Normalization Method Utilizing Endogenous Proteins for Quantitative Proteomics

We
developed a normalization method utilizing the expression levels
of a panel of endogenous proteins as normalization standards (EPNS
herein). We tested the validity of the method using two sets of tandem
mass tag (TMT)-labeled data and found that this normalization method
effectively reduced global intensity bias at the protein level. The
coefficient of variation (CV) of the overall median was reduced by
55% and 82% on average, compared to the reduction by 72% and 86% after
normalization using the upper quartile. Furthermore, we used differential
protein expression analysis and statistical learning to identify biomarkers
for colorectal cancer from a CPTAC data set. The expression changes
of a panel of proteins, including NUP205, GTPBP4, CNN2, GNL3, and
S100A11, all of which highly correlate with colorectal cancer. Applying
these five proteins as model features, random forest modeling obtained
prediction results with the maximum AUC of 0.9998 using EPNS-normalized
data, comparing favorably to the AUC of 0.9739 using the raw data.
Thus, the normalization method based on EPNS reduced the global intensity
bias and is applicable for quantitative proteomic analysis.