Jing Chen1, Yang Zhou2, Yaqi Gao3, Weijie Cao2, Hui Sun2, Yanfang Liu2, Chong Wang2. 1. a Nursing College of Zhengzhou University , Zhengzhou , China. 2. b Department of Hematology , The First Affiliated Hospital of Zhengzhou University , Zhengzhou , China. 3. c Nursing College of Hebi Polytechnic , Hebi , China.
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.
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.
Authors: Soyoung Shin; Woori Jang; Myungshin Kim; Yonggoo Kim; Suk Young Park; Joonhong Park; Young Jun Yang Journal: Medicine (Baltimore) Date: 2018-01 Impact factor: 1.889