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2014 ; 120
(24
): 3902-13
Nephropedia Template TP
gab.com Text
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English Wikipedia
Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6,
myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal
adenocarcinoma
#MMPMID25100294
Zaidi AH
; Gopalakrishnan V
; Kasi PM
; Zeng X
; Malhotra U
; Balasubramanian J
; Visweswaran S
; Sun M
; Flint MS
; Davison JM
; Hood BL
; Conrads TP
; Bergman JJ
; Bigbee WL
; Jobe BA
Cancer
2014[Dec]; 120
(24
): 3902-13
PMID25100294
show ga
BACKGROUND: Esophageal adenocarcinoma (EAC) is associated with a dismal
prognosis. The identification of cancer biomarkers can advance the possibility
for early detection and better monitoring of tumor progression and/or response to
therapy. The authors present results from the development of a serum-based,
4-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker
panel for EAC. METHODS: A vertically integrated, proteomics-based biomarker
discovery approach was used to identify candidate serum biomarkers for the
detection of EAC. Liquid chromatography-tandem mass spectrometry analysis was
performed on formalin-fixed, paraffin-embedded tissue samples that were collected
from across the Barrett esophagus (BE)-EAC disease spectrum. The mass
spectrometry-based spectral count data were used to guide the selection of
candidate serum biomarkers. Then, the serum enzyme-linked immunosorbent assay
data were validated in an independent cohort and were used to develop a
multiparametric risk-assessment model to predict the presence of disease.
RESULTS: With a minimum threshold of 10 spectral counts, 351 proteins were
identified as differentially abundant along the spectrum of Barrett esophagus,
high-grade dysplasia, and EAC (P<.05). Eleven proteins from this data set were
then tested using enzyme-linked immunosorbent assays in serum samples, of which 5
proteins were significantly elevated in abundance among patients who had EAC
compared with normal controls, which mirrored trends across the disease spectrum
present in the tissue data. By using serum data, a Bayesian rule-learning
predictive model with 4 biomarkers was developed to accurately classify disease
class; the cross-validation results for the merged data set yielded accuracy of
87% and an area under the receiver operating characteristic curve of 93%.
CONCLUSIONS: Serum biomarkers hold significant promise for the early, noninvasive
detection of EAC.
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