Lipid Metabolism-Related Gene Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Advanced Gastric Cancer

Figure 7

AGC risk characteristics were developed and verified, and a line graph was established using GSEA. (a) Five pathways activated in C2 were selected by GSEA based on differential genes between C2 and C1. (b, c) The batch effect was removed by combining the AGC samples of TCGA-STAD and GSE62254. (d, e) LASSO-Cox regression was used to construct a prognostic model. When , a prognostic model containing 6 genes was obtained. (f) X-Tile software was used to calculate the optimal cut-off value of 0.74. (g) Prognostic heat maps showed the survival status, risk score, and distribution of 6 gene expressions of AGCs in high-risk and low-risk groups. (i–k) Using TCGA-STAD as the training set, KM curve, ROC curve, and DCA curve were used to determine the prognostic value of the model. (l–n) GSE62254 was used as external validation to verify the prognostic value of the model by KM curve, ROC curve, and DCA curve. (o, p) Based on TCGA-STAD, risk score combined with TNM stage, age, gender, differentiation degree, and stage to draw calibration curve. (q) ROC curve was plotted to predict the prognosis of AGC patients.

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