Evidence map›Paper›PMID 29556568›Full record

ArticleBiochemistry and biophysics reports2018

Gastric cancer biomarkers; A systems biology approach.

Mohammad Saberi Anvar, Zarrin Minuchehr, Mohsen Shahlaei, Samira Kheitan

Open access · goldAbstract read
In one paragraph

Article in Biochemistry and biophysics reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

10 citing papers in PubMed, 23 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

4 authors at 2 institutions in 1 country.

Mohammad Saberi AnvarDepartment of Systems Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
Zarrin MinuchehrDepartment of Systems Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
Mohsen ShahlaeiNano Drug Delivery Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Samira KheitanDepartment of Systems Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
National Institute of Genetic Engineering and Biotechnology · IRKermanshah University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer is one of the most fatal cancers in the world. Many efforts in recent years have attempted to find effective proteins in gastric cancer. By using a comprehensive list of proteins involved in gastric cancer, scientists were able to retrieve interaction information. The study of protein-protein interaction networks through systems biology based analysis provides appropriate strategies to discover candidate proteins and key biological pathways. In this study, we investigated dominant functional themes and centrality parameters including betweenness as well as the degree of each topological clusters and expressionally active sub-networks in the resulted network. The results of functional analysis on gene sets showed that neurotrophin signaling pathway, cell cycle and nucleotide excision possess the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1 and TP53 manifested as the most significant nodes in the interaction network of the engaged proteins in gastric cancer. This study also demonstrates pathways and proteins that are applicable as diagnostic markers and therapeutic targets for future attempts to overcome gastric cancer.

Indexed as

Gastric cancerHNF4ANeurotrophin signaling pathwaySystems biologyTAF1TP53

Identifiers

PMID29556568
PMCPMC5857180
OpenAlexW2791283291

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.