Evidence map›Paper›PMID 24838641›Full record

ArticleBrazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica2014

Transduction motif analysis of gastric cancer based on a human signaling network.

G Liu, D Z Li, C S Jiang, W Wang

Open access · goldAbstract read
In one paragraph

Article in Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 93% 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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

G LiuDepartment of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou, China.
D Z LiDepartment of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou, China.
C S JiangDepartment of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou, China.
W WangDepartment of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou, China.
Fuzhou General Hospital of Nanjing Military Command · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

Indexed as

Data MiningGene Regulatory NetworksAmino Acid MotifsCarcinogenesisCell DeathFeedback, PhysiologicalGene Expression RegulationGene OntologyHumansMolecular Sequence AnnotationPhosphorylationSignal TransductionStomach Neoplasms

Identifiers

PMID24838641
PMCPMC4075304
OpenAlexW1996877734

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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.