ArticleFrontiers in genetics2019
Identification of Cancer Hallmarks Based on the Gene Co-expression Networks of Seven Cancers.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- A gene set enrichment analysis for cancer hallmarks.Journal of pharmaceutical analysis · 2025Article
- Multiscale network modeling reveals the gene regulatory landscape driving cancer prognosis in 32 cancer types.Genome research · 2023Article
- Review
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins.Journal of personalized medicine · 2022Article
- Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.Scientific reports · 2020Article
- Cancer cell line-specific protein profiles in extracellular vesicles identified by proteomics.PloS one · 2020Article
- Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in cancer hallmarks are likely to be cancer drivers. However, there is no appropriate method in the current literature for identifying genetic cancer hallmarks, especially considering the interrelationships among the genes. Here, we hypothesized that "dense clusters" (or "communities") in the gene co-expression networks of cancer patients may represent functional units regarding cancer formation and progression, and the communities present in the co-expression networks of multiple types of cancer may be cancer hallmarks. Consequently, we mined the conserved communities in the gene co-expression networks of seven cancers in order to identify candidate hallmarks. Functional annotation of the communities showed that they were mainly related to immune response, the cell cycle and the biological processes that maintain basic cellular functions. Survival analysis using the genes involved in the conserved communities verified that two of these hallmarks could predict the survival risks of cancer patients in multiple types of cancer. Furthermore, the genes involved in these hallmarks, one of which was related to the cell cycle, could be useful in screening for cancer drugs.
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