A new approach to epigenome-wide discovery of non-invasive methylation biomarkers for colorectal cancer screening in circulating cell-free DNA using pooled samples
Gallardo-Gómez et al. Clinical Epigenetics
A new approach to epigenome-wide discovery of non-invasive methylation biomarkers for colorectal cancer screening in circulating cell-free DNA using pooled samples
María Gallardo-Gómez 0 3
Sebastian Moran 2
María Páez de la Cadena 0 3
Vicenta Soledad Martínez-Zorzano 0 3
Francisco Javier Rodríguez-Berrocal 0 3
Mar Rodríguez-Girondo 1 7
Manel Esteller 2
Joaquín Cubiella 6
Luis Bujanda 5
Antoni Castells 4
Francesc Balaguer 4
Rodrigo Jover 8
Loretta De Chiara 0 3
0 Department of Biochemistry, Genetics and Immunology, Centro Singular de Investigación de Galicia (CINBIO), University of Vigo , Campus As Lagoas-Marcosende s/n, 36310 Vigo , Spain
1 Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre , Leiden , The Netherlands
2 Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL) , Barcelona , Spain
3 Department of Biochemistry, Genetics and Immunology, Centro Singular de Investigación de Galicia (CINBIO), University of Vigo , Campus As Lagoas-Marcosende s/n, 36310 Vigo , Spain
4 Gastroenterology Department, Hospital Clínic, IDIBAPS, CIBERehd, University of Barcelona , Barcelona , Spain
5 Department of Gastroenterology, Instituto Biodonostia, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Universidad del País Vasco (UPV/EHU) , San Sebastián , Spain
6 Department of Gastroenterology, Complexo Hospitalario Universitario de Ourense, Instituto de Investigación Biomédica Galicia Sur, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd) , Ourense , Spain
7 SiDOR Research Group and Centro de Investigaciones Biomédicas (CINBIO), Faculty of Economics and Business Administration, University of Vigo , Vigo , Spain
8 Department of Gastroenterology, Hospital General Universitario de Alicante , Alicante , Spain
Background: Colorectal cancer is the fourth cause of cancer-related deaths worldwide, though detection at early stages associates with good prognosis. Thus, there is a clear demand for novel non-invasive tests for the early detection of colorectal cancer and premalignant advanced adenomas, to be used in population-wide screening programs. Aberrant DNA methylation detected in liquid biopsies, such as serum circulating cell-free DNA (cfDNA), is a promising source of non-invasive biomarkers. This study aimed to assess the feasibility of using cfDNA pooled samples to identify potential serum methylation biomarkers for the detection of advanced colorectal neoplasia (colorectal cancer or advanced adenomas) using microarray-based technology. Results: cfDNA was extracted from serum samples from 20 individuals with no colorectal findings, 20 patients with advanced adenomas, and 20 patients with colorectal cancer (stages I and II). Two pooled samples were prepared for each pathological group using equal amounts of cfDNA from 10 individuals, sex-, age-, and recruitment hospitalmatched. We measured the methylation levels of 866,836 CpG positions across the genome using the MethylationEPIC array. Pooled serum cfDNA methylation data meets the quality requirements. The proportion of detected CpG in all pools (> 99% with detection p value < 0.01) exceeded Illumina Infinium methylation data quality metrics of the number of sites detected. The differential methylation analysis revealed 1384 CpG sites (5% false discovery rate) with at least 10% difference in the methylation level between no colorectal findings controls and advanced neoplasia, the majority of which were hypomethylated. Unsupervised clustering showed that cfDNA methylation patterns can distinguish advanced neoplasia from healthy controls, as well as separate tumor tissue from healthy mucosa in an independent dataset. We also observed that advanced adenomas and stage I/II colorectal cancer methylation profiles, grouped as advanced neoplasia, are largely homogenous and clustered close together. (Continued on next page)
(Continued from previous page)
Conclusions: This preliminary study shows the viability of microarray-based methylation biomarker discovery using
pooled serum cfDNA samples as an alternative approach to tissue specimens. Our strategy sets an open door for
deciphering new non-invasive biomarkers not only for colorectal cancer detection, but also for other types of cancers.
