Fig. 4

Principal component analysis of risk prognostic models. (A) PCA conducted on the complete gene set demonstrates the capacity to differentiate between different risk individuals. The first three principal components accounted for 11.9%, 7.16%, and 4.76% of the variance, respectively. (B) PCA focused on MRGs reveals the capacity to differentiate high-risk from low-risk populations, with the top three components explaining 53.07%, 11.6%, and 10.83% of the variance.(C) PCA analysis of MRLs indicates their efficacy in differentiating between different risk individuals, with the top two components accounting for 33.02%, 8.07%, and 4.96% of the variance. (D) PCA of the risk model effectively differentiates between different risk groups, with the top three components (from four lncRNAs) accounting for 42.1%, 23.68%, and 19.27% of variance.