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pmid:35741835
Robust Prediction of Prognosis and Immunotherapy Response for Bladder Cancer through Machine Learning Algorithm.
Shanshan Hu · Shengying Gu · Shuowen Wang · Chendong Qi · Chenyang Shi · Fengdan Qian · Guorong Fan
Genes · 2022 doi:10.3390/genes13061073
Paper
complete
Experiment
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Data
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Analysis
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Execution
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Complete file
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In this study, a comprehensive analysis of 19 ferroptosis-related genes (FRGs) was performed in 1322 patients with BCa from four independent patient cohorts and a pan-cancer cohort of 9824 patients. Twelve FRGs were selected through machine learning algorithm to construct the prognosis model. Significantly differential survival outcomes (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.55−2.82, p < 0.0001) were observed between patients with high and low ferroptosis scores in the TCGA cohort, which was also verified in the E-MTAB-4321 cohort (HR = 4.71, 95% CI: 1.58−14.03, p < 0.0001), the GSE31684 cohort (HR = 1.76, 95% CI: 1.08−2.87, p = 0.02), and the pan-cancer cohort (HR = 1.15, 95% CI: 1.07−1.24, p < 0.0001). Tumor immunity-related pathways, including the IL-17 signaling pathway and JAK-STAT signaling pathway, were found to be associated with the ferroptosis score in BCa through a functional enrichment analysis. 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