Discovery and Validation of an Epithelial-Mesenchymal Transition-Based Signature in Gastric Cancer by Genomics and Prognosis Analysis

Figure 2

Generation of a prognostic EMT-related gene model for gastric cancer in TCGA dataset. (a) Univariate Cox regression analysis for prognosis-related EMT-related genes in gastric cancer. (b) Selecting the optimal parameter () in the LASSO model using 10-fold cross-verification. (c) LASSO coefficient profiles of prognosis-related EMT genes. (d) Distribution of RS in gastric cancer patients and determination of the cutoff value of high-RS (red) and low-RS (green) groups according to RS median. (e) Distribution of survival status (dead: red and alive: green) in high- and low-RS groups. (f) Kaplan-Meier OS curves for the high- and low-RS groups. (g) The time-dependent ROC for the RS model. (h) Univariate and (i) multivariate Cox regression analyses of RS and other clinical features.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)