Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma
Cellular Signalling,
Год журнала:
2024,
Номер
117, С. 111077 - 111077
Опубликована: Фев. 2, 2024
Язык: Английский
Identification of UNC5B as a novel aggressive biomarker for osteosarcoma based on basement membrane genes
Gene,
Год журнала:
2024,
Номер
930, С. 148871 - 148871
Опубликована: Авг. 19, 2024
The
prognosis
of
patients
with
metastatic
osteosarcoma
is
poor,
and
the
variation
basement
membrane
genes
(BMGs)
associated
cancer
metastasis.
However,
role
BMGs
in
has
been
poorly
studied.
were
collected
differentially
expressed
(DE-BMGs)
found
through
difference
analysis.
DE-BMGs
further
screened
by
univariate
Cox
regression
Lasso
analyses,
six
key
identified
defined
as
signatures
(BMGS).
Then,
BMGS
was
used
to
construct
risk
score
system,
divided
into
high-
low-risk
groups
based
on
median
score.
Single-sample
gene
set
enrichment
analysis
(ssGSEA)
ESTIMATE
scores
investigate
differences
immune
infiltration
between
two
scoring
groups.
Additionally,
we
investigated
whether
UNC5B
affects
various
features
tumors
bioinformatic
involved
multiple
biological
functions
cells
wound
healing
assay,
transwell
western
blot.
reliably
predicts
metastasis,
patient
prognosis,
immunity.
expression
elevated
osteosarcoma,
correlated
characteristics
such
infiltration,
drug
sensitivity.
In
vitro
assays
showed
that
knockdown
reduced
cells'
capacity
for
migration
invasion,
EMT
process.
A
novel
system
can
effectively
predict
developed
validated.
this
one
aggressive
biomarkers
osteosarcoma.
Язык: Английский
Circadian rhythm genes contribute to the prognosis prediction and potential therapeutic target in gastric cancer
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 25, 2024
The
role
of
circadian
rhythm
genes
(CRGs)
in
gastric
cancer
(GC)
is
poorly
understood.
This
study
aimed
to
develop
a
CRG
signature
improve
understanding
prognosis
and
immunotherapy
responses
GC
patients.
We
integrated
the
Cancer
Genome
Atlas-Stomach
adenocarcinoma
(TCGA-STAD)
dataset
with
CRGs
prognostic
for
GC.
signature's
predictive
ability
was
validated
using
Kaplan-Meier
ROC
curves.
CIBERSORT
algorithm
evaluated
immune
cell
proportions,
tumor
dysfunction
exclusion
score,
as
well
phenotype
determined
response
STAD
Finally,
we
assessed
expression
real-time
quantitative
polymerase
chain
reaction.
developed
4-CRG
STAD,
demonstrating
accurate
ability.
low-risk
group
showed
increased
B
memory
CD8
+
T
cells,
decreased
M2
Macrophages
compared
high-risk
group.
Patients
had
higher
likelihood
benefiting
from
immunotherapy.
Additionally,
tissues
exhibited
elevated
OPN3
TP53
adjacent
tissue.
successfully
established
CRGs,
accurately
predicting
immunotherapeutic
among
patients,
providing
insights
development
personalized
therapeutic
strategies
these
Язык: Английский