Research Paper Volume 13, Issue 10 pp 14499—14521

Identification of a 15-pseudogene based prognostic signature for predicting survival and antitumor immune response in breast cancer

Construction of the risk score model based on prognostic pseudogenes. (A) The hazard ratios (HR), 95% confidence intervals (CI) calculated by univariate Cox proportional hazard regression of 15 prognostic pseudogenes using TCGA data. (B) LASSO coefficient profiles of 15 prognostic pseudogenes. (C) Ten-time cross-validation for tuning parameter selection in the LASSO model of 15 prognostic pseudogenes. (D) The breast cancer patients from TCGA dataset in high-risk group displayed significantly shorter overall survival than those in low-risk group (p = 0.0025). (E) The breast cancer patients from EGA dataset in high-risk group displayed significantly shorter overall survival than those in low-risk group (p = 0.0313). (F) The ROC curve and AUC for the risk score model in TCGA dataset. (G) The ROC curve and AUC for the risk score model in EGA dataset.

Figure 1. Construction of the risk score model based on prognostic pseudogenes. (A) The hazard ratios (HR), 95% confidence intervals (CI) calculated by univariate Cox proportional hazard regression of 15 prognostic pseudogenes using TCGA data. (B) LASSO coefficient profiles of 15 prognostic pseudogenes. (C) Ten-time cross-validation for tuning parameter selection in the LASSO model of 15 prognostic pseudogenes. (D) The breast cancer patients from TCGA dataset in high-risk group displayed significantly shorter overall survival than those in low-risk group (p = 0.0025). (E) The breast cancer patients from EGA dataset in high-risk group displayed significantly shorter overall survival than those in low-risk group (p = 0.0313). (F) The ROC curve and AUC for the risk score model in TCGA dataset. (G) The ROC curve and AUC for the risk score model in EGA dataset.