miRNA-mRNA network detects hub mRNAs and cancer specific miRNAs in lung cancer.

Q2 Medicine In Silico Biology Pub Date : 2011-01-01 DOI:10.3233/ISB-2012-0444
Saranya Devaraj, Jeyakumar Natarajan
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引用次数: 11

Abstract

MicroRNA expression profiles can improve classification, diagnosis, and prognostic information of malignancies, including lung cancer. In this paper, we undertook to develop a miRNA-mRNA network and uncover unique growth suppressive miRNAs in lung cancer using microarray data. The miRNA-mRNA network was developed based on a bipartite graph theory approach, and a number of miRNA-mRNA modules have been identified to mine associations between miRNAs and mRNAs. From the network, we identified totally 29 protective miRNA-mRNA regulatory modules, since we restricted our search to protective miRNAs. Subsequently we analyzed the pathways for the target genes in the protective miRNA-mRNA modules using Pathway-Express. The miRNA-mRNA network efficiently detects hub mRNAs deregulated by the protective miRNAs and identifies cancer specific miRNAs in lung cancer. From the pathway analysis results, the ECM receptor pathway, Focal adhesion pathway and cell adhesion molecules pathway seem to be more interesting to investigate, since these pathways were related to all the ten protective miRNAs. Furthermore, protective miRNA target analysis revealed that genes VCAN, SIL, CD44 and MMP14 were found to have an important role in these pathways. Hence, it was inferred that these genes can be important putative targets for those protective miRNAs. A greater understanding of the mechanisms regulating VCAN, SIL, CD44 and MMP14 expression and activity will assist in the development of specific inhibitors of cancer cell metastasis. Thus these observations are expected to have an intense implication in cancer and may be useful for further research.

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miRNA-mRNA网络在肺癌中检测中心mrna和癌症特异性mirna。
MicroRNA表达谱可以改善包括肺癌在内的恶性肿瘤的分类、诊断和预后信息。在本文中,我们致力于开发miRNA-mRNA网络,并利用微阵列数据揭示肺癌中独特的生长抑制mirna。miRNA-mRNA网络是基于二部图理论方法开发的,并且已经确定了许多miRNA-mRNA模块来挖掘mirna和mrna之间的关联。从网络中,我们确定了总共29个保护性miRNA-mRNA调控模块,因为我们将搜索限制在保护性mirna上。随后,我们使用Pathway-Express分析了保护性miRNA-mRNA模块中靶基因的通路。miRNA-mRNA网络有效地检测被保护性mirna解除调控的枢纽mrna,并在肺癌中识别癌症特异性mirna。从途径分析结果来看,ECM受体途径、局灶黏附途径和细胞黏附分子途径似乎更值得研究,因为这些途径都与这10种保护性mirna相关。此外,保护性miRNA靶分析显示,基因VCAN、SIL、CD44和MMP14在这些途径中发挥重要作用。因此,我们推测这些基因可能是这些保护性mirna的重要靶点。进一步了解VCAN、SIL、CD44和MMP14表达和活性的调控机制将有助于开发特异性的癌细胞转移抑制剂。因此,这些观察结果有望对癌症产生强烈的影响,并可能对进一步的研究有用。
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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