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Identification of Susceptibility Genes in Hepatic Cancer Using Whole Exome Sequencing and Risk Prediction Model Construction

Authors
  • Zheng, Jinghui
  • Tang, Youming1
  • Hou, Encun1
  • Bai, Guangde1
  • Lian, Zuping1
  • Xie, Peisheng1
  • Tang, Weizhi1
  • 1 Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, China , (China)
Type
Published Article
Journal
Revista Romana de Medicina de Laborator
Publisher
De Gruyter Open Sp. z o.o.
Publication Date
Jan 01, 2020
Volume
28
Issue
1
Pages
67–74
Identifiers
DOI: 10.2478/rrlm-2020-0008
Source
De Gruyter
Keywords
License
Green

Abstract

Objective: To identify the susceptible single nucleotide polymorphisms (SNPs) loci in HCC patients in Guangxi Region, screen biomarkers from differential SNPs loci by using predictors, and establish risk prediction models for HCC, to provide a basis of screening high-risk individuals of HCC. Methods: Blood sample and clinical data of 50 normal participants and 50 hepatic cancer (HCC) patients in Rui Kang Hospital affiliated to Guangxi University of Traditional Chinese Medicine were collected. Normal participants and HCC patients were assigned to training set and testing set, respectively. Whole Exome Sequencing (WES) technique was employed to compare the exon sequence of the normal participants and HCC patients. Five predictors were used to screen the biomarkers and construct HCC prediction models. The prediction models were validated with both training and testing set. Results: Two-hundred seventy SNPs were identified to be significantly different from HCC, among which 100 SNPs were selected as biomarkers for prediction models. Five prediction models constructed with the 100 SNPs showed good sensitivity and specificity for HCC prediction among the training set and testing set. Conclusion: A series of SNPs were identified as susceptible genes for HCC. Some of these SNPs including CNN2, CD177, KMT2C, and HLADQB1 were consistent with the previously identified polymorphisms by targeted genes examination. The prediction models constructed with part of those SNPs could accurately predict HCC development.

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