Evidence map›Paper›PMID 28615921›Full record

ArticleBiomarker insights2017

Data-Independent Acquisition and Parallel Reaction Monitoring Mass Spectrometry Identification of Serum Biomarkers for Ovarian Cancer.

Navin Rauniyar, Gang Peng, TuKiet T Lam, Hongyu Zhao, Gil Mor, Kenneth R Williams

Open access · goldAbstract read
In one paragraph

Article in Biomarker insights, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.5field-weighted citation impact, top 21% 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

15 citing papers in PubMed, 33 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Navin RauniyarW.M. Keck Foundation Biotechnology Resource Laboratory, Yale School of Medicine, Yale University, New Haven, CT, USA.
Gang PengBiostatistics Department, School of Public Health, Yale University, New Haven, CT, USA.
TuKiet T LamW.M. Keck Foundation Biotechnology Resource Laboratory, Yale School of Medicine, Yale University, New Haven, CT, USA.
Hongyu ZhaoBiostatistics Department, School of Public Health, Yale University, New Haven, CT, USA.
Gil MorObstetrics, Gynecology & Reproductive Sciences, Yale School of Medicine, Yale University, New Haven, CT, USA.
Kenneth R WilliamsDepartment of Molecular Biophysics and Biochemistry, Yale School of Medicine, Yale University, New Haven, CT, USA.
Yale University · USW. M. Keck Foundation · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A data-independent acquisition (DIA)/parallel reaction monitoring (PRM) workflow was implemented to identify improved ovarian cancer biomarkers. Data-independent acquisition on ovarian cancer versus control sera and literature searches identified 50 biomarkers and indicated that apolipoprotein A-IV (ApoA-IV) is the most significantly differentially regulated protein. Parallel reaction monitoring with Targeted Ovarian Cancer Proteome Assay validated differential ApoA-IV expression and quantified 9 other biomarkers. Random Forest (RF) analyses achieved 92.3% classification accuracy and confirmed ApoA-IV as the leading biomarker. Indeed, all samples were classified correctly with an [ApoA-IV] breakpoint. The next best biomarkers were C-reactive protein, transferrin, and transthyretin. The Targeted Ovarian Cancer Proteome Assay suggests that ApoA-IV is a more reliable biomarker than had been determined by immunological assays and it is a better biomarker than ApoA-I, which is in the OVA1 test for ovarian cancer. This research provides a PRM/RF approach together with 4 promising biomarkers to speed the development of a clinical assay for ovarian cancer.

Indexed as

Data-independent acquisitionovarian cancerparallel reaction monitoringserum biomarkerstargeted proteomics

Identifiers

PMID28615921
PMCPMC5462478
OpenAlexW2622389451

What OpenQuestion holds

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LicenceCC BY-NC
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.