Identification of Chiari Type I Malformation subtypes using whole genome expression profiles and cranial base morphometrics

BMC Medical Genomics

Simon G Gregory⁵  Allison E Ashley-Koch²  Herbert Fuchs⁴  Gerald Grant³  David S Enterline⁶  Chien-Kuang C Ding¹  Heidi Cope²  Karen Soldano²  Eric Lock¹  Christina A Markunas¹ 

[1] Duke Center for Human Genetics, Duke University Medical Center, Durham, NC, USA;Duke Center for Human Disease Modeling, Duke University Medical Center, Durham, NC, USA;Department of Neurosurgery, Stanford University/Lucile Packard Children’s Hospital, Stanford, CA, USA;Division of Neurosurgery, Department of Surgery, Duke University Medical Center, Durham, NC, USA;Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA;Division of Neuroradiology, Department of Radiology, Duke University Medical Center, Durham, NC, USA

关键词: Clustering;    Cranial base morphometrics;    Whole genome expression;    Disease subtypes;    Posterior fossa;    Chiari Type I Malformation;   

Others  :  796666
DOI  :  10.1186/1755-8794-7-39

 received in 2014-01-22, accepted in 2014-06-18,  发布年份 2014

【 摘 要 】

Background

Chiari Type I Malformation (CMI) is characterized by herniation of the cerebellar tonsils through the foramen magnum at the base of the skull, resulting in significant neurologic morbidity. As CMI patients display a high degree of clinical variability and multiple mechanisms have been proposed for tonsillar herniation, it is hypothesized that this heterogeneous disorder is due to multiple genetic and environmental factors. The purpose of the present study was to gain a better understanding of what factors contribute to this heterogeneity by using an unsupervised statistical approach to define disease subtypes within a case-only pediatric population.

Methods

A collection of forty-four pediatric CMI patients were ascertained to identify disease subtypes using whole genome expression profiles generated from patient blood and dura mater tissue samples, and radiological data consisting of posterior fossa (PF) morphometrics. Sparse k-means clustering and an extension to accommodate multiple data sources were used to cluster patients into more homogeneous groups using biological and radiological data both individually and collectively.

Results

All clustering analyses resulted in the significant identification of patient classes, with the pure biological classes derived from patient blood and dura mater samples demonstrating the strongest evidence. Those patient classes were further characterized by identifying enriched biological pathways, as well as correlated cranial base morphological and clinical traits.

Conclusions

Our results implicate several strong biological candidates warranting further investigation from the dura expression analysis and also identified a blood gene expression profile corresponding to a global down-regulation in protein synthesis.

【 授权许可】

   
2014 Markunas et al.; licensee BioMed Central Ltd.

【 图 表 】

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