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Table 4 Summary of logistic regression values for biomarkers predicting group status

From: Shotgun proteomics coupled to nanoparticle-based biomarker enrichment reveals a novel panel of extracellular matrix proteins as candidate serum protein biomarkers for early-stage breast cancer detection

 

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

  1. 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
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Breast Cancer Research

ISSN: 1465-542X

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