ArticleFrontiers in genetics2014
The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks.
Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 61 citations in OpenAlex.
- Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer.Scientific reports · 2026Article
- The adverse outcome pathway for breast cancer: a knowledge management framework bridging biomedicine and toxicology.Discover oncology · 2023Review
- Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment.International journal of environmental research and public health · 2023Review
- Modular and mechanistic changes across stages of colorectal cancer.BMC cancer · 2022Article
- Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer.Frontiers in oncology · 2022Article
- Bioinformatics and network-based screening and discovery of potential molecular targets and small molecular drugs for breast cancer.Frontiers in pharmacology · 2022Article
- Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations.BMC bioinformatics · 2021Article
- Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study.NPJ systems biology and applications · 2021Article
- Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer.Frontiers in genetics · 2021Article
- Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations.Frontiers in genetics · 2021Article
- Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance.Scientific reports · 2020Article
- Constructing cancer patient-specific and group-specific gene networks with multi-omics data.BMC medical genomics · 2020Article
- Network inference in systems biology: recent developments, challenges, and applications.Current opinion in biotechnology · 2020Review
- Finding prognostic gene pairs for cancer from patient-specific gene networks.BMC medical genomics · 2019Article
- Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data.Current genomics · 2019Article
- Leveraging User-Friendly Network Approaches to Extract Knowledge From High-ThroughputFrontiers in genetics · 2019Review
- rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks.Nucleic acids research · 2018Article
- Harnessing the biological complexity of Big Data from LINCS gene expression signatures.PloS one · 2018Article
- Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods.Frontiers in genetics · 2018Article
- Exploring novel key regulators in breast cancer network.PloS one · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.