Evidence map›Paper›PMID 19035623›Full record

ReviewMolecular diagnosis & therapy2008

Diagnostic and prognostic sarcoma signatures.

Elai Davicioni, Daniel H Wai, Michael J Anderson

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular diagnosis & therapy, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. The pharmacogenomics of osteosarcoma.The pharmacogenomics journal · 2017
    Review
  5. Targeted therapy for soft tissue sarcomas in adolescents and young adults.Adolescent health, medicine and therapeutics · 2017
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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.

Elai DavicioniGenome Diagnostics Inc., Pasadena, California, USA.
Daniel H Wai
Michael J Anderson

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sarcomas are a diverse group of childhood and adult tumors that arise from mesenchymal tissue. In contrast to epithelial tumors, most of which are defined by a specific organ system, sarcomas can arise virtually anywhere in the body, such that their characteristic histopathology and clinical presentation form the core diagnostic criteria.Precise identification by differential diagnosis is the first element of a successful treatment, since these tumors show wide variation in response to specific therapies and misdiagnosis can lead to suboptimal therapy. However, due to overlapping histopathologic features among the sarcomas, as well as the multiple subtypes or variants within a single histologic group, pathologists and clinicians are increasingly reliant on molecular diagnostic approaches to aid in the differential diagnosis. Gene expression profiling or microarray analysis is now being used to develop expression signatures that appear to be better than histological features or any single biomarker at discriminating tumor types, identifying clinical variants, and modeling complex tumor behavior.This review examines the current progress in identifying diagnostic and prognostic expression signatures for four sarcomas: rhabdomyosarcoma, Ewing's family of tumors, synovial sarcoma, and osteosarcoma. In this context, we discuss the current status and future potential for using expression signatures to improve tumor classification, outcome prediction, and therapeutic response in patients with these sarcomas.

Indexed as

Gene Expression ProfilingHumansNeoplasm MetastasisNeoplasm ProteinsPrognosisSarcomaNeoplasm Proteins

Identifiers

PMID19035623

What OpenQuestion holds

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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.