Additional file 1 of Multiomics characterization of pyroptosis in the tumor microenvironment and therapeutic relevance in metastatic melanoma
Creators: name: Chen, Wenqiong
name: He, Yi
name: Zhou, Guowei
name: Chen, Xiang
name: Ye, Youqiong
name: Zhang, Guanxiong
name: Liu, Hong
Publisher: name: figshare
Year: 2024
Subjects: subject: Immunology
subject: FOS: Clinical medicine

description: Additional file 1: Table S1. Summary of human melanoma datasets. Fig. S1. Expression pattern and NMF clustering of PRGs in TCGA-SKCM. (A) Gene expression correlation of 74 pyroptosis-related genes in primary (bottom left) and metastatic (top right) samples. Blue and red indicate the magnitudes of correlations: blue, high; red, low. Significant points are labeled using *. Hierarchical clustering based on gene expression was used. (B) The bar graph shows the number of PRGs correlating positively or negatively with each PRG, with the primary tumor group on the left and metastatic tumor group on the right. The X axis represents the number of positively or negatively correlating PRGs, and the Y axis represents the PRGs. The positive and negative signs of the X axis coordinates represent positive or negative correlations. A significant correlation was considered when the absolute value of Pearson correlation coefficient was greater than 0.3 and p-value &lt; 0.05. (C) Venn diagram showing PRGs correlating with GSDMD expression in primary and metastatic tumors. The number of genes in each part of the Venn diagram and their percentage in the overall population (26 genes) are indicated in the figure. Metastatic: 13 (50%); overlap: 9 (34.6%); primary: 4 (15.4%). (D-E) Optimal NMF clustering of mRNA expression profiles for primary (D) and metastatic (E) samples. The left image shows the parameters of NMF clustering, and the optimal rank was selected as the front point of the line segment with the minimum slope in the cophenetic plot. The image on the right is the consensus matrix drawn based on the features extracted by the NMF algorithm. (F) Gene expression correlation of 34 PRGs in metastatic samples. Both the horizontal and vertical axes are 34 PRGs. Blue and red indicate the magnitudes of the correlations: blue, high; red, low. Significant points are labeled using black asterisks. Fig. S2. Validation of the PScore model using 3 pyroptosis-related datasets. Pyroptosis-related datasets, including GSE57253, GSE153494 and GSE192714, were obtained from GEO, and PScore was calculated with significant genes using univariate Cox regression analysis. The x axis represents the control group and experimental groups in the corresponding dataset. PScore was significantly higher in the experimental groups than the control group (GSE57253: left, Kruskal‒Wallis, P = 5.7E-03; GSE153494: middle, Kruskal‒Wallis, P = 0.014; GSE192714: right, Wilcoxon, P = 0.029). In GSE57253, NLRC4-macrophage activation syndrome (MAS) and neonatal-onset multisystem inflammatory disease (NOMID) are related to NLRC4 or NLRP3, respectively, and accompanied by IL-1β and IL-18 overproduction and increased pyroptosis. GSE153494 was used to describe the progression status of myocardial infarction associated with pyroptosis over time. Mll4 knockout elicits GSDMD-mediated pyroptosis in GSE192714. Fig. S3. Forest plot representation of multivariate Cox regression analyses using 4 datasets. Multivariate Cox regression analyses of PScore and 3 other variables, including sex, age and stage, in TCGA-SKCM (A) and 3 independent melanoma datasets, including GSE19234 (B), GSE54467 (C) and GSE65904 (D). Risk factors: HR &gt; 1 and p-value &lt;= 0.05; protective factors: HR &lt; 1 and p-value &lt;= 0.05; nonsignificant factors: p-value &gt; 0.05. Fig. S4. Clinical relevance of PRGs in primary cutaneous melanoma. (A) Heatmap showing expression of 74 PRGs in different NMF clusters or PScore groups of primary patients in the TCGA-SKCM cohort; red and blue denote high and low expression, respectively. The horizontal axis represents the individual patients, and the vertical axis represents the PRGs. PFS, OS and patient identities are shown above the heatmap. Light green