Evidence map›Paper›PMID 27842508›Full record

ArticleBMC cancer2016

Global DNA methylation profiling uncovers distinct methylation patterns of protocadherin alpha4 in metastatic and non-metastatic rhabdomyosarcoma.

L Tombolan, E Poli, P Martini, A Zin, C Millino, B Pacchioni, B Celegato, G Bisogno, C Romualdi, A Rosolen and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

11 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
  2. Review
  3. The DNA methylation landscape of musculoskeletal sarcomas.Exploration of targeted anti-tumor therapy · 2025
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Protocadherins at the Crossroad of Signaling Pathways.Frontiers in molecular neuroscience · 2020
    Review
  10. Article
  11. 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

11 authors at 2 institutions in 1 country.

L TombolanDepartment of Biology, University of Padova, Padova, Italy. lucia.tombolan@unipd.it.ORCID 0000-0002-8208-6244
E PoliDepartment of Women's and Children's Health, University of Padova, Padova, Italy.
P MartiniDepartment of Biology, University of Padova, Padova, Italy.
A ZinInstitute of Pediatric Research, IRP, Padova, Italy.
C MillinoC.R.I.B.I.-Biotechnology Centre, University of Padova, Padova, Italy.
B PacchioniC.R.I.B.I.-Biotechnology Centre, University of Padova, Padova, Italy.
B CelegatoC.R.I.B.I.-Biotechnology Centre, University of Padova, Padova, Italy.
G BisognoDepartment of Women's and Children's Health, Hematology Oncology Division, University of Padova, Padova, Italy.
C RomualdiDepartment of Biology, University of Padova, Padova, Italy.
A RosolenDepartment of Women's and Children's Health, Hematology Oncology Division, University of Padova, Padova, Italy.
G LanfranchiDepartment of Biology, University of Padova, Padova, Italy. gerolamo.lanfranchi@unipd.it.
University of Padua · ITCittà della Speranza Foundation · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRhabdomyosarcoma (RMS), which can be classified as embryonal RMS (ERMS) and alveolar RMS (ARMS), represents the most frequent soft tissue sarcoma in the pediatric population; the latter shows greater aggressiveness and metastatic potential with respect to the former. Epigenetic alterations in cancer include DNA methylation changes and histone modifications that influence overall gene expression patterns. Different tumor subtypes are characterized by distinct methylation signatures that could facilitate early disease detection and greater prognostic accuracy.

methodsA genome-wide approach was used to examine methylation patterns associated with different prognoses, and DNA methylome analysis was carried out using the Agilent Human DNA Methylation platform. The results were validated using bisulfite sequencing and 5-aza-2'deoxycytidine treatment in RMS cell lines. Some in vitro functional studies were also performed to explore the involvement of a target gene in RMS tumor cells.

resultsIn accordance with the Intergroup Rhabdomyosarcoma Study (IRS) grouping, study results showed that distinct methylation patterns distinguish RMS subgroups and that a cluster of protocadherin genes are hypermethylated in metastatic RMS. Among these, PCDHA4, whose expression was decreased by DNA methylation, emerged as a down-regulated gene in the metastatic samples. As PCDHA4-silenced cells have a significantly higher cell proliferation rate paralleled by higher cell invasiveness, PCDHA4 seems to behave as a tumor suppressor in metastatic RMS.

conclusionStudy results demonstrated that DNA methylation patterns distinguish between metastatic and non-metastatic RMS and suggest that epigenetic regulation of specific genes could represent a novel therapeutic target that could enhance the efficiency of RMS treatments.

Indexed as

DNA MethylationGene Expression Regulation, NeoplasticAzacitidineBiopsyCell Adhesion Molecules, NeuronalCell Line, TumorCluster AnalysisCytidine TriphosphateEpigenesis, GeneticGene Expression ProfilingGenome-Wide Association StudyHumansHydroxamic AcidsNeoplasm MetastasisNeuropeptidesPromoter Regions, Genetic5-aza-2'-deoxycytidine-5'-triphosphateAzacitidineCell Adhesion Molecules, NeuronalCytidine TriphosphateHydroxamic AcidsNeuropeptidesPCDHA4 protein, humanProtocadherinsReceptors, Cell Surfacetrichostatin ADNA methylationEpigeneticsMicroarrayPCDHA4Rhabdomyosarcoma

Identifiers

PMID27842508
PMCPMC5109816
OpenAlexW2553009047

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.