Constructing a new prognostic signature of gastric cancer based on multiple data sets

Figures & data

Figure 1. Flow chart of this research

Figure 2. The differentially expressed genes in gastric cancer

(A) Heat map; (B) Volcano plot.

Figure 3. Functional enrichment analysis of the DEGs

(A) The biological process enrichment results of GO with different genes. (B) The cellular components enrichment results of GO with different genes. (C) The molecular function enrichment results of GO with different genes. (D) KEGG enrichment results of differential genes.

Figure 4. Prognosis-related gene screening

(A, B) Selecting the best parameters for gastric cancer in LASSO regression analysis, and 26 gene probes significantly related to prognosis; (C) Multivariate Cox regression analysis to get prognosis related 14 genes.

Figure 5. Prognostic analysis of 14-genes signature in the train cohort

(A) Scatterplots of GC patients with different survival status in training group; Risk score distribution of GC patients with different risks (low, green; high, red) in the testing group; (B) Kaplan–Meier Survival curve of low-risk and high-risk subgroups; (C) 5-year time-dependent ROC for survival prediction models; (D) Univariate Cox regression analysis on the prognosis of clinicopathological characteristics and risk scores in patients with GC; (E) Multivariate Cox regression analysis on the prognosis of clinicopathological characteristics and risk scores in patients with GC.

Figure 6. Prognostic analysis of 14 genes signature in the GSE15459 data set

(A) Scatterplots of GC patients with different survival status in training group; Risk score distribution of GC patients with different risks (low, green; high, red) in the testing group; (B) Kaplan–Meier Survival curve of low-risk and high-risk subgroups; (C) 5-year time-dependent ROC for survival prediction models; (D) Univariate Cox regression analysis on the prognosis of clinicopathological characteristics and risk scores in patients with GC; (E) Multivariate Cox regression analysis on the prognosis of clinicopathological characteristics and risk scores in patients with GC.

Figure 7. Risk and clinicopathological characteristics of 14 genes

(A) The relationship between clinicopathological characteristics and risk group; (B) The relationship between 14 genes expression level and clinicopathological characteristics and risk value.

Figure 8. High-risk group conducts GSEA enrichment pathway analysis

Figure 9. Establishment and validation of Nomogram (a) Nomogram for predicting 1–5 years OS of GC patients. (b) calibration chart for nomogram accuracy

Figure 10. Verification of 14 genes expression in GC and normal gastric tissue using the HPA database

Figure 11. Validation the prognostic value of 14 genes in GC by Kaplan Meier-plotter