Evidence map›Paper›PMID 23161554›Full record

ArticleProteomics. Clinical applications2013

SILAC-based quantitative proteomic analysis of gastric cancer secretome.

Arivusudar Marimuthu, Yashwanth Subbannayya, Nandini A Sahasrabuddhe, Lavanya Balakrishnan, Nazia Syed, Nirujogi Raja Sekhar, Teesta V Katte, Sneha M Pinto, Srinivas M Srikanth, Praveen Kumar and 15 more

Open access · greenAbstract read
In one paragraph

Article in Proteomics. Clinical applications, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed, 68 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Circulating Proteins as Diagnostic Markers in Gastric Cancer.International journal of molecular sciences · 2023
    Review
  10. The evolving landscape of PCSK9 inhibition in cancer.European journal of pharmacology · 2023
    Review
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Review
  19. 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

25 authors at 11 institutions in 3 countries.

Arivusudar MarimuthuInstitute of Bioinformatics, International Technology Park, Bangalore, India.
Yashwanth Subbannayya
Nandini A Sahasrabuddhe
Lavanya Balakrishnan
Nazia Syed
Nirujogi Raja Sekhar
Teesta V Katte
Sneha M Pinto
Srinivas M Srikanth
Praveen Kumar
Harsh Pawar
Manoj K Kashyap
Jagadeesha Maharudraiah
Hassan Ashktorab
Duane T Smoot
Girija Ramaswamy
Rekha V Kumar
Yulan Cheng
Stephen J Meltzer
Juan Carlos Roa
Raghothama Chaerkady
T S Keshava Prasad
H C Harsha
Aditi Chatterjee
Akhilesh Pandey
Institute of Bioinformatics · INJohns Hopkins University · USPondicherry University · INRajiv Gandhi University of Health Sciences · INHoward University · USJohns Hopkins Medicine · USKidwai Memorial Institute of Oncology · INKuvempu University · INManipal Academy of Higher Education · INMeharry Medical College · USUniversidad de La Frontera · CL

Funding

PROTEOMICS CORE FACILITYG12MD007597 · NIMHD · HOWARD UNIVERSITY · PI SOUTHERLAND, WILLIAM M. · 2012 to 2018
$14.8M
Proteome Characterization Center: A Genoproteomics Pipeline for Cance ResearchU24CA160036 · NCI · JOHNS HOPKINS UNIVERSITY · PI CHAN, DANIEL WANYUI, ZHANG, HUI · 2011 to 2016
$10.7M
NCI NIH HHS U24 CA160036NCI NIH HHS U24CA160036NIMHD NIH HHS G12 MD007597NIMHD NIH HHS G12MD007597Wellcome Trust
6 · The paper itself

Abstract

purposeGastric cancer is a commonly occurring cancer in Asia and one of the leading causes of cancer deaths. However, there is no reliable blood-based screening test for this cancer. Identifying proteins secreted from tumor cells could lead to the discovery of clinically useful biomarkers for early detection of gastric cancer. EXPERIMENTAL

designA SILAC-based quantitative proteomic approach was employed to identify secreted proteins that were differentially expressed between neoplastic and non-neoplastic gastric epithelial cells. Proteins from the secretome were subjected to SDS-PAGE and SCX-based fractionation, followed by mass spectrometric analysis on an LTQ-Orbitrap Velos mass spectrometer. Immunohistochemical labeling was employed to validate a subset of candidates using tissue microarrays.

resultsWe identified 2205 proteins in the gastric cancer secretome of which 263 proteins were overexpressed greater than fourfold in gastric cancer-derived cell lines as compared to non-neoplastic gastric epithelial cells. Three candidate proteins, proprotein convertase subtilisin/kexin type 9 (PCSK9), lectin mannose binding 2 (LMAN2), and PDGFA-associated protein 1 (PDAP1) were validated by immunohistochemical labeling. CONCLUSIONS AND CLINICAL RELEVANCE: We report here the largest cancer secretome described to date. The novel biomarkers identified in the current study are excellent candidates for further testing as early detection biomarkers for gastric adenocarcinoma.

Indexed as

ProteomicsAdenocarcinomaAmino AcidsBiomarkers, TumorCell Line, TumorChromatography, High Pressure LiquidComputational BiologyElectrophoresis, Polyacrylamide GelHumansImmunohistochemistryIntercellular Signaling Peptides and ProteinsIsotope LabelingMannose-Binding LectinsMass SpectrometryMembrane Transport ProteinsProprotein Convertase 9Amino AcidsBiomarkers, TumorIntercellular Signaling Peptides and ProteinsLMAN2 protein, humanMannose-Binding LectinsMembrane Transport ProteinsPCSK9 protein, humanPDAP1 protein, humanProprotein Convertase 9Proprotein ConvertasesProteinsSerine Endopeptidases

Identifiers

PMID23161554
PMCPMC3804263
OpenAlexW1986312526

What OpenQuestion holds

Textmetadata
LicenceTDM
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.