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通过调控网络分析鉴别阴性和阳性胃癌

Differentiation of -negative and positive gastric cancer via regulatory network analysis.

作者信息

Abdi Saeid, Zamanian Azodi Mona, Rezaei-Tavirani Mostafa, Razzaghi Mohammadreza, Heidari Mohammah Hossein, Akbarzadeh Baghban Alireza

机构信息

Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

出版信息

Gastroenterol Hepatol Bed Bench. 2020 Spring;13(2):161-167.

Abstract

AIM

To understand the molecular difference between negative and positive gastric cancer, a regulatory network analysis is investigated.

BACKGROUND

Helicobacter pylori as the one of the most leading causes of gastric cancer is yet to be studied in terms of its molecular pathogenicity.

METHODS

Cytoscape version of 3.7.2 with its applications was employed to conduct this study via corresponding algorithms.

RESULTS

A total of 161 microRNAs were identified differentially expressed in the comparison of two groups of gastric cancer including negative and positive with infection. CluePedia explored the regulatory network and found down-regulation dominant while considering the linked hub genes.

CONCLUSION

It can be concluded that the presented microRNAs and target genes could have associations with carcinogenesis in gastric cancer through dysregulation of some vital biological processes. These microRNAs and target genes include hsa-miR-943, hsa-miR-935, hsa-miR-367, hsa-miR-363, hsa-miR-25, and hsa-miR-196b and ADRA1A, KCNA4, SOD1, and SESN3, respectively. However, verification analysis in this regard is required to establish these relationships.

摘要

目的

为了解阴性和阳性胃癌之间的分子差异,开展了一项调控网络分析。

背景

幽门螺杆菌作为胃癌最主要的病因之一,其分子致病性尚未得到研究。

方法

使用Cytoscape 3.7.2版本及其应用程序,通过相应算法进行本研究。

结果

在两组胃癌(包括感染阴性和阳性)的比较中,共鉴定出161种差异表达的微小RNA。CluePedia探索了调控网络,在考虑相关枢纽基因时发现下调占主导。

结论

可以得出结论,所呈现的微小RNA和靶基因可能通过某些重要生物过程的失调与胃癌的致癌作用有关。这些微小RNA和靶基因分别包括hsa-miR-943、hsa-miR-935、hsa-miR-367、hsa-miR-363、hsa-miR-25和hsa-miR-196b以及ADRA1A、KCNA4、SOD1和SESN3。然而,需要在这方面进行验证分析以确立这些关系。

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