and green are used to represent the ‘Low_PS’ and ‘High_PS’ groups. Red, blue and orange represent ‘Cluster_1’, ‘Cluster_2’ and ‘Cluster_3’ derived from NMF. The results of univariate Cox regression analysis of PRGs are annotated on the left, as are the PRGs selected as features during NMF. PRGs were divided into three subclusters (PRGs_cluster) using k-means clustering. (B) KM curves for OS in primary samples stratified by the NMF algorithm. The x-axis represents survival time (unit: year), and the y-axis represents OS rate. The colors of the KM curves represent different NMF-derived clusters. (C) Forest plot showing univariate Cox regression analysis of OS with 74 PRGs in primary melanoma patients. The x-axis represents the HR, and the y-axis represents the different PRGs. Yellow and blue indicate nonsignificant and protective genes, respectively. Protective genes: HR &lt; 1 and p-value &lt;= 0.05; nonsignificant genes: pvalue &gt; 0.05. (D) Comparison of PScore across NMF-derived clusters. The x-axis represents NMF-derived clusters, and the y-axis represents PScore. Colors correspond to the heatmap (A) and KM curve (B). (E) Kaplan‒Meier curves for OS in primary samples stratified by PScore. The x-axis represents survival time (unit: year), and the y-axis represents OS rate. The colors of the KM curves represent different PScore groups. Fig. S5. Combining druggable mutations and PScore distinguishes the survival of metastatic BRAF-mutated melanoma patients. (A-B) Mutation landscapes of the top 10 high-frequency mutations and PRGs. The horizontal axis of the heatmap represents the patients, and the vertical axis represents the genes. (C-D) Boxplots show PScore in different mutation statuses in metastatic TCGA-SKCM. C: Top 10 high-frequency genes. D: Common therapeutic targets and top four PRGs. The x-axis represents mutation statuses, and the y-axis represents PScore. (E) Forest plot showing the HR of survival analysis related to Figure 2E. Risk factors: HR &gt; 1 and p-value &lt;= 0.05; protective factors: HR &lt; 1 and p-value &lt;= 0.05; nonsignificant factors: pvalue &gt; 0.05. Fig. S6. Immune cell scores calculated with MCPcounter for seven datasets. Correlation of PScore with immune cell scores in multiple melanoma cohorts. The x-axis represents the type of infiltrating cells, and the y-axis represents the different datasets. ‘TCGA_metastatic’ refers to metastatic TCGA-SKCM patients. The color of the lattice represents the correlation coefficient. Significant points are labeled using black diamonds. Fig. S7. Violin plots showing expression of the 31 genes used to calculate PScore corresponding to Figure 5B-E. (A) GSE115978, (B) GSE72056. The x-axis represents the 31 PRGs, and the y-axis represents the different cell subpopulations. The colors of the violin plot reflect cell types that are consistent with Figure 5B-E. Fig. S8. Spatial expression of the 31 genes used to calculate PScore corresponding to Figure 5G-H. Blue, white and purple indicate the magnitudes of PRGs: blue, low; white, middle; purple, high. The corresponding cell annotations can be found in Figure 5F. These genes are sorted alphabetically. Fig. S9. Comparisons of the PScore model and other pyroptosis-related models. (A) Forest plot showing HR and confidence intervals of different methods in multiple datasets. The horizontal axis represents log10(HR), and the vertical axis represents different methods and datasets. (B) Bar chart showing the c-index of different methods in multiple datasets. The c-index performance of 11 methods, including ssGSEA using 31 protective PRGs (PScore) and 10 obtainable pyroptosis-related models, were compared across four datasets. The horizontal axis represents different methods, the vertical axis represents the c-index, and the colors of the bar graph represent different datasets. Fig. S10. Correlation between the number of input genes and the c-index. The correlation of gene input size for the 11 methods mentioned in Fig. S9 with concordance (c-index) across different datasets was analyzed. The x-axis represents the number of genes used in each method, and the y-axis represents the c-index.