ReviewGenes2019
The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook.
Review in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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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.
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Who cites it
23 citing papers in PubMed.
- Why are Some Tissues More Vulnerable? Revisiting Tissue Specificity in Hereditary Cancer Syndromes.Molecular diagnosis & therapy · 2026Review
- Loss of long-range co-expression is a common feature in cancer.NPJ systems biology and applications · 2026Article
- Pseudogene Coexpression Networks Reveal a Robust Prognostic Signature for Pediatric B-ALL Survival.Cancer research communications · 2026Article
- Comprehensive profiling of RNA modification-related genes identifies RNA mExperimental & molecular medicine · 2025Article
- Identification and verification of international neuroblastoma staging system (INSS) stage-related genes as potential biomarkers for neuroblastoma prognostic models.Frontiers in cell and developmental biology · 2025Article
- Potential Drug Synergy Through the ERBB2 Pathway in HER2+ Breast Tumors.International journal of molecular sciences · 2024Article
- Review
- Analysis of VEGF, IGF1/2 and the Long Noncoding RNA (lncRNA) H19 Expression in Polish Women with Endometriosis.International journal of molecular sciences · 2024Article
- Onco-Breastomics: An Eco-Evo-Devo Holistic Approach.International journal of molecular sciences · 2024Review
- Gene co-expression networks reveal sex-biased differences in musculoskeletal ageing.Frontiers in aging · 2024Article
- The network structure of hematopoietic cancers.Scientific reports · 2023Article
- Single-cell gene regulatory network prediction by explainable AI.Nucleic acids research · 2023Article
- Molecular mechanisms of multi-omic regulation in breast cancer.Frontiers in oncology · 2023Review
- The Role of Copy Number Variants in Gene Co-Expression Patterns for Luminal B Breast Tumors.Frontiers in genetics · 2022Article
- Multi-Omics Model Applied to Cancer Genetics.International journal of molecular sciences · 2021Review
- Modularity in Biological Networks.Frontiers in genetics · 2021Review
- Article
- Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations.Frontiers in genetics · 2021Article
- Strategies for Functional Interrogation of Big Cancer Data Using Drosophila Cancer Models.International journal of molecular sciences · 2020Review
- On a Class of Tensor Markov Fields.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.
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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.