Establishment of Relational Model of Congenital Heart Disease Markers and GO Functional Analysis of the Association between Its Serum Markers and Susceptibility Genes

Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2016, Article ID 9506829, 14 pages http://dx.doi.org/10.1155/2016/9506829 Research Article Establishment of Relational Model of Congenital Heart Disease Markers and GO Functional Analysis of the Association between Its Serum Markers and Susceptibility Genes Min Liu,1,2 Luosha Zhao,1 and Jiaying Yuan3 1 Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, No. 1 East Jianshe Road, Zhengzhou 450052, China 2 Department of Cardiovascular Medicine, Zhengzhou Central Hospital, Zhengzhou University, No. 195 Tongbai Road, Zhengzhou 450007, China 3 Department of Ultrasound Diagnosis, Directly under Hospital of Henan Military Region, No. 18 Jinshui Road, Zhengzhou 450000, China Correspondence should be addressed to Luosha Zhao; Received 3 August 2015; Revised 24 September 2015; Accepted 1 October 2015 Academic Editor: Krishna Agarwal Copyright © 2016 Min Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose. The purpose of present study was to construct the best screening model of congenital heart disease serum markers and to provide reference for further prevention and treatment of the disease. Methods. Documents from 2006 to 2014 were collected and meta-analysis was used for screening susceptibility genes and serum markers closely related to the diagnosis of congenital heart disease. Data of serum markers were extracted from 80 congenital heart disease patients and 80 healthy controls, respectively, and then logistic regression analysis and support vector machine were utilized to establish prediction models of serum markers and Gene Ontology (GO) functional annotation. Results. Results showed that NKX2.5, GATA4, and FOG2 were susceptibility genes of congenital heart disease. CRP, BNP, and cTnI were risk factors of congenital heart disease (𝑝 < 0.05); cTnI, hs-CRP, BNP, and Lp(a) were significantly close to congenital heart disease (𝑝 < 0.01). ROC curve indicated that the accuracy rate of Lp(a) and cTnI, Lp(a) and BNP, and BNP and cTnI joint prediction was 93.4%, 87.1%, and 97.2%, respectively. But the detection accuracy rate of the markers’ relational model established by support vector machine was only 85%. GO analysis suggested that NKX2.5, GATA4, and FOG2 were functionally related to Lp(a) and BNP. Conclusions. The combined markers model of BNP and cTnI had the highest accuracy rate, providing a theoretical basis for the diagnosis of congenital heart disease. 1. Introduction Congenital heart disease (CHD) indicates the presence of abnormality in heart and vascular structure and function at birth, the pathogenesis of which is complex. It is the interaction results of multiple factors like heredity and environment. The known risk factors include mental stimulation during pregnancy [1], harmful substances exposure [2], smoking and drinking [3], viral infections at early stage of pregnancy [4], diabetes mellitus [5], history of unhealthy pregnancy [6], and too high maternal age [7]. Its clinical consequences are extremely serious. It is the important cause of miscarriage, stillbirth, neonatal death, and children, adolescents, and adults with disabilities. The incidence of fetal CHD reaches as much as 6% to 10% [8] and continues to show a significant upward trend in China [9]. Currently, CHD is still cured by surgery. Many scholars believe that a number of indicators such as the level of serum C-reactive protein (CRP), brain natriuretic peptide (BNP), cardiac troponin I (cTnI), and Lipoprotein(a) (Lp(a)) can better reflect the functional status of the heart in patients with CHD and have good potential in clinical analysis. These proteins may serve as indicators in prognosis evaluation. Since the United States has announced precision medicine plan, countries around the world have increased the support for precision medicine. With the enrichment and 2 improvement of clinical big data and biological networks, it has become a general trend to complete interdisciplinary collaboration in disease prediction, diagnosis, and etiology analysis. In daily life, clinicians commonly use Logistic regression analysis to analyze the prognostic factors of the disease and estimate the probability of occurrence of variables [10]. Support vector machine (SVM) is a new machine learning method based on statistical theory. SVM is good at coping with linearly nonseparable sample data, which is achieved mainly through the slack variables (which are also called punishment variables) and kernel technology. It provides a unified framework in solving learning problems of finite samples [11]. Increasing studies show that the pathogenesis of congenital heart disease is related to certain transcription factors, while the relationship between the susceptibility genes and serological markers of congenital heart disease is not yet reported. With the rapid application of bioinformatics, Gene Ontology (GO) has become important tool and method in the field of bioinformatics. In terms of gene function annotation, GO plays a huge role. It can analyze the location of gene or protein in the cell, molecular functions, and biological processes involved; thus it simplifies the annotation of genes and their products as standardized vocabularies. In this study, data of the susceptibility genes and clinical serology risk factors literatures of CHD were performed Meta-analysis to systematically evaluate them. By detecting levels of serum markers in patients with CHD, Logistic regression analysis, receiver operating characteristic (ROC) curve, and SVM approaches were used to evaluate the value of each serum marker in clinical diagnosis of CHD. The detection model of serum markers of this disease was then established. The functional relationship between susceptibility genes and serum markers was established by GO analysis. As a result, this study provides a theoretical basis for clinical practice and personalized treatment of cardiovascular disease. 2. Materials and Methods 2.1. Meta-Analysis 2.1.1. Subjects. Clinical research documents on susceptibility genes and serological markers of CHD published in China and foreign countries from January 2006 to October 2014 were selected. 2.1.2. Document Retrieval. Google Scholar was a major source of Chinese documents; PubMed, EMBASE, MEDLINE, and MD consult were main sources of English documents and the Chinese or English key words were “congenital heart disease”, “gene”, and “mutation” as well as “congenital heart disease”, “serum markers”, and “diagnosis”. The years of publication were from January 1, 2000, to October 31, 2014. 2.2. Statistical Analysis. RevMan5.1 was used for metaanalysis of the included literature. 𝑝 ≥ 0.05 showed that the merge statistics of multiple stu (...truncated)