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| |
AUC (95% CI)
|
Criterion
|
Sensitivity
|
Specificity
|
|---|
|
Model 1
|
|
LGGSAVISLEGKPL
|
0.86 (0.76–0.96)
|
> 0.25
|
78.95
|
82.93
|
|
HTFMGVVSLGSPSGEVSHPR
|
0.57 (0.40–0.74)
|
> 0.34
|
47.37
|
80.49
|
|
SPFSVAVSPSLDLSK
|
0.52 (0.36–0.68)
|
> 0.32
|
26.32
|
87.80
|
|
Combined
|
0.88 (0.80–0.97)
|
> 0.25
|
89.47
|
80.49
|
|
Model 2
|
|
LVVLGSGGVGK
|
0.62 (0.45–0.78)
|
> 0.47
|
31.58
|
97.56
|
|
VYLFLQPR
|
0.56 (0.38–0.74)
|
> 0.39
|
31.58
|
97.56
|
|
ANLPQSFQVDTSK
|
0.55 (0.38–0.73)
|
> 0.35
|
31.58
|
87.80
|
|
LAQAAQSSVATITR
|
0.79 (0.64–0.93)
|
> 0.37
|
63.16
|
92.68
|
|
Combined
|
0.93 (0.86–1.00)
|
> 0.19
|
100
|
85.37
|
- Model 1 was run on 41 potential peptide biomarkers with p < 0.05. Significant predictors from model 1 were tested using model 2. Logistic regression was used to determine the sensitivity, specificity, and area-under-curve (AUC) of single markers and combined panels of peptide biomarkers, after bootstrapping 1000 samples with 95% confidence intervals for each specified cutoff value of the criterion. CI confidence interval
