Supplementary material - A data science approach for the identification of molecular signatures of aggressive cancers.

Supplementary material - A data science approach for the identification of molecular signatures of aggressive cancers.

• Supplementary Table 1: List of R dependency packages.
• Supplementary Table 2: Clinical data for Kidney Renal Papillary samples
• Supplementary Table 3: Clinical data for Prostate samples
• Supplementary Table 4: Clinical data for Thyroid samples
• Supplementary Table 5: Clinical data for Liver samples
• Supplementary Table 6: Clinical data for Lung Squamous Cell samples
• Supplementary Table 7: Clinical data for Stomach samples
• Supplementary Table 8:  Gene members of each pathway listed from the literature and extracted from KEGG, Biocarta, and Reactome databases. 
• Supplementary Table 9: Gene members with their number of connections, frequency of overexpression, sample number, normalized counts, and normalized connections.
• Supplementary Table 10: Genes with their normalized connections per type of cancer and aggressiveness.
• Supplementary Table 11: Mean, Std. deviation, and variance of normalized counts from target target genes between H and L classes.
• Supplementary Table 12: Genes that are specific to each cancer classe.
• Supplementary Table 13: Sub interactome of proteins corresponding to the genes of Tables 3 and 4 (except H2AFZ and TMEFF2 ) as well as Figure 6.
• Supplementary Table 14: Kruskal-Wallis test by pathway.
• Supplementary Table 15: Wilcoxon test of cancer types per pathway.
• Supplementary Table 16: Percentage of contribution for up-regulated genes to the PCA’s first, second, and third components.
• Supplementary Table 17: Gene importance as defined by RFC.