uu.seUppsala University Publications
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.ORCID iD: 0000-0003-2103-4253
Univ Siena, Dept Gen Surg & Surg Oncol, Siena, Italy;Gdansk Med Univ, Dept Surg Oncol, Gdansk, Poland.
Ariana Pharmaceut, Paris, France.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-6074-7462
Show others and affiliations
2019 (English)In: EBioMedicine, E-ISSN 2352-3964, Vol. 44, p. 322-333Article in journal (Refereed) Published
Abstract [en]

Background: Gastric cancer (GC) is the third leading cause of cancer death. Early detection is a key factor to reduce its mortality. Methods: We retrospectively collected pre- and postoperative serum samples as well as tumour tissues and adjacent normal tissues from 100 GC patients. Serum samples from non-cancerous patients were served as controls (n = 50). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure levels of over 300 proteins. Alteration of each protein was analysed by univariate analysis. Elastic-net logistic regression was performed to select serum proteins into the diagnostic model. Findings: We identified 19 serum proteins (CEACAM5, CA9, MSLN, CCL20, SCF, TGF-alpha, MMP-1, MMP-10, IGF-1, CDCPI, PPIA, DDAH-1, HMOX-1, FLI1, IL-7, ZBTB-17, APBB1IP, KAZALD-1, and ADAMTS-15) that together distinguish GC cases from controls with a diagnostic sensitivity of 93%, specificity of 100%, and area under receiver operating characteristic curve (AUC) of 0.99 (95% CI: 0.98-1). Moreover, the 19-serum protein signature pro-vided an increased diagnostic capacity in patients at TNMI-II stage (sensitivity 89%, specificity 100%, AUC 0.99) and in patients with high miaosatellite instability (MSI) (91%. 98%, and 0.99) compared to individual proteins. These promising results will inspire a large-scale independent cohort study to be pursued for validating the proposed protein signature. Interpretation: Based on targeted proteomics and elastic-net logistic regression, we identified a 19-serum protein signature which could contribute to clinical GC diagnosis, especially for patients at early stage and those with high MSI. (C) 2019 The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 44, p. 322-333
Keywords [en]
Gastric cancer, Diagnosis, Biomarker, PEA, Proteomics
National Category
Immunology in the medical area
Identifiers
URN: urn:nbn:se:uu:diva-390592DOI: 10.1016/j.ebiom.2019.05.044ISI: 000472768900038PubMedID: 31151932OAI: oai:DiVA.org:uu-390592DiVA, id: diva2:1342364
Funder
EU, Horizon 2020, 316929Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved

Open Access in DiVA

fulltext(1285 kB)4 downloads
File information
File name FULLTEXT01.pdfFile size 1285 kBChecksum SHA-512
86172ee1fbd13430d8457330be1e1eb22112a968bf52071b27ba3e9a70df55a9840cac21fbac762e426f621a3ddd82a23b4b87797c8f13ab322c8a88f0a23358
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Authority records BETA

Shen, Qiujinde Oliveira, Felipe Marques SouzaKamali-Moghaddam, Masood

Search in DiVA

By author/editor
Shen, Qiujinde Oliveira, Felipe Marques SouzaLisacek, FrederiqueKamali-Moghaddam, Masood
By organisation
Science for Life Laboratory, SciLifeLabMolecular toolsDepartment of Immunology, Genetics and Pathology
In the same journal
EBioMedicine
Immunology in the medical area

Search outside of DiVA

GoogleGoogle Scholar
Total: 4 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 34 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf