Evidence map›Paper›PMID 29713252›Full record

ArticleClinical proteomics2018

Quantitative proteomic analysis of pancreatic cyst fluid proteins associated with malignancy in intraductal papillary mucinous neoplasms.

Misol Do, Dohyun Han, Joseph Injae Wang, Hyunsoo Kim, Wooil Kwon, Youngmin Han, Jin-Young Jang, Youngsoo Kim

Open access · goldAbstract read
In one paragraph

Article in Clinical proteomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 23% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Clinical and Molecular Attributes and Evaluation of Pancreatic Cystic Neoplasm.Biochimica et biophysica acta. Reviews on cancer · 2023
    Review
  7. Article
  8. Article
  9. Observational
  10. Article
  11. Article
  12. Molecular Diagnosis of Cystic Neoplasms of the Pancreas: a Review.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2020
    Review
  13. Review
  14. 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

8 authors at 2 institutions in 2 countries.

Misol Do *1Department of Biomedical Sciences, Seoul National University College of Medicine, 28 Yeongeon-dong, Seoul, 110-799 Korea.
Dohyun Han *4Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Seoul, Korea.
Joseph Injae Wang2Department of Biomedical Engineering, Seoul National University College of Medicine, 28 Yeongeon-dong, Seoul, 110-799 Korea.
Hyunsoo Kim2Department of Biomedical Engineering, Seoul National University College of Medicine, 28 Yeongeon-dong, Seoul, 110-799 Korea.
Wooil Kwon3Department of Surgery, Seoul National University College of Medicine, 28 Yeongeon-dong, Seoul, 110-799 Korea.
Youngmin Han3Department of Surgery, Seoul National University College of Medicine, 28 Yeongeon-dong, Seoul, 110-799 Korea.
Jin-Young Jang3Department of Surgery, Seoul National University College of Medicine, 28 Yeongeon-dong, Seoul, 110-799 Korea.
Youngsoo Kim1Department of Biomedical Sciences, Seoul National University College of Medicine, 28 Yeongeon-dong, Seoul, 110-799 Korea.ORCID 0000-0001-8881-0662
Seoul National University · KRSeoul National University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe application of advanced imaging technologies for identifying pancreatic cysts has become widespread. However, accurately differentiating between low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive intraductal papillary mucinous neoplasms (IPMNs) remains a diagnostic challenge with current biomarkers, necessitating the development of novel biomarkers that can distinguish IPMN malignancy.

methodsCyst fluid samples were collected from nine IPMN patients (3 LGD, 3 HGD, and 3 invasive IPMN) during their pancreatectomies. An integrated proteomics approach that combines filter-aided sample preparation, stage tip-based high-pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data of pancreatic cyst fluid and discover marker candidates for IPMN malignancy. Biological processes of differentially expressed proteins that are related to pancreatic cysts and aggressive malignancy were analyzed using bioinformatics tools such as gene ontology analysis and Ingenuity pathway analysis. In order to confirm the validity of the marker candidates, 19 cyst fluid samples were analyzed by western blot.

resultsA dataset of 2992 proteins was constructed from pancreatic cyst fluid samples. A subsequent analysis found 2963 identified proteins in individual samples, 2837 of which were quantifiable. Differentially expressed proteins between histological grades of IPMN were associated with pancreatic diseases and malignancy according to ingenuity pathway analysis. Eighteen biomarker candidates that were differentially expressed across IPMN histological grades were discovered-7 DEPs that were upregulated and 11 that were downregulated in more malignant grades. HOOK1 and PTPN6 were validated by western blot in an independent cohort, the results of which were consistent with our proteomic data.

conclusionsThis study demonstrates that novel biomarker candidates for IPMN malignancy can be discovered through proteomic analysis of pancreatic cyst fluid.

Indexed as

Biomarker candidatesIntraductal papillary mucinous neoplasm (IPMN)IPMN dysplasiaLC–MS/MSPancreatic cyst fluid

Identifiers

PMID29713252
PMCPMC5907296
OpenAlexW2801230854

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LicenceCC BY
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Registered trials

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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.