Establishment of a prediction model of changing trends in cardiac hypertrophy disease based on microarray data screening
- Authors:
- Published online on: February 24, 2016 https://doi.org/10.3892/etm.2016.3105
- Pages: 1734-1740
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Copyright: © Ma et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Cardiac hypertrophy is associated with the thickening of the heart muscle (1) and the risk factors of cardiac hypertrophy include hypertension, obesity, muscular dystrophy, cardiomyopathy or heart failure (2). Furthermore, it has been demonstrated that genetic factors and signaling pathways may participate in the pathogenesis of cardiac hypertrophy, which may be associated with an enhanced risk of sudden cardiac death and cardiovascular mortality (3,4). As the early symptoms of this disease are difficult to detect, it is crucial that novel molecular markers for the early therapy of cardiac hypertrophy are identified.
Molecular markers of cardiac hypertrophy have been identified (5). In particular, Kontaraki et al (6) identified GATA4, myocardin and β-myosin heavy chain as early cardiac marker genes. Furthermore, smooth muscle α-actin has been demonstrated to be a molecular marker for pressure-overload hypertrophy (7). Using mouse models, Qing et al (8) have previously reported that miR-22 serves a crucial function in the regulation of cardiac hypertrophy and cardiac remodeling. Fibroblast growth factor 21, which is an endocrine factor, has a protective role in cardiac cells (9). As an increasing number of molecular markers are identified, mathematical models can be constructed to predict the risk of cancer (10).
Various types of mathematical models have contributed to the prediction of diseases. Flux balance models of cellular metabolism have been used to analyze and predict transcriptional regulation under certain conditions, including catabolite repression and amino acid biosynthesis pathway repression (11). Furthermore, various genes and pathways associated with differentiation, including MAOA and ADH1B metabolic genes in human pulmonary type II cells (12) and nuclear factor-kappaB pathway in a mouse model of genitourinary inflammation (13), have been identified via mathematical cluster analysis using GENECLUSTER, which is a publicly available computer package that contributed to the establishment of an effective treatment for acute promyelocytic leukemia (14). According to a previous study conducted by Kondo and Miura (15), the reaction-diffusion model is effective in biological pattern formation. Thus, these previous studies suggest the mathematical modeling is a useful tool for the prediction of disease.
Using microarray data downloaded from the Gene Expression Omnibus (GEO) database (accession, GSE21600), which included 35 heart samples harvested from a Wistar rat on postoperative days 1, 6 and 42 following aorta ligation and sham-operated Wistar rats, respectively. Hellman et al (16) demonstrated a correlation between hyaluronan concentration and specific gene expression levels using SPSS software. Analysis of the correlation matrix was performed according to the Principal components method (17), and orthogonal partial least squares-discrimination analysis was used to analyze the datasets of GSE21600, in which the previous clustering, including extracellular matrix and adhesion molecules were confirmed, and fatty acid metabolism, glucose metabolism, mitochondria and atherosclerosis were detected as the new clustering (18). However, these previous two studies failed to predict the changing trends of genes in this disease. Hence, the present study aimed to reanalyze the expression profiles of GSE21600 in order to construct a predictive model of cardiac hypertrophy using linear discriminant analysis (LDA) method. GSE21600 microarray data was used to identify differentially expressed genes (DEGs) using a Limma package in R (version. 3.26.5), which calculates linear models of microarray data. Common DEGs were used to construct a mathematical model in order to predict the expression levels of genes in the cardiac hypertrophy samples. The mathematical model was verified receiver operating characteristic (ROC) curve and the consistency of predictive and measurement data. The present study may be useful for the early prediction of changing trends in cardiac hypertrophy disease at the gene level.
Materials and methods
Data preprocessing and DEGs screening
GSE21600 microarray data were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/) (16). GSE21600 included data from 35 heart samples harvested from 36 Wistar rats which were excised on postoperative days 1, 6 and 42 following aorta ligation and sham-operated groups, respectively. Each group contained six samples at each time point, with the exception of the samples harvested from the aorta ligated group at 6 days, where n=5. The microarray platform of GSE21600 was Illumina GPL6101 RatRef-12 expression bead chip (version 1.0; Illumina, Inc., San Diego, CA, USA).
