Potential correlation between DMSs of aging-related genes and
clinical index of asthma
To further assess whether the differential methylation of the 9
aging-related genes is related to the occurrence and severity of asthma,
we detected the correlation between the differential 68 DMSs in
aging-related genes and the lung function indicators of asthma patients.
The results demonstrated that there were 10 DMSs significantly were
associated with lung function. The maximum correlation
coefficient for each DMSs was presented in Figure 3. The remaining
correlation analysis data was showed in Figure S1. For these 10 DMSs,
three DMSs (Chr4:75310649, Chr6:108883024, Chr17:7591672) were closely
related to at least three clinical indicators. In addition, other three
DMSs (Chr4:75310649, Chr20:32274088, Chr6:108882977) were related to two
clinical indicators. It has also been shown that the correlation
coefficients of the 10 DMSs were all greater than 0.38 withp -value < 0.05. It was also particularly noteworthy that
Chr17:7591672 was closely related to four lung function indicators (FVC,
FEV1, PEF, FEF25), with a correlation
coefficient of 0.671 and a p -value equal to 0.0001. These data
strongly suggested that theses differential DNAm of the specific
aging-related DMSs may influence the occurrence and severity of asthma.
The complete data for the 10 DMSs and clinical indicators were showed in
Table 3.
Feasibility ofcandidate DMSs as
biomarkers of asthma
Since the differential 10 DMSs have been confirmed to be closely
associated to the clinical lung function of asthma patients, we further
evaluated their potential as biomarkers for asthma patients. First, ROC
analysis of the methylation levels of each candidate DMS was performed.
The areas under the curve (AUC) of 9 DMSs (p -value<
5%) were between 65.3% and 76.3%, and the AUC of 6 DMSs was greater
than 70% (Figure 4A and Table 4). Besides, logistic regression was
conducted and the ROC of 9 candidate DMSs showed that the AUC of the
predicted probability of the 9 candidate DMSs was as high as 95.4%, and
the result was statistically significantly (p -value <
0.1%, Figure 4B). These results indicated that the 9 candidate DMSs had
the potential value to be the biomarkers for asthma. Meanwhile, to
verify the above results, PCA analysis consisting of 9 candidate DMSs
was executed. The result revealed that the methylation levels of the
total 9 DMSs could effectively distinguish asthma patients from HCs
(Figure 4C).
To better understand the possible value of the 9 DMSs, we further
calculate the methylation change rate of the 9 DMSs in HCs and asthma
patients, which is a description of the possibility of methylation
status alteration. Then, the status of the changed methylation or
unchanged methylation was determined using the optimal cutoff value. The
optimal cutoffs of the 9 DMSs were calculated according to the Youden
index which was presented in Table 3. The methylation change rate of HCs
and asthmatic patients were included in Figure 5. Specially, the
methylation change rate of the total 9 DMSs in HCs showed a significant
decreasing trend, whereas significantly increased methylation change
rate was observed in asthma patients (Figure 5A). The methylation change
rate of the total 9 DMSs in asthma was 33.3% ~ 100%,
and the rate in HCs was only 0 ~ 55.6%. Notably, the
change rate of a single DMS in asthma patients was between 47.27% and
89.09%, while it was 1.96% ~ 41.17% in HCs (Figure
5B). Similarly, asthma patients had a higher rate of methylation change.
Statistical results showed that the methylation change rate of the total
9 DMSs was significantly increased in asthma patients (p -value
< 0.1%, Figure 6A). In addition, ROC analysis was implemented
according to the methylation change rate of the 9 DMSs in all samples
(Figure 6B) and there was a higher AUC compared to previous method
(AUC=0.98). Moreover, the PCA analysis results also indicated that the
methylation change rate of 9 DMSs could better distinguish asthma
patients from HCs (Figure 6C).