis and conduct risk stratification to make more rational treat-ment decisions.
万方数据
维普
