Additional file 1 of Pancancer network analysis reveals key master regulators for cancer invasiveness
Creators: name: Jethalia, Mahesh
name: Jani, Siddhi P.
name: Ceccarelli, Michele
name: Mall, Raghvendra
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: Physiology
subject: Science Policy
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Additional file 1: Figure S1: Kaplan-Meier plot highlights the difference in survival between the INV High vs INV Low groups for the 10 cancers of interest. Here OV is Ovarian Cancer, LUAD is Lung Adenocarcinoma, STAD is Stomach Adenocarcinoma, BLCA is Bladder Urothelial Carcinoma, COAD is Colon Adenocarcinoma, KIRC is Kidney Renal Cell Carcinoma, MESO is Mesothelioma, PAAD is Pancreatic Adenocarcinoma, KIRP is Kidney Renal Papillary Cell Carcinoma and LGG is Low Grade Gliomas. Figure S2: Quantile-Normalized and log2 transformed gene expression profiles for BLCA tumor samples. Figure S3: Activity profiles of transcriptional regulators follow a normal distribution for a particular cancer. Figure S4: Here we highlight that when the normalized enrichment scores (NES) for TRs are positive then, these TRs have high positive activity in INV-H samples and high negative activity in INV-L samples. Thus, TRs with positive NES scores are more specific to the INV-H phenotype. Similarly, when the NES are negative for TRs then, these TRs have high positive activity in INV-L samples and high negative activity in INV-H samples. Thus, TRs with negative NES are more specific to the INV-L phenotype (p-value &lt; 1e-5). Figure S5: GO Terms including Biological Processes (b), Cellular Components (c ), Molecular Functions (m) which are significantly enriched when performing over-expression analysis of common MRs for INV-H and INV-L phenotype respectively. Figure S6: The top enriched pathways obtained by over-expression analysis for the top MRs peculiar to INV High phenotype are highlighted here. The pathways are clustered and color-coded according to their respective clusters. Figure S7: Heatmaps depicting the MR activity of the MRs specific to INV-H and INV-L phenotypes (based on the 10 prognostic cancers) and present in each of the 8 PRECOG validation datasets. Table S1: MRs common across all the 10 cancer types of interest and specific to INV-H phenotype (Mean FC &gt; 0). Table S2: List of master regulators (differentially activated TRs) common across 6 or more prognostic cancer subtypes. We showcase the cancers for which these MRs are differentially active. Table S3: List of 156 significant MRs common across the majority of the prognostic cancers and are ranked based on fold change (FC) between activities in INV-H vs activities in INV-L samples across all the 10 cancers using Wilcoxon rank-sum test. Table S4: List of 156 MRs specific to INV-L and INV-H phenotype and their activity profile for the set of 22 INV Neutral cancers. The significance (155 out of 156) of difference in activities in INV-H vs INV-L cancer samples is highlighted using Wilcoxon rank-sum test. Table S5: List of 156 MRs specific to INV-L and INV-H phenotype and their activity profile for the set of 8 PRECOG datasets. The significance (153 out of 156) of the difference in activities in INV-H vs INV-L cancer samples is highlighted using the Wilcoxon rank-sum test. Table S6: Differentially activated MRs (out of 156 MRs) in the PRECOG cancer datasets for BLCA, BRCA, COAD and GBM cancer types. Table S7: Differentially activated MRs (out of 156 MRs) in the PRECOG cancer datasets for HNSC, LUAD, OV and SKCM cancer types.