Neutrophil extracellular trap related risk score exhibits crucial prognostic value in skin cutaneous melanoma, associating with distinct immune characteristics
Skin Research and Technology,
Год журнала:
2024,
Номер
30(8)
Опубликована: Авг. 1, 2024
Abstract
Background
Neutrophil
extracellular
traps
(NETs)
are
related
to
the
prognosis
of
cancer
patients.
Nevertheless,
potential
prognostic
values
NETs
in
skin
cutaneous
melanoma
(SKCM)
remains
largely
unknown.
Materials
and
methods
The
NET‐related
gene
signature
was
constructed
by
LASSO
Cox
regression
analysis
using
TCGA‐SKCM
cohort.
overall
survival
(OS)
immune
status
SKCM
patients
between
high‐
low‐NET
score
(high‐score,
low‐score)
groups
were
explored.
scRNA‐seq
dataset
GSE115978
used
understand
role
NET
at
single
cell
resolution.
Results
A
five
genes‐based
(TLR2,
CLEC6A,
PDE4B,
SLC22A4
CYP4F3)
as
model
for
SKCM.
OS
with
low‐score
better
than
that
high‐score.
Additionally,
negatively
associated
infiltration
some
cells
(e.g.
type
I
Macrophages,
CD8‐T
cells,
CD4‐T
cells).
Moreover,
high‐score
had
low
stromal,
ESTIMATE
scores.
Furthermore,
drug
sensitivity
results
showed
Lapatinib,
Trametinib
Erlotinib
may
have
therapeutic
advantages
Conclusion
We
established
a
found
exhibit
good
predictive
ability
prognosis.
not
only
predict
outcome
SKCM,
but
also
reflect
conditions
Язык: Английский
A Glycolysis and gluconeogenesis-related model for breast cancer prognosis
Cancer Biomarkers,
Год журнала:
2024,
Номер
41(3-4)
Опубликована: Дек. 1, 2024
Background
Breast
cancer
is
a
malignant
tumor
with
high
morbidity
and
mortality,
which
seriously
endangers
the
health
of
women
around
world.
Biomarker-based
exploration
will
be
effective
for
better
diagnosis,
prediction
targeted
therapy.
Objective
To
construct
biomarker
models
related
to
glycolysis
gluconeogenesis
in
breast
cancer.
Methods
The
gene
expression
932
patients
Cancer
Genome
Atlas
(TCGA)
database
was
analyzed
by
Gene
Set
Variation
Analysis
(GSVA)
using
gluconeogenesis-related
pathways.
Differential
genes
were
searched
T-test.
Univariate
Cox
proportional
hazards
model
(COX)
regression,
Least
Absolute
Shrinkage
Selection
Operator
(LASSO)
Multivariate
COX
regression
used
find
clinically
significant
prognostic
survival.
After
that,
constructed
signature
externally
validated
through
Expression
Omnibus
(GEO).
Finally,
nomogram
predict
survival
patients.
In
addition,
analyzing
role
biomarkers
pan-cancer.
Results
A
risk
scoring
associated
developed
validated.
created
2-,
3-,
5-
Conclusions
predictive
accurately
predicted
prognosis
Язык: Английский