Evidence map›Paper›PMID 28209158›Full record

ArticleBMC systems biology2017

Gene regulatory network underlying the immortalization of epithelial cells.

Luis Fernando Méndez-López, Jose Davila-Velderrain, Elisa Domínguez-Hüttinger, Christian Enríquez-Olguín, Juan Carlos Martínez-García, Elena R Alvarez-Buylla

Abstract read
In one paragraph

Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. A computational approach for perturbation-induced EMT transitions.NPJ systems biology and applications · 2025
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Revisiting Epithelial Carcinogenesis.International journal of molecular sciences · 2022
    Article
  7. Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022
    Article
  8. Article
  9. Article
  10. In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer.International journal of molecular sciences · 2021
    Review
  11. Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Luis Fernando Méndez-LópezCentro de Investigación y Desarrollo en Ciencias de la Salud (CIDICS), Universidad Autonoma de Nuevo Leon, A. P. 14-740, México, 07300, D.F, México.
Jose Davila-VelderrainCentro de Ciencias de la Complejidad, UNAM, Cd. Universitaria, México, 04510, D.F, México.
Elisa Domínguez-HüttingerInstituto de Ecología, UNAM, Cd. Universitaria, México, 04510, D.F, México.
Christian Enríquez-OlguínDepartamento de Control Automático, Cinvestav-IPN, A. P. 14-740, México, 07300, D.F, México.
Juan Carlos Martínez-GarcíaDepartamento de Control Automático, Cinvestav-IPN, A. P. 14-740, México, 07300, D.F, México. juancarlos_martinez-garcia@conciliencia.org.
Elena R Alvarez-BuyllaInstituto de Ecología, UNAM, Cd. Universitaria, México, 04510, D.F, México. eabuylla@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTumorigenic transformation of human epithelial cells in vitro has been described experimentally as the potential result of spontaneous immortalization. This process is characterized by a series of cell-state transitions, in which normal epithelial cells acquire first a senescent state which is later surpassed to attain a mesenchymal stem-like phenotype with a potentially tumorigenic behavior. In this paper we aim to provide a system-level mechanistic explanation to the emergence of these cell types, and to the time-ordered transition patterns that are common to neoplasias of epithelial origin. To this end, we first integrate published functional and well-curated molecular data of the components and interactions that have been found to be involved in such cell states and transitions into a network of 41 molecular components. We then reduce this initial network by removing simple mediators (i.e., linear pathways), and formalize the resulting regulatory core into logical rules that govern the dynamics of each of the network components as a function of the states of its regulators.

resultsComputational dynamic analysis shows that our proposed Gene Regulatory Network model recovers exactly three attractors, each of them defined by a specific gene expression profile that corresponds to the epithelial, senescent, and mesenchymal stem-like cellular phenotypes, respectively. We show that although a mesenchymal stem-like state can be attained even under unperturbed physiological conditions, the likelihood of converging to this state is increased when pro-inflammatory conditions are simulated, providing a systems-level mechanistic explanation for the carcinogenic role of chronic inflammatory conditions observed in the clinic. We also found that the regulatory core yields an epigenetic landscape that restricts temporal patterns of progression between the steady states, such that recovered patterns resemble the time-ordered transitions observed during the spontaneous immortalization of epithelial cells, both in vivo and in vitro.

conclusionOur study strongly suggests that the in vitro tumorigenic transformation of epithelial cells, which strongly correlates with the patterns observed during the pathological progression of epithelial carcinogenesis in vivo, emerges from underlying regulatory networks involved in epithelial trans-differentiation during development.

Indexed as

Gene Regulatory NetworksModels, GeneticCell DifferentiationCell Transformation, NeoplasticCellular SenescenceEpigenesis, GeneticEpithelial CellsMesenchymal Stem CellsMutationPhenotypeBoolean modelsCarcinomasEpigenetic landscapeGene regulatory networksPhenotypic attractors

Identifiers

PMID28209158
PMCPMC5314717

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.