Improvements in the search for potential biomarkers by proteomics: application of principal component and discriminant analyses for two-dimensional maps evaluation.
In this study, we evaluated if the application of multivariate analysis on the data obtained from two-dimensional protein maps could mean an improvement in the search for protein markers. First, we performed a classical proteomic study of the differential expression of serum N-glycoproteins in colorectal cancer patients. Then, applying principal component analysis (PCA) we assessed the utility of the 2-D protein pattern and certain subsets of spots as a tool to distinguish control and case samples, and tested the accuracy of the classification model by linear discriminant analysis (LDA). On the other hand we looked for altered spots by univariate statistics and then analysed them as a cluster by PCA and LDA. We found that those proteins combined presented a theoretical sensitivity and specificity of 100%. Finally, the spots with known protein identity were analysed by multivariate methods, finding a subgroup that behaved as the most obvious candidates for further validation trials.
Rodríguez-Piñeiro
Ana María
AM
Departamento de Bioquímica, Genética e Inmunología, Universidad de Vigo, 36310 Vigo, Spain. anarp@uvigo.es
Rodríguez-Berrocal
Francisco Javier
FJ
Páez de la Cadena
María
M
D015415:Biomarkers / Q000032:analysis* / Q000737:chemistry; D002846:Chromatography, Affinity; D016002:Discriminant Analysis; D015180:Electrophoresis, Gel, Two-Dimensional / Q000379:methods*; D008962:Models, Theoretical; D015999:Multivariate Analysis; D025341:Principal Component Analysis / Q000379:methods; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results
D015415:Biomarkers