Evidence map›Paper›PMID 37644431›Full record

ArticleBMC cancer2023

A co-formulation of interferons alpha2b and gamma distinctively targets cell cycle in the glioblastoma-derived cell line U-87MG.

Jamilet Miranda, Dania Vázquez-Blomquist, Ricardo Bringas, Jorge Fernandez-de-Cossio, Daniel Palenzuela, Lidia I Novoa, Iraldo Bello-Rivero

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 36% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Jamilet Miranda *Bioinformatics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba. jamilet.miranda@cigb.edu.cu.
Dania Vázquez-Blomquist *Pharmacogenomics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba. dania.vazquez@cigb.edu.cu.
Ricardo BringasBioinformatics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba.
Jorge Fernandez-de-CossioBioinformatics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba.
Daniel PalenzuelaPharmacogenomics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba.
Lidia I NovoaPharmacogenomics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba.
Iraldo Bello-RiveroClinical Assays Division, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba.
Centro de Ingeniería Genética y Biotecnología · CU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeberFERON is a co-formulation of α2b and γ interferons, based on their synergism, which has shown its clinical superiority over individual interferons in basal cell carcinomas. In glioblastoma (GBM), HeberFERON has displayed promising preclinical and clinical results. This led us to design a microarray experiment aimed at identifying the molecular mechanisms involved in the distinctive effect of HeberFERON compared to the individual interferons in U-87MG model.

methodsTranscriptional expression profiling including a control (untreated) and three groups receiving α2b-interferon, γ-interferon and HeberFERON was performed using an Illumina HT-12 microarray platform. Unsupervised methods for gene and sample grouping, identification of differentially expressed genes, functional enrichment and network analysis computational biology methods were applied to identify distinctive transcription patterns of HeberFERON. Validation of most representative genes was performed by qPCR. For the cell cycle analysis of cells treated with HeberFERON for 24 h, 48 and 72 h we used flow cytometry.

resultsThe three treatments show different behavior based on the gene expression profiles. The enrichment analysis identified several mitotic cell cycle related events, in particular from prometaphase to anaphase, which are exclusively targeted by HeberFERON. The FOXM1 transcription factor network that is involved in several cell cycle phases and is highly expressed in GBMs, is significantly down regulated. Flow cytometry experiments corroborated the action of HeberFERON on the cell cycle in a dose and time dependent manner with a clear cellular arrest as of 24 h post-treatment. Despite the fact that p53 was not down-regulated, several genes involved in its regulatory activity were functionally enriched. Network analysis also revealed a strong relationship of p53 with genes targeted by HeberFERON. We propose a mechanistic model to explain this distinctive action, based on the simultaneous activation of PKR and ATF3, p53 phosphorylation changes, as well as its reduced MDM2 mediated ubiquitination and export from the nucleus to the cytoplasm. PLK1, AURKB, BIRC5 and CCNB1 genes, all regulated by FOXM1, also play central roles in this model. These and other interactions could explain a G2/M arrest and the effect of HeberFERON on the proliferation of U-87MG.

conclusionsWe proposed molecular mechanisms underlying the distinctive behavior of HeberFERON compared to the treatments with the individual interferons in U-87MG model, where cell cycle related events were highly relevant.

Indexed as

GlioblastomaSkin NeoplasmsAnaphaseApoptosisCell Line, TumorG2 Phase Cell Cycle CheckpointsHumansInterferon-alphaInterferon-gammaInterferon-alphaInterferon-gammaAlpha interferonAURKBBIRC5(Survivin)CDC20Drug combinationFOXM1Gamma interferonGlioblastomaHeberFERONMitotic cell cyclep53PLK1STAT1U-87MG

Identifiers

PMID37644431
PMCPMC10463508
OpenAlexW4386251996

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.