Hallmarks of cancer

This project stored supplementary files that we didn't include in the paper ' Exploring the Evolution of the Gene Ontology and itsImpact on Enrichment Analysis'

Data and experimental methods to support the work in the following paper:

Establishing Consensus Annotation for the Hallmarks of Cancer, 2020, Yi Chen, F.J.Verbeek and K.Wolstencroft, in submission

The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. In this investigation we compare cancer hallmark mapping strategies from different studies, based on Gene Ontology and biological pathway annotation. By analysing the semantic ...

The hallmarks mapping schemes under comparison were developed over the period of 7 years and therefore were developed using different versions of the Gene Ontology and associated annotation. Understanding which differences between mapping schemes were the result of topological or annotation changes to GO could therefore help to further refine consensus and make results and conclusions more comparable between studies.

Multiple studies have devised mapping schemes to associate cancer hallmarks with Gene Ontology terms and biological pathway. This study compares the similarities and differences between them, in order to establish consensus knowledge.

This study examines how different hallmark gene datasets intersect with prognostic cancer genes

A Weighted Gene Co-Expression Network Analysis (WGCNA) of breast cancer prognostic genes (derived from transcriptome data from the TCGA Genomics Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/)), and cancer hallmark genes.

A Jaccard Index of the overlap between prognostic and hallmark genes for 17 cancer types across different mapping schemes. The impact of selecting different mapping schemes was assessed by pairwise comparisons where there were 5 or more shared genes.

this assay include the hub genes of modules from different mapping schemes with highly functional similarities.

The table shows the number of valid terms, annotations and edges for 3 aspects from Jan 2015 to Dec 2021

The table shows the number of terms, annotations and edges from Jan 2015 to Dec 2021.

The table shows the top 20 nodes with the highest degree centrality score in BP, MF and CC

Downloaded from UniprotKB

1222 patients

For each module, GSEA analysis was conducted by using web-tool g:profiler and only biological process (BP) were retained. Parameters for GSEA are default.

Calculated by using R package GOSemSim.

Modules were identified by WGCNA. hub genes of each modules were identified based on intramodular degree.

Including 91 genes and 1222 Breast cancer patient This is an input data for WGCNA

Including 289 genes and 1222 Breast cancer patient This is an input data for WGCNA

Including 277 genes and 1222 Breast cancer patients. This is an input data for WGCNA.

Including 294 genes and 1222 Breast cancer Patient. This is an input data for WGCNA

Consensus between selected mapping methods.

Additional file 2

Additional File 1

Hallmark gene sets belong to different mapping schemes.

derived from Ulhen's research published in 2017.