Identification and Analysis of Senescence-Related Genes in Head and Neck Squamous Cell Carcinoma by a Comprehensive Bioinformatics Approach

Figure 7

CSRS.Score combined with clinicopathological characteristics to further improve prognostic models and survival prediction. (a) Full-scale annotations of patients including gender, CSRS.Score, age, grade, and TNM stage were applied to develop a survival decision tree for optimizing the risk stratification. (b) Among the four subgroups, significant differences in overall survival could be found. (c, d) Comparative analysis between different subgroups. (e, f) Univariate (e) and multivariate (f) Cox analysis of CSRS.Score and clinicopathological features. (g) A nomogram was developed based on age, CSRS.Score, and stage. (h) Calibration curves for 1, 3, and 5 years for the columnar graph. (i) Decision curve analysis of the nomogram, CSRS.Score, and other clinical features. (j) Compared with other clinicopathological features, the nomogram exhibited the most powerful capacity for survival prediction.

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