Samples were divided into three groups: Day 1 (D1), day 6 (D6) and day 42 (D42). DEGs between the postoperative and sham-operated samples were identified in these three groups, respectively. Firstly, normalization of the microarray data was performed in the R language (19,20), and DEGs were subsequently identified using a Limma package in R (21). False discovery rate (FDR) was used to adjust the P-value, according to the method outlined by Benjamin and Hochberg (22). FDR<0.05 and >1 log2fold change (FC) were chosen as the cut-off criteria.
Specific gene screening
In order to screen the specific expression levels of genes at each time point, DEGs were compared between the two groups. Subsequently, hierarchical clustering analysis (23) was performed on the common DEGs in the three groups.
Sorting algorithm and construction of the mathematical model
Linear discriminant analysis (LDA) is a method that is commonly widely used in microarray classification to obtain discrimination function. LDA analysis can be performed when there are ≥2 groups and each group contains >2 variables (24,25). In this method, a linear equation based on the variations in the two groups is established: Y=a + b11 + b22 +…+ bnXn, where ‘a’ represents a constant and ‘b1,b2 … and bn’ represents the regression coefficient. In the present study, the cardiac hypertrophy samples were defined as ‘1’ and the control samples were defined as ‘-1’. Based on the dynamic expression changes of the common DEGs detected in the D1 group, the expression pattern in the D42 group was predicted via the calculated mathematical model constructed using the LDA method (26).
Verification of the mathematical model
Disease classification models are typically determined using multivariate regression analysis (27,28), ROC curve (29–32) or prospective validation (33). ROC curve was used in the present study in order to evaluate the discriminant effect of the mathematical model and directly observe the accuracy of the present analysis method. Indices, including specificity and sensitivity, were calculated in order to estimate the predictive ability of LDA, in addition to area under the curve (AUC) of the ROC curve, which was also calculated to estimate accuracy. In the present study, AUC was used to distinguish non-accuracy (AUC≤0.5), low accuracy (0.5<AUC≤0.7), moderate accuracy (0.7<AUC≤0.9) and high accuracy (0.9<AUC<1). Furthermore, by comparing the prediction data with the measurement data in the D42 samples, the consistency of two sets of data was evaluated.
Results
Identification, comparison and feature selection of DEGs
Normalization of the microarray data is presented in Fig. 1. DEGs were identified, and the genes with FDR<0.05 and >1 log2FC were considered as differentially expressed between the ligated samples and sham-operated samples. A total of 319, 44 and 57 DEGs were identified in the D1, D6 and D42 groups respectively.
A total of 23 DEGs were detected between the D1 and D6 groups, 14 DEGs were detected between the D1 and D42 groups, and five DEGs were identified between the D6 and D42 groups. Five common DEGs, including A kinase interacting protein 1 (AKIP1), ankyrin repeat domain 23 (ANKRD23), latent transforming growth factor beta binding protein (LTBP2), transforming growth factor (TGF)-β2 and tumor necrosis factor receptor superfamily member 12a (TNFRSF12A), were identified among the three groups (Fig. 2).
Clustering analysis of the five common DEGs demonstrated that the sham operated and ligated samples were respectively clustered together; however, three ligated samples (16.67%; 3/18) were mixed into the operated group and two sham-operated samples (11.76%; 2/17) were mixed into the ligated group (Fig. 3). These five common DEGs were identified as downregulated genes (Table I).
 | Table I.Expression levels of five common differentially expressed genes the in aorta ligated operation group were calculated, as compared with the sham operated group. |
Construction and verification of the mathematical model
Based on the expression levels and dynamic changes detected in the five common DEGs, a linear equation between the D1 and D42 groups was calculated as follows: y=1.526×-186.671; where ‘y’ and ‘x’ represent the expression levels in the D42 and D1 groups, respectively.
