Evidence map›Paper›PMID 20934395›Full record

ReviewMolecular oncology2010

Cancer secretomics reveal pathophysiological pathways in cancer molecular oncology.

George S Karagiannis, Maria P Pavlou, Eleftherios P Diamandis

Abstract readReview
In one paragraph

Review in Molecular oncology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.

0numbers the graph read from it
0cells of the map it votes in
81citing 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

81 citing papers in PubMed.

  1. Article
  2. Review
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  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Liquid biopsy to personalize treatment for metastatic prostate cancer.American journal of translational research · 2024
    Review
  14. Article
  15. Article
  16. Cancer Cell-Derived PDGFB Stimulates mTORC1 Activation in Renal Carcinoma.International journal of molecular sciences · 2023
    Article
  17. Review
  18. Article
  19. Article
  20. Review

21 more citing papers are in PubMed but not listed here.

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.

George S KaragiannisDepartment of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, Canada.
Maria P Pavlou
Eleftherios P Diamandis

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emerging proteomic tools and mass spectrometry play pivotal roles in protein identification, quantification and characterization, even in complex biological samples. The cancer secretome, namely the whole collection of proteins secreted by cancer cells through various secretory pathways, has only recently been shown to have significant potential for diverse applications in oncoproteomics. For example, secreted proteins might represent putative tumor biomarkers or therapeutic targets for various types of cancer. Consequently, many proteomic strategies for secretome analysis have been extensively deployed over the last few years. These efforts generated a large amount of information awaiting deeper mining, better understanding and careful interpretation. Distinct sub-fields, such as degradomics, exosome proteomics and tumor-host cell interactions have been developed, in an attempt to provide certain answers to partially elucidated mechanisms of cancer pathobiology. In this review, advances, concerns and challenges in the field of secretome analysis as well as possible clinical applications are discussed.

Indexed as

NeoplasmsBiomarkers, TumorComputational BiologyDatabases, ProteinDisease ProgressionHumansInflammationNeoplasm MetastasisNeoplasm ProteinsProteomicsBiomarkers, TumorNeoplasm Proteins

Identifiers

PMID20934395
PMCPMC5527923

What OpenQuestion holds

Textmetadata
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.