Comprehensive analysis of disulfidptosis related genes and prognosis of gastric cancer

Figure 1 The results of the differential expression analysis of disulfidptosis related genes in gastric cancer and adjacent normal tissues are presented. A: Shows the difference analysis of disulfidptosis related genes in gastric cancer tissue samples and adjacent normal tissue samples; B: Shows the waterfall plot of disulfidptosis related genes mutations; C: Presents the mutation frequency of disulfidptosis related genes; D: Shows the mutation sites of disulfidptosis related genes. CNV: Copy number variation.

Figure 2 Screening disulfidptosis related genes related to the prognosis of gastric cancer. A-J: Show the Kaplan-Meier analysis of the survival curves of disulfidptosis related genes between high and low expression groups, and 10 disulfidptosis related genes related to gastric cancer prognosis were identified; K: Shows the COX analysis of the disulfidptosis related genes circle plot related to gastric cancer prognosis, and five significantly prognostic disulfidptosis related genes were identified.

Figure 3 The sample classification, subgroup survival analysis, and differential gene heatmap related to disulfidptosis related genes are presented. A: Shows the clustering matrix plot of disulfidptosis related genes-related samples; B: Shows the clustering index plot of disulfidptosis related genes-related samples; C: Presents the relative change area under the cumulative distribution function curve; D: Shows the tracking plot of disulfidptosis related genes subgroup samples; E: Presents the survival analysis curves of disulfidptosis related genes subgroups; F: Shows the differential gene clustering heatmap between disulfidptosis related genes subgroups.

Figure 4 The significantly different kyoto encyclopedia of genes and genomes pathways and gene ontology functional analysis between disulfidptosis related genes subgroups are presented. A: Shows the significantly different kyoto encyclopedia of genes and genomes pathway enrichment analysis between disulfidptosis related genes subgroups; B: Shows the significantly different gene ontology pathway enrichment analysis between disulfidptosis related genes subgroups.

Figure 5 The immune cell differential analysis, principal component analysis analysis, significantly different genes, and gene ontology/kyoto encyclopedia of genes and genomes analysis between disulfidptosis related genes subgroups are presented. A: Shows the immune cell differential analysis between disulfidptosis related genes subgroups; B: Presents the principal component analysis analysis of disulfidptosis related genes subgroups; C: Shows the significantly different genes between disulfidptosis related genes subgroups; D: Presents the gene ontology analysis of significantly different genes between disulfidptosis related genes subgroups; E: Presents the kyoto encyclopedia of genes and genomes analysis of significantly different genes between disulfidptosis related genes subgroups.

Figure 6 The differential gene-related sample clustering matrix, clustering index, cumulative distribution function curve, tracking plot, survival curve, heat map, differential analysis of disulfidptosis related genes, lasso regression plot, and cvfit plot are presented. A: Shows the clustering matrix of differential gene-related samples; B: Presents the clustering index of differential gene-related samples; C: Shows the relative change area of the cumulative distribution function curve of differential gene-related samples; D: Presents the tracking plot of differential gene subgroups; E: Shows the survival curve of differential gene-related samples; F: Presents the heat map of differential gene-related samples; G: Presents the differential analysis of disulfidptosis related genes between differential gene-related sample groups; H: Shows the lasso regression plot; I: Presents the cvfit plot of the lasso regression.

Figure 7 Testing the reliability of prognostic models. A: A Sankey diagram of the relationships between various data is presented; B: Shows a box plot of the disulfidptosis subtype; C: Presents a box plot of gene subtypes; D: Shows the differential analysis of disulfidptosis related genes between high and low-risk groups.

Figure 8 The accuracy of the prognostic model was verified by subgroup analysis. A-C: Survival curves between different groups are presented in panels; D-F: Show receiver operating characteristic curves between different groups; G-I: Risk curves for each group are presented; J-L: Show survival status plots for each group; M-O: Risk heat maps for each group are presented.

Figure 9 Further analysis of prognostic models to screen potential therapeutic targets. A: Presents a column line chart; B: Shows a calibration curve; C: Presents a heat map of the correlation between model genes and immune cells; D:The survival curve of APOD in gastric cancer was significantly different between high and low risk groups (P < 0.05); E: The survival curve of PLS3 in gastric cancer was shown between high and low risk groups, and the results suggested that the difference was not significant (P > 0.05).

Figure 10  The correlation between immune cells and risk scores is analyzed. A: There was a positive correlation between B cells naive and risk score; B: The result shows that macrophages M0 is negatively correlated with risk score; C: The results showed that macrophages M1 was positively correlated with risk score; D: The results showed that macrophages M2 was positively correlated with risk score; E: There was a negative correlation between mast cells activated and risk score; F: The results showed that mast cells reting was positively correlated with risk score; G: There was a positive correlation between monocytes and risk score; H: There was a negative correlation between natural killer cells activated and risk score; I: There was a negative correlation between plasma cells and risk score; J: There was a positive correlation between T cells CD4 naive and risk score; K: It showed that T cells follicular helper was negatively correlated with risk score; L: There was a positive correlation between T cells gamma delta and risk score; M: There was a positive correlation between T cells regulation and risk score.

Figure 11  The correlation between the prognostic scoring model and tumor microenvironment, microsatellite instability, and RNAss, as well as drug sensitivity analysis, is presented. A: Shows the tumor microenvironment score for high and low-risk groups; B and C: Present waterfall plots of mutations for high and low-risk groups; D: Analyzes the differences in tumor mutation burden between high and low-risk groups; E: Shows the relationship between tumor mutation burden and risk score; F and G: Present microsatellite instability analysis for high and low-risk groups; H: Analyzes the correlation between stem cells and risk score; I and J: Present drug sensitivity analysis for drugs such as Bosutinib and Bryostatin.

Figure 12  The immunohistochemical analysis of the APOD gene based on human protein atlas is presented. A: Tumor tissue; B: Normal tissue. T: Tumor tissue; B: Normal tissue.