Assessment of the ROC curve demonstrated that AUC was 0.831, which indicated that the predictive accuracy was 83.1% and the specificity and sensitivity were 0.8, respectively (Fig. 4A). By comparing the predictive and measurement data at 42 days (Table II), the consistency of these two datasets was calculated to be 76.5% (Fig. 4B).
| Table II.Predicted data at day 42 using a linear equation of the gene expression levels of cardiac hypertrophy. |
Discussion
In the present study, the expression profiles of sham operated and ligated heart samples harvested from a Wistar rat were analyzed and 319, 44 and 57 DEGs were subsequently identified in the D1, D6 and D42 groups, respectively. AKIP1, ANKRD23, LTBP2, TGF-β2 and TNFRSF12A were identified as common DEGs among the three groups, and their association with cardiac hypertrophy has previously been demonstrated (34–37). AKIP1 was identified as a key regulator of heart function via the cAMP-dependent protein kinase signaling pathway (38). During periods of the oxidant stress, the expression of AKIP1 is capable of protecting cardiac myocytes from the ischemic injury via enhanced mitochondrial integrity (38). Furthermore, the expression of AKIP1 may also protect the heart via mitochondrial stress adaptation (39), and it has been demonstrated that mitochondrial DNA damage may contribute to the development of cardiac hypertrophy and heart failure (40). These results suggested that AKIP1 may serve a crucial function in the development of cardiac hypertrophy via mitochondrial stress adaptation mechanisms. Hellman et al (16) have previously demonstrated that LTBP2 and TGF-β2 are associated with the development of cardiac hypertrophy. LTBP2, which belongs to the fibrillin superfamily, regulates the release of TGF-β1 (41,42). Previous studies have demonstrated that TGF-β, including TGF-β1, TGF-β2 and TGF-β3, have an important role in the pathogenesis of cardiac hypertrophy by stimulating the proliferation of cardiomyocytes (43,44). These results demonstrated that LTBP2 and TGF-β2 are associated with the regulation of cardiac hypertrophy. However, the role of ANKRD23 and TNFRSF12A in the development of cardiac hypertrophy is yet to be elucidated. As the results of the present study demonstrated that they were detected as common genes in the three groups, we hypothesize that AKIP1, ANKRD23, LTBP2, TGF-β2 and TNFRSF12A may contribute to the development of cardiac hypertrophy.
Numerous mathematical techniques have been developed in order to analyze large datasets, and mathematical modeling is a useful and powerful tool for the analysis of gene expression patterns (14). LDA is a well-known multivariate technique that is used for dimension reduction and classification (45). A 3-gene model, TNFRSF8, BATF3 and TMOD1, which was obtained by LDA and leave-one-out cross-validation, was previously used to separate ALK (−) and anaplastic large-cell lymphoma from peripheral T-cell lymphoma, and the accuracy of the model was ~97% (46). Furthermore, a class-prediction model of patients with Graft-vs-host disease was previously constructed using LDA, and the accuracy was 63–80%, as estimated by reverse transcription-quantitative polymerase chain reaction (47). ROC, which directly displays the correlation of specificity and sensitivity can be used to assess the accuracy of diagnostic tests (48). In a previous study conducted by Barretina et al (49), Cancer Cell Line Encyclopedia, which is a predictive model, was cross-validated by specificity and sensitivity of the ROC curve and used to predict the drug response to gene expression, including topoisomerase inhibitors associated with Schlafen family member 11. Similarly, a predictions model has previously been constructed for dementia using LDA and verified by ROC curve, and the accuracy of the model was 66%; whereas the specificity and sensitivity were 73% and 64%, respectively (50). In the present study, a prediction model of cardiac hypertrophy was constructed. The assessment of ROC curve demonstrated that the predictive accuracy of the model was ~83.1% and the specificity and sensitivity were 0.8, respectively. By comparing the predictive and measurement data at 42 days, the consistency of these two datasets was calculated to be 76.5%. These results suggested that the present prediction model provides improved predictive ability, which may contribute to the early prediction of the changing trends in gene expression exhibited in patients with cardiac hypertrophy disease. However, to elevate the discrimination ability of the model, further studies with an increased number of samples and more suitable machine learning algorithm are required.
In the present study, 319, 44 and 57 DEGs were detected in D1, D6 and D42 groups, respectively. AKIP1, ANKRD23, LTBP2, TGF-β2 and TNFRSF12A were identified as common DEGs. A linear equation was calculated between the D1 and D42 groups, as follows: y=1.526×-186.671. This linear equation, which acted as a prediction model of gene expression levels, may contribute to the early prediction of the changing trends in cardiac hypertrophy disease.
Acknowledgements
The authors of the present study would like to thank Fenghe Information Technology Co., Ltd (Shanghai, China) for in-depth editing and language assistance.
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