Evidence map›Paper›PMID 21533267›Full record

ArticlePloS one2011

A comprehensive peptidome profiling technology for the identification of early detection biomarkers for lung adenocarcinoma.

Koji Ueda, Naomi Saichi, Sachiko Takami, Daechun Kang, Atsuhiko Toyama, Yataro Daigo, Nobuhisa Ishikawa, Nobuoki Kohno, Kenji Tamura, Taro Shuin and 4 more

Abstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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.

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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. 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

14 authors.

Koji UedaLaboratory for Biomarker Development, Center for Genomic Medicine, RIKEN, Yokohama, Japan. k-ueda@riken.jp
Naomi Saichi
Sachiko Takami
Daechun Kang
Atsuhiko Toyama
Yataro Daigo
Nobuhisa Ishikawa
Nobuoki Kohno
Kenji Tamura
Taro Shuin
Masato Nakayama
Taka-Aki Sato
Yusuke Nakamura
Hidewaki Nakagawa

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mass spectrometry-based peptidomics approaches have proven its usefulness in several areas such as the discovery of physiologically active peptides or biomarker candidates derived from various biological fluids including blood and cerebrospinal fluid. However, to identify biomarkers that are reproducible and clinically applicable, development of a novel technology, which enables rapid, sensitive, and quantitative analysis using hundreds of clinical specimens, has been eagerly awaited. Here we report an integrative peptidomic approach for identification of lung cancer-specific serum peptide biomarkers. It is based on the one-step effective enrichment of peptidome fractions (molecular weight of 1,000-5,000) with size exclusion chromatography in combination with the precise label-free quantification analysis of nano-LC/MS/MS data set using Expressionist proteome server platform. We applied this method to 92 serum samples well-managed with our SOP (standard operating procedure) (30 healthy controls and 62 lung adenocarcinoma patients), and quantitatively assessed the detected 3,537 peptide signals. Among them, 118 peptides showed significantly altered serum levels between the control and lung cancer groups (p<0.01 and fold change >5.0). Subsequently we identified peptide sequences by MS/MS analysis and further assessed the reproducibility of Expressionist-based quantification results and their diagnostic powers by MRM-based relative-quantification analysis for 96 independently prepared serum samples and found that APOA4 273-283, FIBA 5-16, and LBN 306-313 should be clinically useful biomarkers for both early detection and tumor staging of lung cancer. Our peptidome profiling technology can provide simple, high-throughput, and reliable quantification of a large number of clinical samples, which is applicable for diverse peptidome-targeting biomarker discoveries using any types of biological specimens.

Indexed as

AdenocarcinomaBiomarkers, TumorCase-Control StudiesChromatography, LiquidHumansLung NeoplasmsNeoplasm ProteinsROC CurveTandem Mass SpectrometryBiomarkers, TumorNeoplasm Proteins

Identifiers

PMID21533267
PMCPMC3075260

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.