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在疾病关联中寻找方向性和基因-疾病预测。

Finding directionality and gene-disease predictions in disease associations.

作者信息

Garcia-Albornoz Manuel, Nielsen Jens

机构信息

Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden.

出版信息

BMC Syst Biol. 2015 Jul 15;9:35. doi: 10.1186/s12918-015-0184-9.

Abstract

BACKGROUND

Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders.

METHODS

The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases.

RESULTS

880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology.

CONCLUSIONS

The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications.

摘要

背景

了解人类疾病潜在的分子机制对于复杂疾病的诊断和治疗至关重要,传统上是通过建立疾病基因与其相关疾病之间的关联来实现的。这种网络分析通常仅包括分子成分和共享基因的相互作用。本研究在生物信息学框架下提供了一种网络和关联分析,涉及整合HUGO基因命名委员会批准的基因符号、KEGG代谢途径和ICD-10-CM编码,以便基于不同人类疾病共享的基因和代谢途径之间的包含水平和超几何富集来分析人类疾病。

方法

本研究整合了HGNC批准的基因符号、KEGG代谢途径和ICD-10-CM编码,以便基于不同疾病共享的基因和代谢途径之间的包含水平和超几何富集来分析关联。

结果

880个独特的ICD-10-CM编码被映射到4315个OMIM表型和3083个具有致病变异的基因。由此,总共705个ICD-10-CM编码与1587个具有致病变异的基因和801条KEGG途径相关联,形成了一个由15455个编码-基因-途径相互作用组成的三方网络。这些关联进一步用于疾病之间的包含分析以及基于超几何富集方法的基因-疾病预测。

结论

结果表明,尽管同一类疾病之间共享大量基因和代谢途径,但这些基因和途径之间的包含水平是有方向性的,且与疾病分类无关。然而,基因-疾病-途径关联可用于预测新的基因-疾病相互作用,这将有助于药物发现和治疗应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/4501277/1f8b913e9542/12918_2015_184_Fig1_HTML.jpg

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