ArticleBioinformation2014
Identification of miRNAs Expression Profile in Gastric Cancer Using Self-Organizing Maps (SOM).
Article in Bioinformation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- Epigenetic Field Cancerization in Gastric Cancer: microRNAs as Promising Biomarkers.Journal of Cancer · 2019Article
- SOX2 interferes with the function of CDX2 in bile acid-induced gastric intestinal metaplasia.Cancer cell international · 2019Article
- Identification of key miRNAs and genes associated with stomach adenocarcinoma from The Cancer Genome Atlas database.FEBS open bio · 2018Article
- Increased T-helper 17 cell differentiation mediated by exosome-mediated microRNA-451 redistribution in gastric cancer infiltrated T cells.Cancer science · 2018Article
- MiR-31 Regulates Rho-Associated Kinase-Myosin Light Chain (ROCK-MLC) Pathway and Inhibits Gastric Cancer Invasion: Roles of RhoA.Medical science monitor : international medical journal of experimental and clinical research · 2016Article
- Role of miRNAs and their potential to be useful as diagnostic and prognostic biomarkers in gastric cancer.World journal of gastroenterology · 2016Review
- hsa-miR-29c and hsa-miR-135b differential expression as potential biomarker of gastric carcinogenesis.World journal of gastroenterology · 2016Article
- MicroRNA-141 inhibits tumor growth and metastasis in gastric cancer by directly targeting transcriptional co-activator with PDZ-binding motif, TAZ.Cell death & disease · 2015Article
- miR-21 inhibits the effects of cyclooxygenase-2 inhibitor NS398 on apoptosis and invasion in gastric cancer cells.OncoTargets and therapy · 2015Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, an unsupervised artificial neural network was implemented to identify the patters of specific signatures. The network was based on the differential expression of miRNAs (under or over expression) found in healthy or cancerous gastric tissues. Among the tissues analyzes, the neural network evaluated 514 miRNAs of gastric tissue that exhibited significant differential expression. The result suggested a specific expression signature nine miRNAs (hsa-mir-21, hsa-mir-29a, hsa-mir-29c, hsa-mir-148a, hsa-mir-141, hsa-let-7b, hsa-mir-31, hsa-mir-451, and hsa-mir-192), all with significant values (p-value < 0.01 and fold change > 5) that clustered the samples into two groups: healthy tissue and gastric cancer tissue. The results obtained "in silico" must be validated in a molecular biology laboratory; if confirmed, this method may be used in the future as a risk marker for gastric cancer development.
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Registered trials
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.