Colorectal cancer (CRC) is the fourth leading cause of
cancer-related deaths worldwide, accounting for over 1.4
million new cases in 2012 [
]. While diagnosis at early
stages associates with good prognosis and reduced
mortality rates, the detection and removal of premalignant
advanced adenomas (AA) results in the reduction of
CRC incidence . Since neoplastic transformation can
last decades, there is a broad time window for
implementing screening strategies for the detection of
advanced neoplasia (AN: CRC or AA) [
Approaches for CRC screening can be divided into
two groups. Invasive procedures like colonoscopy allow
the examination of the entire colon and the removal of
lesions (polypectomy); however, limitations of this
strategy include considerably low participation rates and high
]. On the other hand, non-invasive methods like
fecal immunological test (FIT) have the advantage of
increased acceptance and adequate specificity, though
sensitivity for colorectal tumors, especially of proximal
location, and AA is moderate to low [
markers are capable of improving CRC screening
adherence, and a large number of candidates have been
reported for CRC diagnosis, reviewed in . Currently, the
most promising is the SEPT9 methylation assay, though
its performance for the detection of early-stage tumors
and AA needs to be improved [
]. Therefore, there is an
imperative need of finding new non-invasive biomarkers
for CRC screening.
Nowadays, it is well-established that not only
genetic alterations but also epigenetic modifications are
involved in CRC development and progression [
The abnormal methylation occurring during colorectal
neoplasia is characterized by promoter
hypermethylation and transcriptional silencing of tumor suppressor
or DNA repair genes [
], coexisting with a global
loss of methylation that leads to chromosomal and
microsatellite instability and oncogene activation [
]. Both promoter hypermethylation and global
hypomethylation are hallmarks of early stages of
colorectal carcinogenesis [
Several methodologies are suitable for
genomewide methylation biomarker discovery, including
whole and reduced genome bisulfite sequencing and
array-based genotyping technology [
epigenome-wide measurements allow a more
successful identification of methylation alterations
related to complex diseases compared to target
studies. The main drawback is the large sample size
needed, which increases project costs. DNA sample
pooling strategies represent an affordable approach
for biomarker discovery, resulting in reduced costs
and increased amount of input DNA when small
amounts are available. Additionally, it has been
reported that pooled samples provide similar results to
individual samples in both genome-wide [
] association studies.
During the last years, it has been demonstrated
that circulating cell-free DNA (cfDNA) present in
liquid biopsies reflects methylation changes originated
in tumor cells [
]. Given the stability of DNA
methylation in body fluids [
], the discovery of
cfDNA methylation markers using serum samples
seems a very attractive alternative to direct the
search of non-invasive biomarkers.
Taking advantage of this fact, we hypothesized that
an array-based epigenome-wide analysis using serum
cfDNA as input could be a novel and affordable
approach for the discovery of a methylation marker
panel with greater diagnostic value, compared to
other indirect strategies using tumor tissue and
mucosa as input DNA. Therefore, in the present study,
we aim to assess the feasibility of hybridizing pooled
serum cfDNA to the MethylationEPIC array to detect
differentially methylated patterns between patients
with advanced neoplasia and individuals with no
Study population and serum samples
Individuals were recruited from the following Spanish
Hospitals: Hospital Donostia (San Sebastián), Complexo
Hospitalario Universitario de Ourense (Ourense), Hospital
Clínic de Barcelona (Barcelona), and Hospital General
Universitario de Alicante (Alicante). Patients’
characteristics are described elsewhere [
]. We carried out a
stratified random sampling using colorectal finding and gender
as stratifying variables. Moreover, age was restricted to
50–75 years and strata were matched by recruitment
hospital and age. We selected from this multicenter cohort 20
individuals with no colorectal findings (NCF), 20
individuals with AA (adenomas ≥ 10 mm, with villous
component or high-grade dysplasia), and 20 CRC cases (7 stage I
and 13 stage II, according to the AJCC staging system
]). Individuals were classified according to the most
advanced lesion after colonoscopy. Lesions were considered
“proximal” when located only proximal to the splenic
flexure of the colon and “distal” when lesions were found only
in the distal colon or in both distal and proximal colons.
Advanced neoplasia (AN) was defined as AA or CRC.
Blood samples were obtained the same day of the
colonoscopy, immediately prior to the procedure.
Blood samples were coagulated and subsequently
centrifuged according to the manufacturer’s instruction for
serum collection. Serum samples were stored at − 20 °C
DNA extraction and sample pooling
We extracted cfDNA from 0.5–1.5 mL of serum using a
phenol-chloroform protocol as described by Clemens et
], with minor modifications, and resuspended in
20 μL sterile water. DNA concentration was determined
for each individual sample using the Qubit dsDNA HS
Assay Kit (Thermo Fisher Scientific, MA, USA), a
fluorimetric assay specific for double-stranded DNA that gives
an accurate measurement of DNA concentration. All
cfDNA samples were stored at − 20 °C.
Two independent pooled samples were constructed
for each pathological group (NCF, AA, and CRC) using
equal amounts of cfDNA from 10 individuals per pool.
The factors considered to match between pools were
gender, age, and recruitment hospital. Table 1 shows
epidemiologic and clinical data of each individual included
in pool A and B (NCF), pool C and D (AA), and pool E
and F (CRC).
