Rare Diseases Symptoms Automatic Extraction
Home
A random Abstract
Our Project
Our Team
Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma.
[esophageal adenocarcinoma]
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-
A
6
,
and
protein
S
100
-
A
9
)
biomarker
panel
for
EAC
.
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
.
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
%
.
Serum
biomarkers
hold
significant
promise
for
the
early
,
noninvasive
detection
of
EAC
.
Cancer
2014
.
©
2014
American
Cancer
Society
.
Diseases
Validation
Diseases presenting
"multiparametric risk-assessment model"
symptom
esophageal adenocarcinoma
You can validate or delete this automatically detected symptom
Validate the Symptom
Delete the Symptom