Fig. 4 | Scientific Reports

Fig. 4

From: Functional genomics integration of glycolysis-related gene networks reveals prognostic biomarkers and immune microenvironment regulation in breast cancer

Fig. 4

Development and validation of a multi-gene prognostic signature for patient stratification and survival prediction. (A) Forest plot of univariate Cox regression analysis identifying prognostic genes. (B, C) LASSO regression analysis of selected prognostic genes. (D) The distribution of clinical characteristics and the expression of model genes according to the GRGs risk score. (E) Sankey diagram showing the relationship between survival status, GRGs clusters, and risk scores. (F) Difference-in-difference analysis of cluster risk scores. (G) KM curve showing the correlation between Riskscore prediction model and prognosis in the TCGA cohort. (H) Survival time status in the TCGA cohort. (I) ROC curves for 1-year, 3-year, and 5-year prognoses based on gene prognostic features in the TCGA cohort. (J) KM curve showing the correlation between Riskscore prediction model and prognosis in the METABRIC cohort. (K) Survival time status in the METABRIC cohort. (L) ROC curves for 1-year, 3-year, and 5-year prognoses based on gene prognostic features in the METABRIC cohort. ****p < 0.0001.

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