Statistical analysis
The characteristic data of all recruited HCs and asthma patients were
showed as Mean ± SD, p -value < 0.05, analyzed by
unpaired T test. T test and nonparametric test (Mann-Whitney U test)
were used to analyze the mRNA expression and the methylation array of
AREG, ATG3, E2F1, FOXO3, HDAC1, MMP2, NUF2, TGFB1 and TP53. We used the
Benjamin Hochberg method to control the false discovery rate (FDR). The
selection of distinguishingly expressed CpG sites was performed by
Logistic regression analysis, with latent risk factors of age and gender
[24]. The correlation between the percentage of methylation of
candidate CpG sites and successive variables for instance
FEV1%, FVC, FEV1 and PEF was assessed
by Pearson’s correlation or Spearman’s correlation. ROC analysis was
obtained to elucidate the accuracy of candidate DMSs or methylation
change rate s in predicting asthma. For each candidate DMS, the optimal
cutoff value for predicting asthma and corresponding sensitivity and
specificity were defined by the maximum Youden index value (sensitivity
+ specificity-1) [25]. The methylation percentage of candidate DMSs
or the methylation status (change or not change) were used for PCA to
identify asthma. For each candidate DMS, the change in methylation
status was defined by its optimal threshold [26]. The methylation
change rate in each sample mainly referred to the probability that the
methylation status of the candidate DMSs changed. The statistical
analyses were implemented using SPSS version 22.0 (IBM Corporation,
Armonk, NY, USA). A two-tailed p -value < 0.05 was
considered statistically significant, **** p < 0.0001;
* p < 0.05.