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一项用于鉴定导致遗传性球形红细胞增多症基因的遗传特征与基因相互作用研究。

A genetic features and gene interaction study for identifying the genes that cause hereditary spherocytosis.

作者信息

Chen Jing, Zhou Yang, Gao Yaqi, Cao Weijie, Sun Hui, Liu Yanfang, Wang Chong

机构信息

a Nursing College of Zhengzhou University , Zhengzhou , China.

b Department of Hematology , The First Affiliated Hospital of Zhengzhou University , Zhengzhou , China.

出版信息

Hematology. 2017 May;22(4):240-247. doi: 10.1080/10245332.2016.1235673. Epub 2016 Oct 3.

Abstract

OBJECTIVE

Hereditary spherocytosis (HS) is a hemolytic disorder characterized by the presence of spherical-shaped red blood cells on the peripheral blood smear. Non-dominant HS cases are due to de novo mutations of the type associated with dominant inheritance or recessive genes. This study is aimed to identify HS-related biological mechanisms and predicting HS candidate genes.

METHODS

We searched the known HS-related genes from the public databases. By analyzing the gene ontology (GO) and biological pathway of these genes, we extracted the optimal features to encode HS genes. Based on them, we predicted the HS-related genes from genes of whole genomes using the Random Forest classification. We used the gene interaction networks analysis to further identify the core regulatory genes that were related to HS.

RESULTS

Forty-one known HS-related genes were found out and encoded. Three hundred and sixty-seven GO terms and ten biological pathway terms were identified as the optimal features for prediction. We subsequently predicted 150 novel HS-related genes and identified the core regulatory genes in the interaction network of predicted and known genes. These features and genes that we identified could complement the genetic features of HS.

摘要

目的

遗传性球形红细胞增多症(HS)是一种溶血性疾病,其特征是外周血涂片上出现球形红细胞。非显性HS病例是由于与显性遗传或隐性基因相关的新发突变所致。本研究旨在确定与HS相关的生物学机制并预测HS候选基因。

方法

我们从公共数据库中搜索已知的与HS相关的基因。通过分析这些基因的基因本体(GO)和生物学途径,我们提取了用于编码HS基因的最佳特征。基于这些特征,我们使用随机森林分类法从全基因组基因中预测与HS相关的基因。我们使用基因相互作用网络分析来进一步鉴定与HS相关的核心调控基因。

结果

找出并编码了41个已知的与HS相关的基因。确定了367个GO术语和10个生物学途径术语作为预测的最佳特征。随后我们预测了150个新的与HS相关的基因,并在预测基因和已知基因的相互作用网络中鉴定了核心调控基因。我们鉴定出的这些特征和基因可以补充HS的遗传特征。

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