Evidence map›Paper›PMID 30383835›Full record

ArticlePloS one2018

Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer.

Kanthida Kusonmano, Mari K Halle, Elisabeth Wik, Erling A Hoivik, Camilla Krakstad, Karen K Mauland, Ingvild L Tangen, Anna Berg, Henrica M J Werner, Jone Trovik and 5 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.5field-weighted citation impact, top 19% of its field
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

9 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

15 authors at 2 institutions in 2 countries.

Kanthida KusonmanoComputational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.ORCID 0000-0002-6532-9144
Mari K HalleDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Elisabeth WikCentre for Cancer Biomarkers, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
Erling A HoivikDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Camilla KrakstadDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Karen K MaulandDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Ingvild L TangenDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Anna BergDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Henrica M J WernerDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Jone TrovikDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Anne M ØyanCentre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
Karl-Henning KallandCentre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
Inge JonassenComputational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
Helga B SalvesenDepartment of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Kjell PetersenComputational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.ORCID 0000-0001-5201-0268
Haukeland University Hospital · NOUniversity of Bergen · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have identified nine highly connected and differentially expressed gene subnetworks between aggressive primary tumors and metastatic lesions in endometrial carcinomas. We implemented a novel pipeline combining gene set and network approaches, which here allows integration of protein-protein interactions and gene expression data. The resulting subnetworks are significantly associated with disease progression across tumor stages from complex atypical hyperplasia, primary tumors to metastatic lesions. The nine subnetworks include genes related to metastasizing features such as epithelial-mesenchymal transition (EMT), hypoxia and cell proliferation. TCF4 and TWIST2 were found as central genes in the subnetwork related to EMT. Two of the identified subnetworks display statistically significant association to patient survival, which were further supported by an independent validation in the data from The Cancer Genome Atlas data collection. The first subnetwork contains genes related to cell proliferation and cell cycle, while the second contains genes involved in hypoxia such as HIF1A and EGLN3. Our findings provide a promising context to elucidate the biological mechanisms of metastasis, suggest potential prognostic markers and further identify therapeutic targets. The pipeline R source code is freely available, including permutation tests to assess statistical significance of the identified subnetworks.

Indexed as

CarcinomaCell ProliferationComputational BiologyEndometrial NeoplasmsEpithelial-Mesenchymal TransitionFemaleGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansHypoxiaModels, StatisticalNeoplasm MetastasisRNASoftwareRNA

Identifiers

PMID30383835
PMCPMC6211718
OpenAlexW2899023679

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.