Evidence map›Paper›PMID 19863813›Full record

ArticleCell division2009

Proteomics, pathway array and signaling network-based medicine in cancer.

David Y Zhang, Fei Ye, Ling Gao, Xiaoliang Liu, Xin Zhao, Yufang Che, Hongxia Wang, Libo Wang, Josephine Wu, Dong Song and 6 more

Open access · goldAbstract read
In one paragraph

Article in Cell division, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

30 citing papers in PubMed, 69 citations in OpenAlex.

  1. Trial
  2. Review
  3. Review
  4. Review
  5. High-throughput proteomics: a methodological mini-review.Laboratory investigation; a journal of technical methods and pathology · 2022
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Proteomics: A new perspective for cancer.Advanced biomedical research · 2016
    Review
  15. Article
  16. Article
  17. Article
  18. Review
  19. Pathway-based analysis of breast cancer.American journal of translational research · 2014
    Article
  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

16 authors at 6 institutions in 2 countries.

David Y ZhangDepartment of Pathology, Mount Sinai School of Medicine, New York, NY, USA. david.zhang@mssm.edu.
Fei Ye
Ling Gao
Xiaoliang Liu
Xin Zhao
Yufang Che
Hongxia Wang
Libo Wang
Josephine Wu
Dong Song
Wei Liu
Hong Xu
Bo Jiang
Weijia Zhang
Jinhua Wang
Peng Lee
First Hospital of Jilin University · CNIcahn School of Medicine at Mount Sinai · USNanfang Hospital · CNNew York University · USJilin University · CNRenji Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures can be used for cancer diagnosis, prognosis, and personalized treatment. During the past several decades, the available technology to study signaling networks has significantly evolved to include such platforms as genomic microarray (expression array, SNP array, CGH array, etc.) and proteomic analysis, which globally assesses genetic, epigenetic, and proteomic alterations in cancer. In this review, we compared Pathway Array analysis with other proteomic approaches in analyzing protein network involved in cancer and its utility serving as cancer biomarkers in diagnosis, prognosis and therapeutic target identification. With the advent of bioinformatics, constructing high complexity signaling networks is possible. As the use of signaling network-based cancer diagnosis, prognosis and treatment is anticipated in the near future, medical and scientific communities should be prepared to apply these techniques to further enhance personalized medicine.

Identifiers

PMID19863813
PMCPMC2780394
OpenAlexW2141025459

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.