Prognostic and Hallmark Gene Networks

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 ...

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.

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