Construction and verification of risk scoring prognostic model
After 1000 resampling, 22 metabolic genes were subjected to LASSO Cox
regression analysis to construct a prognostic model, which containing 16
metabolic genes. It shows the 16 genes and their coefficients in the
risk scoring model (Table 1 ). Calculate the risk score of each patient
based on the mRNA expression level and risk coefficient of each gene.We
divided the TCGA-COAD and GSE40976 samples into high-risk groups and
low-risk groups based on the median risk score. Kaplan–Meier analysis
was performed to prove that the overall survival of the high-risk group
was poor (Figure 2A, B). The risk score distribution showed that the
mortality rate of the high-risk group was higher than that of the
low-risk group (Figure 2C, D). A heat map was developed to show the
high-risk and low-risk TCGA-COAD and GSE40976 gene expression profiles
(Fig. 2E, F). The heat map shows the expression of 16 gene markers.
SEPHS1, P4HA1, ENPP2, PTGDS, GPX3, CP, ASPA, POLR3A, PKM and POLR2D are
positively correlated with high-risk groups, indicating that high
expression of these genes is associated with a shorter overall survival
time. XDH, EPHX2, ADH1B, HMGCL, GPD1L, and MAOA revealed opposite
effects, indicating that high expression of these genes is associated
with longer overall survival time. P Value<0.05 is considered
statistically different.