Identification of crucial genes through WGCNA in the progression of clear cell renal cell carcinoma
Abstract
Background
Due
to
the
limited
clinical
treatment
options
for
clear
cell
renal
carcinoma
(ccRCC),
this
study
aimed
explore
molecular
mechanisms
underlying
ccRCC
and
identify
potential
therapeutic
targets.
Methods
A
series
of
bioinformatics
techniques
were
utilized.
Differentially
expressed
genes
identified
from
Gene
Expression
Omnibus
(GEO)
dataset.
Weighted
gene
co-expression
network
analysis
(WGCNA)
was
employed
isolate
relevant
modules.
Least
absolute
shrinkage
selection
operator
regression
applied
determine
target
genes,
which
subsequently
validated
in
The
Cancer
Genome
Atlas
Program
(TCGA)
Multivariate
Cox
proportional
hazards
model
conducted.
Ontology
Kyoto
Encyclopedia
Genes
Genomes
enrichment
analyses
performed
on
intersection
genes.
relationship
between
immune
cells
explored.
Dual
verification
using
GEO
TCGA
data
carried
out
screen
Results
WGCNA
utilized
This
led
discovery
236
differentially
193
candidate
hub
12
AIF1L
showed
statistical
differences,
with
higher
expression
some
samples.
Enrichment
revealed
these
genes'
implications
tumors.
Twelve
ccRCC-related
identified,
having
diagnostic
value
correlations
cells.
Through
dual
verification,
five
screened
had
unique
characteristics.
Clinical
correlation
suggested
it
might
act
as
a
suppressor
gene.
Differences
tumor
microenvironment
observed
high-
low-expression
groups.
Conclusion
presented
notable
findings.
combination
different
datasets
offered
comprehensive
understanding
promise.
finding
provides
foundation
direction
future
research
ccRCC's
strategies.
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Ноя. 18, 2024
Язык: Английский