Scientific Reports · 2015 · 11 citations · 19 references
Life SciencesBiochemistryClinical SciencesInflammationKidney TransplantationProteomicsClinical ProteomicsInflammatory BiomarkersProtein IdentificationProteomic BiomarkersProteomic ProfilingTransplant BiomarkersC-reactive Protein TestingInflammation-related Protein ChangesLogistic RegressionMultivariable Regression AnalysisPlasma Protein BiomarkersPlasma ProteomicsProtein Biomarker QuantificationReclassification
The objective of the study was to define the specific plasma protein signature that predicts the increase of the inflammation marker C-reactive protein from index day to next-day using proteome analysis and novel bioinformatics tools. We performed a prospective study of 91 incident kidney transplant recipients and quantified 359 plasma proteins simultaneously using nano-Liquid-Chromatography-Tandem Mass-Spectrometry in individual samples and plasma C-reactive protein on the index day and the next day. Next-day C-reactive protein increased in 59 patients whereas it decreased in 32 patients. The prediction model selected and validated 82 plasma proteins which determined increased next-day C-reactive protein (area under receiver-operator-characteristics curve, 0.772; 95% confidence interval, 0.669 to 0.876; P < 0.0001). Multivariable logistic regression showed that 82-plex protein signature (P < 0.001) was associated with observed increased next-day C-reactive protein. The 82-plex protein signature outperformed routine clinical procedures. The category-free net reclassification index improved with 82-plex plasma protein signature (total net reclassification index, 88.3%). Using the 82-plex plasma protein signature increased net reclassification index with a clinical meaningful 10% increase of risk mainly by the improvement of reclassification of subjects in the event group. An 82-plex plasma protein signature predicts an increase of the inflammatory marker C-reactive protein.
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C-reactive protein: a critical update
Mark B. Pepys, Gideon M. Hirschfield · Journal of Clinical Investigation · 2003 · 3.4K citations · Full text
Use of proteomic patterns in serum to identify ovarian cancer
Emanuel F. Petricoin, Ali M. Ardekani, Ben A. Hitt et al. · The Lancet · 2002 · 3.3K citations
Medicine, Gynecology, Oncology +17