Evidence map›Paper›PMID 25100294›Full record

ArticleCancer2014

Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma.

Ali H Zaidi, Vanathi Gopalakrishnan, Pashtoon M Kasi, Xuemei Zeng, Usha Malhotra, Jeya Balasubramanian, Shyam Visweswaran, Mai Sun, Melanie S Flint, Jon M Davison and 5 more

Registry-linked trialAbstract readEvaluation Study
In one paragraph

Article in Cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02464930 (Evaluation of MicroRNA Expression in Blood and Cytology Specimens as a Novel Method for Detecting Barrett's Esophagus), which is not on this map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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.

NCT02464930 unknown statusnot on this mapstarted 2015, after this paper: background citation

Evaluation of MicroRNA Expression in Blood and Cytology Specimens as a Novel Method for Detecting Barrett's Esophagus

TypeobservationalSponsorMidwest Biomedical ResearchRan2015 to 2018Enrolled220ConditionsBarrett's Esophagus, Gastroesophageal Reflux, Esophageal Adenocarcinoma
3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Observational
  20. Review
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

15 authors.

Ali H ZaidiInstitute for the Treatment of Esophageal and Thoracic Disease, Allegheny Health Network, Pittsburgh, Pennsylvania.
Vanathi Gopalakrishnan
Pashtoon M Kasi
Xuemei Zeng
Usha Malhotra
Jeya Balasubramanian
Shyam Visweswaran
Mai Sun
Melanie S Flint
Jon M Davison
Brian L Hood
Thomas P Conrads
Jacques J Bergman
William L Bigbee
Blair A Jobe

Funding

VECTOR CORE FACILITYP30CA047904 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Jessie Nedrow · 1988 to 2026
$158.0M
Bayesian Rule Learning Methods for Disease Prediction and Biomarker DiscoveryR01LM010950 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GOPALAKRISHNAN, VANATHI · 2011 to 2013
$1.1M
NCI NIH HHS P30 CA047904NCI NIH HHS P30CA047904NLM NIH HHS R01 LM010950NLM NIH HHS R01-LM010950
6 · The paper itself

Abstract

backgroundEsophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers can advance the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The authors present results from the development of a serum-based, 4-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker panel for EAC.

methodsA vertically integrated, proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for the detection of EAC. Liquid chromatography-tandem mass spectrometry analysis was performed on formalin-fixed, paraffin-embedded tissue samples that were collected from across the Barrett esophagus (BE)-EAC disease spectrum. The mass spectrometry-based spectral count data were used to guide the selection of candidate serum biomarkers. Then, the serum enzyme-linked immunosorbent assay data were validated in an independent cohort and were used to develop a multiparametric risk-assessment model to predict the presence of disease.

resultsWith a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of Barrett esophagus, high-grade dysplasia, and EAC (P<.05). Eleven proteins from this data set were then tested using enzyme-linked immunosorbent assays in serum samples, of which 5 proteins were significantly elevated in abundance among patients who had EAC compared with normal controls, which mirrored trends across the disease spectrum present in the tissue data. By using serum data, a Bayesian rule-learning predictive model with 4 biomarkers was developed to accurately classify disease class; the cross-validation results for the merged data set yielded accuracy of 87% and an area under the receiver operating characteristic curve of 93%.

conclusionsSerum biomarkers hold significant promise for the early, noninvasive detection of EAC.

Indexed as

AdenocarcinomaAnnexin A6Barrett EsophagusBiglycanBiomarkers, TumorCalgranulin BChromatography, LiquidEarly Detection of CancerEsophageal NeoplasmsHumansModels, BiologicalPeroxidaseTandem Mass SpectrometryAnnexin A6BGN protein, humanBiglycanBiomarkers, TumorCalgranulin BPeroxidasebiomarkerearly detectionesophageal adenocarcinomamonitoringproteinserum

Identifiers

PMID25100294
PMCPMC4441619

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

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.