Establishment of a prediction model of changing trends in cardiac hypertrophy disease based on microarray data screening.

The aim of the present study was to construct a mathematical model to predict the changing trends of cardiac hypertrophy at gene level. Microarray data were downloaded from Gene Expression Omnibus database (accession, GSE21600), which included 35 samples harvested from the heart of Wistar rats on postoperative days 1 (D1 group), 6 (D6 group) and 42 (D42 group) following aorta ligation and sham operated Wistar rats, respectively. Each group contained six samples, with the exception of the samples harvested from the aorta ligated group after 6 days, where n=5. Differentially expressed genes (DEGs) were identified using a Limma package in R. Hierarchical clustering analysis was performed on common DEGs in order to construct a linear equation between the D1 and D42 groups, using linear discriminant analysis. Subsequent verification was performed using receiver operating characteristic (ROC) curve and the measurement data at day 42. A total of 319, 44 and 57 DEGs were detected in D1, D6 and D42 sample groups, respectively. AKIP1, ANKRD23, LTBP2, TGF-β2 and TNFRSF12A were identified as common DEGs in all groups. The predicted linear equation between D1 and D42 group was calculated to be y=1.526×-186.671. Assessment of the ROC curve demonstrated that the area under the curve was 0.831, with a specificity and sensitivity of 0.8. As compared with the predictive and measurement data at day 42, the consistency of the two sets of data was 76.5%. In conclusion, the present model may contribute to the early prediction of changing trends in cardiac hypertrophy disease at gene level.