Figure 6

Validation of the prognostic performance of the prognostic risk model. (A) Principal component analysis demonstrating the ability of the risk model scores to discriminate between samples in the Training group. (B) Principal component analysis demonstrating the ability of the risk model scores to discriminate between samples in the Testing group. (C) The TCGA dataset yields ROC curves validating the predictive performance of the risk model over the 1-year, 3-year, and 5-year periods, with AUCs of 0.747, 0.738, and 0.738, respectively. (D) ROC curves incorporating traditional clinical factors in the TCGA group validated the predictive performance of the risk model, with risk scores: 0.747; age: 0.646; gender: 0.468; stage: 0.623; T: 0.555; M: 0.593; N: 0.588. (E) The GEO dataset yields ROC curves validating the predictive performance of the risk model over the 1-year, 3-year, and 5-year periods, with AUCs of 0.616, 0.568 and 0.562, respectively, respectively. (F) ROC curves incorporating traditional clinical factors in the GEO group validated the predictive performance of the risk model, with Risk Score: 0.617; Age: 0.612; Gender: 0.525; Stage: 0.640; T: 0.467; M: 0.486; N: 0.457. (G) Nomogram plot to validate that the risk model scores with good prognostic performance. (H) Standard curve showing that 1-, 3-year performance would be more accurate than 5-year. (I) Successively combined traditional clinical factors in univariate Cox regression (HR = 3.442 (2.224–5.376)) and (J) multifactorial Cox regression analyses (HR = 3.024 (2.119–4.823)), P < 0.001.