Evidence map›Paper›PMID 30497369›Full record

ArticleBMC bioinformatics2018

SWIM tool application to expression data of glioblastoma stem-like cell lines, corresponding primary tumors and conventional glioma cell lines.

Giulia Fiscon, Federica Conte, Paola Paci

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.3field-weighted citation impact, top 16% 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

20 citing papers in PubMed, 38 citations in OpenAlex.

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  3. Integrating multi-omics data: Methods and applications in human complex diseases.Biotechnology reports (Amsterdam, Netherlands) · 2025
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  15. In silico drug repurposing in COVID-19: A network-based analysis.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021
    Article
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  17. Article
  18. The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes.International journal of molecular sciences · 2020
    Article
  19. Review
  20. 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

3 authors at 1 institution in 1 country.

Giulia FisconInstitute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185, Italy.
Federica ConteInstitute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185, Italy.
Paola PaciInstitute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185, Italy. paola.paci@iasi.cnr.it.
National Research Council · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is well-known that glioblastoma contains self-renewing, stem-like subpopulation with the ability to sustain tumor growth. These cells - called cancer stem-like cells - share certain phenotypic characteristics with untransformed stem cells and are resistant to many conventional cancer therapies, which might explain the limitations in curing human malignancies. Thus, the identification of genes controlling the differentiation of these stem-like cells is becoming a successful therapeutic strategy, owing to the promise of novel targets for treating malignancies.

methodsRecently, we developed SWIM, a software able to unveil a small pool of genes - called switch genes - critically associated with drastic changes in cell phenotype. Here, we applied SWIM to the expression profiling of glioblastoma stem-like cells and conventional glioma cell lines, in order to identify switch genes related to stem-like phenotype.

resultsSWIM identifies 171 switch genes that are all down-regulated in glioblastoma stem-like cells. This list encompasses genes like CAV1, COL5A1, COL6A3, FLNB, HMMR, ITGA3, ITGA5, MET, SDC1, THBS1, and VEGFC, involved in "ECM-receptor interaction" and "focal adhesion" pathways. The inhibition of switch genes highly correlates with the activation of genes related to neural development and differentiation, such as the 4-core OLIG2, POU3F2, SALL2, SOX2, whose induction has been shown to be sufficient to reprogram differentiated glioblastoma into stem-like cells. Among switch genes, the transcription factor FOSL1 appears as the brightest star since: it is down-regulated in stem-like cells; it highly negatively correlates with the 4-core genes that are all up-regulated in stem-like cells; the promoter regions of the 4-core genes harbor a consensus binding motif for FOSL1.

conclusionsWe suggest that the inhibition of switch genes in stem-like cells could induce the deregulation of cell communication pathways, contributing to neoplastic progression and tumor invasiveness. Conversely, their activation could restore the physiological equilibrium between cell adhesion and migration, hampering the progression of cancer. Moreover, we posit FOSL1 as promising candidate to orchestrate the differentiation of cancer stem-like cells by repressing the 4-core genes' expression, which severely halts cancer growth and might affect the therapeutic outcome. We suggest FOSL1 as novel putative therapeutic and prognostic biomarker, worthy of further investigation.

Indexed as

Gene Expression Regulation, NeoplasticSoftwareAdultBrain NeoplasmsCell Line, TumorCluster AnalysisGene Expression ProfilingGene Regulatory NetworksGenes, NeoplasmGenes, SwitchGlioblastomaGliomaHumansMicroRNAsNeoplastic Stem CellsSurvival AnalysisMicroRNAsBioinformaticsCancer stem cellGlioblastomaGraph theory

Identifiers

PMID30497369
PMCPMC6266956
OpenAlexW2902764977

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.