Identification of common tumor signatures based on gene set enrichment analysis.

Q2 Medicine In Silico Biology Pub Date : 2011-01-01 DOI:10.3233/ISB-2012-0440
Xiaosheng Wang
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Abstract

The identification of common tumor signatures can discover the shared molecular mechanisms underlying tumorgenesis whereby we can prevent and treat tumors by a system intervention. We identified tumor-associated signatures including pathways, transcription factors, microRNAs and gene ontology categories by analyzing gene sets for differential expression between normal vs. tumor phenotypes classes in various tumor gene expression datasets. We obtained the common tumor signatures based on their identified frequencies for different tumor types. Some shared signatures important for various tumor types were uncovered and discussed. We proposed that the interventions aiming at both the shared tumor signatures and the tissue-specific tumor signatures might be a potential approach to overcoming cancer.

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基于基因组富集分析的常见肿瘤特征识别。
识别共同的肿瘤特征可以发现肿瘤发生的共同分子机制,从而通过系统干预预防和治疗肿瘤。我们通过分析各种肿瘤基因表达数据集中正常与肿瘤表型类别之间差异表达的基因集,确定了包括通路、转录因子、microRNA 和基因本体论类别在内的肿瘤相关特征。我们根据不同肿瘤类型的识别频率获得了常见的肿瘤特征。我们发现并讨论了一些对不同肿瘤类型都很重要的共同特征。我们提出,针对共有肿瘤特征和组织特异性肿瘤特征的干预措施可能是攻克癌症的一种潜在方法。
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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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