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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