Additional file 1 of Ensemble learning model for identifying the hallmark genes of NFκB/TNF signaling pathway in cancers
Creators: name: Su, Yin-Yuan
name: Liu, Yu-Ling
name: Huang, Hsuan-Cheng
name: Lin, Chen-Ching
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems 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: The association between the cancer prevalence and the percentage of NFκB/TNF hallmark genes. The cancer prevalence of a gene is the number of cancers that vote for the gene. The percentage on the y-axis is the proportion of NFκB/TNF hallmark genes under the corresponding cancer prevalence. The odds ratio is the ratio of the odds of the NFκB/TNF hallmark genes voted by the number of cancer types on the x-axis to the odds of the non-NFκB/TNF hallmark genes voted by the number of cancer types on the x-axis. Green and red circles show that the NFκB/TNF hallmark gene proportions under the corresponding cancer prevalence are significantly underrepresented and overrepresented, respectively. Grey circles represent insignificance. The significance is determined by Fisher’s exact test with a p-value &lt; 0.05. Figure S2: Enrichment analysis of the functional similarity between candidates from the conventional approach and core member genes. Figure S3: Enrichment score curve of cancer-dysregulated genes based on votes. Gene set enrichment analysis (GSEA) was conducted to test the enrichment of the cancer-dysregulated genes from the C6 gene set of MSigDB on genes' votes. The x-axis represents the voted genes arranged in descending order of their votes and average votes in each cancer type and pan-cancer model, respectively. The red dot indicates the maximum enrichment score (ES). The dashed line represents the maximum ES, while the dotted line represents the baseline of zero. The p-values and z-scores of the ES were assessed by 10,000 random sampling processes. Figure S4: Proportion of cancer-dysregulated (C6) genes across vote thresholds in each cancer and pan-cancer model. Figure S5: Enrichment score curve of cancer-dysregulated (C6) genes on correlation. Figure S6: Proportion of cancer-dysregulated (C6) genes across association score thresholds in each cancer and pan-cancer model. Figure S7: Enrichment analysis of BRCA subtypes. Figure S8: 3-year hazard ratios of genes in the functional module mononuclear cell differentiation for non-TNBC patients. Figure S9: Consistency of gene votes between ensemble learning model with 500 and 1000 member classifiers in 16 cancer types. Figure S10: Enrichment analysis of LUAD subtypes based on smoking history. Table S1. Count of gene categories in the ensemble learning models. Table S2: The biological processes with long descriptions in Figure 2. Table S3: The identified highly-voted functional modules activated/inactivated in TNBC patients.