Exploratory experiment designed to discover new patterns targeting 88 IPCD-related genes in Human patients (TCGA, GEO, ICGC databases). Primary outcome: Overall survival prediction
This bioinformatics analysis aimed to identify immune-related programmed cell death (IPCD) signatures for predicting ovarian cancer prognosis. The researchers downloaded ovarian cancer datasets from TCGA, GEO, and ICGC databases. They screened prognostic genes from IPCD-related differential genes using univariate cox regression analysis. The construction of the IPCDS (immune-related programmed cell death signature) model was performed via 101 algorithm combinations to optimize predictive performance. The model's prognostic capability was validated across multiple datasets using Kaplan-Meier survival analysis and time-dependent ROC curves. The study found 88 IPCD-related prognostic genes that were used for modeling, with the low-IPCDS group showing significantly higher overall survival compared to the high-IPCDS group across most datasets.
Univariate cox regression analysis, 101 algorithm combinations for model construction, Kaplan-Meier analysis, timeROC curves
Identification of prognostic signatures that can predict ovarian cancer patient survival
Higher AUC values in timeROC analysis, significant survival differences between high and low IPCDS groups
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