Evidence map›Paper›PMID 32326232›Full record

ArticleCancers2020

Moesin (

Jeong Hwan Park, Cheol Lee, Dohyun Han, Jae Seok Lee, Kyung Min Lee, Min Ji Song, Kwangsoo Kim, Heonyi Lee, Kyung Chul Moon, Youngsoo Kim and 4 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

13 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. The application of high-throughput proteomics in cytopathology.Journal of pathology and translational medicine · 2022
    Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. 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

14 authors at 3 institutions in 1 country.

Jeong Hwan ParkDepartment of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea.ORCID 0000-0003-4522-9928
Cheol LeeDepartment of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea.ORCID 0000-0001-5098-8529
Dohyun HanDivision of Clinical Bioinformatics, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea.ORCID 0000-0002-0841-1598
Jae Seok LeeDepartment of Pathology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Korea.
Kyung Min LeeCenter for Medical Innovation, Biomedical Research Institute, Seoul National University Hospital, Seoul 03082, Korea.
Min Ji SongDepartment of Pathology, Seoul National University Hospital, Seoul 03080, Korea.
Kwangsoo KimDivision of Clinical Bioinformatics, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea.ORCID 0000-0002-4586-5062
Heonyi LeeDivision of Clinical Bioinformatics, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea.
Kyung Chul MoonDepartment of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea.
Youngsoo KimDepartment of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Korea.
Minsun JungDepartment of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea.ORCID 0000-0002-8701-4282
Ji Hye MoonDepartment of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea.
Hyebin LeeDepartment of Radiation Oncology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, 03181, Korea.
Han Suk RyuDepartment of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea.
Seoul National University · KRSeoul National University Hospital · KRSamsung (South Korea) · KR

Funding

National Research Foundation of Korea 2019R1C1C1006640National Research Foundation of Korea NRF-2017R1C1B5017694Seoul National University Hospital 03-2015-0230
6 · The paper itself

Abstract

Bladder urothelial carcinoma (BUC) is the most lethal malignancy of the urinary tract. Treatment for the disease highly depends on the invasiveness of cancer cells. Therefore, a predictive biomarker needs to be identified for invasive BUC. In this study, we employed proteomics methods on urine liquid-based cytology (LBC) samples and a BUC cell line library to determine a novel predictive biomarker for invasive BUC. Furthermore, an in vitro three-dimensional (3D) invasion study for biological significance and diagnostic validation through immunocytochemistry (ICC) were also performed. The proteomic analysis suggested moesin (

Indexed as

biomarkerbladder urothelial carcinomainvasionliquid-based cytologymoesinproteomics

Identifiers

PMID32326232
PMCPMC7225967
OpenAlexW3020620972

What Socratic 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 Socratic graph.