Evidence map›Paper›PMID 18837081›Full record

ArticleWorld journal of gastroenterology2008

Comparative proteomics analysis of human gastric cancer.

Wei Li, Jian-Fang Li, Ying Qu, Xue-Hua Chen, Jian-Min Qin, Qin-Long Gu, Min Yan, Zheng-Gang Zhu, Bing-Ya Liu

Open access · hybridAbstract read
In one paragraph

Article in World journal of gastroenterology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-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

7 citing papers in PubMed, 24 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

9 authors at 4 institutions in 2 countries.

Wei LiDepartment of Surgery, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Jian-Fang Li
Ying Qu
Xue-Hua Chen
Jian-Min Qin
Qin-Long Gu
Min Yan
Zheng-Gang Zhu
Bing-Ya Liu
Shanghai University of Traditional Chinese Medicine · CNShanghai Jiao Tong University · CNRuijin Hospital · CNScience and Technology Commission of Shanghai Municipality · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo isolate and identify differentially expressed proteins between cancer and normal tissues of gastric cancer by two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).

methodsSoluble fraction proteins of gastric cancer tissues and paired normal tissues were separated by 2-DE. The differentially expressed proteins were selected and identified by MALDI-TOF-MS and database search.

results2-DE profiles with high resolution and reproducibility were obtained. Twenty-three protein spots were excised from sliver staining gel and digested in gel by trypsin, in which fifteen protein spots were identified successfully. Among the identified proteins, there were ten over-expressed and five under-expressed proteins in stomach cancer tissues compared with normal tissues.

conclusionIn this study, the well-resolved, reproducible 2-DE patterns of human gastric cancer tissue and paired normal tissue were established and optimized and certain differentially-expressed proteins were identified. The combined use of 2-DE and MS provides an effective approach to screen for potential tumor markers.

Indexed as

ProteomicsAdultAgedAmino Acid SequenceBiomarkers, TumorCell DifferentiationDatabases, ProteinElectrophoresis, Gel, Two-DimensionalFemaleHumansMaleMiddle AgedMolecular Sequence DataNeoplasm ProteinsNeoplasm StagingReproducibility of ResultsBiomarkers, TumorNeoplasm Proteins

Identifiers

PMID18837081
PMCPMC2748199
OpenAlexW2000061112

What OpenQuestion holds

Textmetadata
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.