Machine learning-derived diagnostic model of epithelial ovarian cancer based on gut microbiome signatures
Journal of Translational Medicine,
Год журнала:
2025,
Номер
23(1)
Опубликована: Март 13, 2025
Prior
studies
have
elucidated
that
alterations
in
gut
microbiota
are
associated
with
a
spectrum
of
tumors
and
metabolic
disorders.
However,
the
diagnostic
value
epithelial
ovarian
cancer
remains
insufficiently
investigated.
A
total
34
patients
diagnosis
(EOC),
15
benign
(TB),
30
healthy
volunteers
(NOR)
were
enrolled
this
study.
Fecal
samples
collected,
followed
by
sequencing
V3–V4
region
16S
rRNA
gene.
The
clinical
data
pathological
characteristics
comprehensively
recorded
for
further
analysis,
PICRUSt2
was
utilized
to
conduct
an
analysis
microbial
functional
predictions,
WGCNA
networks
constructed
integrating
microbiome
data.
LEfSe
employed
identify
markers,
LASSO
SVM
analyses
used
screen
markers
conjunction
Cally
index,
establish
Microbial-Cally
model.
Bootstrap
resampling
internal
validation
model,
whereas
Hosmer–Lemeshow
test
decision
curve
(DCA)
evaluate
performance
Plasma
subjected
untargeted
metabolomics
profiling,
differential
key
metabolites
significantly
altered
cancer.
At
same
time,
Spearman
correlation
study
association
between
metabolites.
supernatants
from
Escherichia
coli
Bifidobacterium
cultures
co-cultured
SKOV3
cells.
Cell
proliferation,
migration,
invasion
evaluated
using
Counting
Kit-8
(CCK-8)
assay,
Transwell
migration
assays.
Apoptosis
assessed
flow
cytometry
fluorescence
signals
Annexin
V
propidium
iodide
(PI)
staining.
Compared
Nor
TB
populations,
individuals
diagnosed
EOC
demonstrated
diminished
diversity
when
contrasted
both
normal
controls
those
presenting
conditions.
Specifically,
relative
abundance
Bilophila,
Bifidobacterium,
other
probiotics
reduced
while
Shigella
marked
enrichment
within
cohort.
Differential
microorganisms
identified
through
application
machine
learning
techniques
delineate
characteristic
profiles
patients.
significant
index
microorganisms.
In
conclusion,
we
biomarkers
alongside
model
cancer,
receiver
operating
(ROC)
Area
Under
Curve
(AUC)
0.976
(95%CI
0.943–1.00),
AUC
obtained
0.974.
revealed
robust
concordance
observed
probabilities
predicted
generated
provided
net
benefit.
233
group
NT
(NOR
TB)
groups.
Among
these,
eight
specific
(HMDB0243492,
C09265,
HMDB0242046,
HMDB0240606,
C04171,
HMDB0060557,
HMDB0252797,
C21412)
exclusively
derived
microbiome.
Notably,
metabolite
HMDB0240606
exhibited
positive
Shigella,
it
showed
negative
Ruminococcus.
vitro
possessed
anti-tumor
activity,
pro-tumor
activity.
This
provides
inaugural
comprehensive
composition
its
among
tumors,
Hunan
province,
China.
Язык: Английский
Altered Bacteria Abundance Is Associated With Outcomes in Head and Neck Squamous Cell Carcinoma
Otolaryngology,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 14, 2025
Abstract
Objective
To
determine
if
microbiome
differences
exist
in
head
and
neck
squamous
cell
carcinoma
(HNSCC)
based
on
high‐risk
pathologic
features,
smoking,
outcomes
using
The
Cancer
Microbiome
Atlas
(TCMA).
Study
Design
Database
study.
Setting
review.
Methods
TCMA
is
a
publicly
available
database
containing
curated,
decontaminated
microbial
profiles
for
tumors
from
1772
patients.
data
were
limited
to
profiles,
survival,
clinicopathologic
features
HNSCC
Phyloseq
objects
created,
low‐read
samples
removed,
differential
abundance
analysis
(DAA)
Analysis
of
Compositions
Microbiomes
with
Bias
Correction
2
(ANCOM‐BC2)
was
performed.
Statistical
done
R
(v4.3.1).
Results
One
hundred
fifty‐six
patients
included
mean
age
59
(std
13,
min
19,
max
90),
72%
male
(n
=
113),
91%
white
140).
Primary
sites
encompassed
oral
cavity
106,
68%),
oropharynx
26,
17%),
larynx/hypopharynx
24,
15%).
For
all
TCMA,
rates
lymphovascular
invasion
17%
26),
perineural
invasion,
34%
53),
microscopic
or
gross
extranodal
extension
(ENE),
19%
30).
DAA
revealed
significant
changes
bacterial
genera
smoking
status,
vital
disease‐specific
survival
(DSS).
Genera
observed
ANCOM‐BC2
include
Scardovia
,
Alloscardovia
Lactobacillus
Corynebacterium
status
DSS.
Conclusion
Changes
the
relative
select
intratumoral
are
associated
adverse
DSS,
HNSCC.
Shifts
need
further
investigation
they
can
provide
any
mechanistic
insight
predictive
role.
Язык: Английский
Progress of tumor-resident intracellular bacteria for cancer therapy
Advanced Drug Delivery Reviews,
Год журнала:
2024,
Номер
214, С. 115458 - 115458
Опубликована: Окт. 9, 2024
Язык: Английский
Intratumoral microbiota: an emerging force in diagnosing and treating hepatocellular carcinoma
Medical Oncology,
Год журнала:
2024,
Номер
41(12)
Опубликована: Окт. 25, 2024
Язык: Английский
Unveiling the distinctive variations in multi-omics triggered by TP53 mutation in lung cancer subtypes: an insight from interaction among intratumoral microbiota, tumor microenvironment, and pathology
Computational Biology and Chemistry,
Год журнала:
2024,
Номер
113, С. 108274 - 108274
Опубликована: Ноя. 7, 2024
Язык: Английский