Additional file 7 of Multi-omics characterization and validation of invasiveness-related molecular features across multiple cancer types

Additional file 7: Figure S2. (A) The prognostic value of the GSVA score based on other three invasiveness gene signature in LUAD patients. (B) Heatmaps exhibit the distribution of expression level of the 24 invasiveness-signature-genes in patients from high- and low-invasiveness score group in 30 cancer types. Figure S3. (A) The distribution of tumor stage in high- and low-invasiveness score group in different cancer types. Only cancers with reliable and available stage information were displayed here. (B) The circos plot exhibits invasiveness-related metabolites in BRCA patients. The length of each arc indicates the Spearman correlation between the invasiveness score and a series of metabolites. Two positively-correlated metabolites with the highest r were displayed in the plot. Data S1. Gene signatures mentioned in the study for GSVA/ssGSEA analysis. Data S2. Invasiveness score of the 9672 patients from 33 cancer types in TCGA. Data S3. Invasiveness-associated molecular features across 30 cancer types (high-invasiveness vs. low-invasiveness). Data S4. The spearman correlation of imputed drug response (GDSC) and invasiveness-related gene expression level. Data S5. Enriched invasiveness-related GO biological process across 30 cancer types. Data S6. Invasiveness related differentially expressed and methylated genes across 30 cancer types. Data S7. The targeting relationship and fold change (high-invasiveness vs. low-invasiveness) of invasiveness-related miRNA and genes across 30 cancer types. Data S8. Correlation between drug response data and the expression level of gene or miRNA and the targeting relationship between miRNA and genes in ESCA (up-regulated genes). Data S9. Correlation between drug response data and the expression level of gene or miRNA and the targeting relationship between miRNA and genes in ESCA (down-regulated genes).