ArticleBMC bioinformatics2021
Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
- Acquired Genetic Variants, Not Tumor Mutation Burden, Drive Resistance to Immunotherapy in Hepatocellular Carcinoma.Liver cancer · 2026Article
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.Bioinformatics (Oxford, England) · 2025Article
- A gene set enrichment analysis for cancer hallmarks.Journal of pharmaceutical analysis · 2025Article
- Expression and prognosis of NR3C1 in uterine corpus endometrial carcinoma based on multiple datasets.Discover oncology · 2025Article
- Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.Nature cancer · 2025Article
- Precision and efficacy of RNA-guided DNA integration in high-expressing muscle loci.Molecular therapy. Nucleic acids · 2024Article
- Hallmarks of cancer in patients with heart failure: data from BIOSTAT-CHF.Cardio-oncology (London, England) · 2024Article
- Malignant features of minipig melanomas prior to spontaneous regression.Scientific reports · 2024Article
- Article
- Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies.BMC chemistry · 2024Article
- Cancer drug sensitivity prediction from routine histology images.NPJ precision oncology · 2024Article
- Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment.Frontiers in toxicology · 2024Article
- Evolvability of cancer-associated genes under APOBEC3A/B selection.bioRxiv : the preprint server for biology · 2023Article
- A Multi-Omics Approach Revealed Common Dysregulated Pathways in Type One and Type Two Endometrial Cancers.International journal of molecular sciences · 2023Article
- A functional analysis of omic network embedding spaces reveals key altered functions in cancer.Bioinformatics (Oxford, England) · 2023Article
- Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts.Scientific reports · 2022Article
- Identification of novel key regulatory lncRNAs in gastric adenocarcinoma.BMC genomics · 2022Review
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus.
resultsHere we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis.
conclusionsReaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.
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