Evidence map›Paper›PMID 25368985›Full record

ArticlePloS one2014

Identification of potential serum proteomic biomarkers for clear cell renal cell carcinoma.

Juan Yang, Jin Yang, Yan Gao, Lingyu Zhao, Liying Liu, Yannan Qin, Xiaofei Wang, Tusheng Song, Chen Huang

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Review
  11. Article
  12. Review
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors.Medical science monitor : international medical journal of experimental and clinical research · 2017
    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

9 authors.

Juan YangKey Laboratory of Environment and Genes Related to Diseases of the Education Ministry, Department of Genetics and Molecular Biology, Medical School of Xi'an Jiaotong University, Xi'an, China.
Jin YangDepartment of Medical Oncology, First Affiliated Hospital of Medical School of Xi'an Jiaotong University, Xi'an, China.
Yan GaoDepartment of Medical Oncology, First Affiliated Hospital of Medical School of Xi'an Jiaotong University, Xi'an, China.
Lingyu ZhaoKey Laboratory of Environment and Genes Related to Diseases of the Education Ministry, Department of Genetics and Molecular Biology, Medical School of Xi'an Jiaotong University, Xi'an, China.
Liying LiuKey Laboratory of Environment and Genes Related to Diseases of the Education Ministry, Department of Genetics and Molecular Biology, Medical School of Xi'an Jiaotong University, Xi'an, China.
Yannan QinKey Laboratory of Environment and Genes Related to Diseases of the Education Ministry, Department of Genetics and Molecular Biology, Medical School of Xi'an Jiaotong University, Xi'an, China.
Xiaofei WangKey Laboratory of Environment and Genes Related to Diseases of the Education Ministry, Department of Genetics and Molecular Biology, Medical School of Xi'an Jiaotong University, Xi'an, China.
Tusheng SongKey Laboratory of Environment and Genes Related to Diseases of the Education Ministry, Department of Genetics and Molecular Biology, Medical School of Xi'an Jiaotong University, Xi'an, China.
Chen HuangKey Laboratory of Environment and Genes Related to Diseases of the Education Ministry, Department of Genetics and Molecular Biology, Medical School of Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate discriminating protein patterns and serum biomarkers between clear cell renal cell carcinoma (ccRCC) patients and healthy controls, as well as between paired pre- and post-operative ccRCC patients.

methodsWe used magnetic bead-based separation followed by matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) to identify patients with ccRCC. A total of 162 serum samples were analyzed in this study, among which there were 58 serum samples from ccRCC patients, 40 from additional paired pre- and post-operative ccRCC patients (n = 20), and 64 from healthy volunteers as healthy controls. ClinProTools software identified several distinct markers between ccRCC patients and healthy controls, as well as between pre- and post-operative patients.

resultsPatients with ccRCC could be identified with a mean sensitivity of 88.38% and a mean specificity of 91.67%. Of 67 m/z peaks that differed among the ccRCC, healthy controls, pre- and post-operative ccRCC patients, 24 were significantly different (P<0.05). Three candidate peaks, which were upregulated in ccRCC group and showed a tendency to return to healthy control values after surgery, were identified as peptide regions of RNA-binding protein 6 (RBP6), tubulin beta chain (TUBB), and zinc finger protein 3 (ZFP3) with the m/z values of 1466.98, 1618.22, and 5905.23, respectively.

conclusionMB-MALDI-TOF-MS method could generate serum peptidome profiles of ccRCC, and provide a new approach to identify potential biomarkers for diagnosis as well as prognosis of this malignancy.

Indexed as

AdultAgedAmino Acid SequenceBiomarkers, TumorCarcinoma, Renal CellFemaleHumansKidney NeoplasmsMaleMiddle AgedMolecular Sequence DataPeptidesProteomicsSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationBiomarkers, TumorPeptides

Identifiers

PMID25368985
PMCPMC4219714

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.