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Identification of Novel Mirnas With Potential Role in Gastric Cancer Diagnosis: In Silico Procedure Publisher



Delshad E1, 2 ; Shafiee M2 ; Maghsoudi H1 ; Shamsabadi F3 ; Bahramian S2
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Authors Affiliations
  1. 1. Department of Biotechnology, Payame Noor University, Tehran, Iran
  2. 2. Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
  3. 3. Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Golestan University of Medical Sciences, Gorgan, Iran

Source: Meta Gene Published:2019


Abstract

Gastric Cancer (GC) is the third most common cause of cancer-related deaths in the world. Due to the lack of enough symptoms in early stages, it is diagnosed in advanced stages in the majority of patients and causes great rate of mortality. Early recognition of GC significantly raises the lucks for successful treatment. Molecular mechanisms of GC are still poorly understood. MiRNAs are small non-coding RNAs which regulate gene expression in post-transcription levels. In cancer cells, miRNAs have been found to be severely dysregulated. Using high-throughput (HTP) technologies such as RNA- Seq, the effects of miRNAs on cancers can be investigated. In this study, we retrieved miRNAs obtained by HTP method from OncoLnc database. Consequently, retrieved miRNAs were compared in literature-based databases such as PubMed. As a result, two lists including experimentally validated and predicted miRNAs were provided. We found 28 predicted miRNAs that so far had not been experimentally validated in GC. In the following, further bioinformatics analyses were performed to obtain the expression profile of both validated and predicted miRNAs in tumor and normal tissues. Also, the role of predicted miRNAs in other cancers, and their possible targets in apoptosis, metastasis and angiogenesis retrieved from related databases. By recognizing the miRNAs involved in initiation and progression of GC, they may be considered as potential biomarkers in GC early diagnosis or targeted treatment and lead to novel therapeutic strategies. We introduce 28 predicted miRNAs involved in GC pathogenesis by in silico analysis. © 2018 Elsevier B.V.