Since the preparation of pooled samples is a critical
step that requires high accuracy, cfDNA from each
individual included in a pool was thawed, tempered,
and re-quantified using the Qubit assay. As reported
by previous DNA pooling protocols [
], in order
to avoid inaccuracies derived from pipetting small
volumes, we decided to dilute by a factor of two
samples with more than 10 ng/μL of DNA. Diluted DNA
was measured again.
Once the actual concentration of all the individual
samples of a pool was available, we determined the
sample containing the limiting ng of cfDNA (based
on measured concentration and volume). Based on
this limiting nanogram, we calculated for each of the
nine remaining samples the volume containing the
same nanogram of cfDNA as the limiting sample.
Finally, the pool was constructed by incorporating
into the tube the corresponding volume of each of
the 10 individual samples of the pool. The cfDNA
mixture was allowed to stand for 1 h, and then the DNA
concentration was quantified with the Qubit Assay to
ensure that the final DNA concentration of the pool was as
expected according to the theoretical calculation:
ðlimiting ngÞ n
ðtotal volume of the poolÞ
where n is the number of individuals included in each
pool (10). Pools were considered valid for the Infinium
Methylation Assay protocol when the difference between
expected and measured concentration (Qubit) was less
than 5%. A graphical description of the pooling protocol
is presented [see Additional file 1]. This protocol was
followed for each of the pools included in the study. The
six pooled cfDNA samples were stored at − 20 °C and
were submitted to the Cancer Epigenetics and Biology
Program (PEBC) facilities at the Bellvitge Biomedical
Research Institute for processing.
Epigenome-wide methylation measurements
DNA methylation was analyzed with the Infinium
MethylationEPIC BeadChip microarray (EPIC; Illumina
Inc., CA, USA), that quantitatively measures the
methylation levels of more than 850,000 CpG sites across the
], located in promoter regions and gene
bodies, and also in intergenic enhancer regions identified by
the ENCODE [
] and FANTOM5  projects.
Pooled samples were bisulfite treated in the same batch,
and MethylationEPIC arrays were hybridized according
to manufacturer’s instructions.
Data preprocessing and differential methylation analysis
Data quality control was assessed with the GenomeStudio
V2011.1, based on the internal control probes present on
the array. The preprocessing, normalization, and correction
steps were conducted using the R environment (versions 3.
3.3 and 3.4.0) with Bioconductor packages. The pipeline
was a sequence of R functions adapted from the minfi [
and ChAMP [
] Bioconductor packages. Our dataset was
normalized using the Functional Normalization
implemented in the minfi package. This algorithm does not rely
on any biological assumption and therefore is suitable for
cases where global changes in the methylation levels are
expected, such as in cancer-normal comparisons [
Detection p values were computed with the minfi
package, and mean detection p values were examined
across all samples in order to identify any failed sample.
Probes with a detection p value > 0.01 in at least one
sample were discarded. We filtered out probes
containing a single nucleotide polymorphism (SNP) at the CpG
interrogation site and at the single nucleotide extension
for any minor allele frequency (MAF), and probes
containing a SNP at the probe body for a MAF >5 %,
Age Lesion descriptionc Lesion locationd
10 mm, T, LGD
25 mm, V, LGD
10 mm, TV, LGD
12 mm, T, LGD
10 mm, T, LGD
10 mm, T, LGD
10 mm, TV, LGD
20 mm, T, LGD
15 mm, T, LGD
3 mm, TV, LGD
30 mm, T, LGD
20 mm, TV, LGD
30 mm, TV, LGD
10 mm, TV, LGD
10 mm, TV, LGD
8 mm, TV, LGD
10 mm, V, LGD
20 mm, V, LGD
20 mm, T, LGD
5 mm, TV, LGD
because differential methylation levels can be
confounded with actual polymorphisms in the DNA
sequence. According to the list provided by Pidsley et al.
], cross-reactive probes were removed. Probes
targeting X and Y chromosomes were also discarded.
In accordance with Du et al. [
], methylation levels
were expressed as beta and M values. Beta values were
used for visualization and intuitive interpretation of the
results, and M values were used for the differential
Prior to differential methylation analysis, data was
checked for batch effects across all array runs using the
combat method implemented in the ChAMP package.
Differentially methylated positions (DMP) between NCF and
AN (AA or CRC) were detected with the dmpFinder
function from the minfi package, which uses an F-test for
categorical phenotype comparisons at a probe level. p values
for each probe were corrected for multiple testing using the
Benjamini-Hochberg procedure, with a false discovery rate
(FDR) of 5% to determine significant DMPs.
In silico evaluation of differential methylation
We applied unsupervised clustering approaches to
evaluate the differentially methylated patterns between AN
and NCF pools in an independent dataset. The publicly
available dataset GSE48684 that includes the
methylation data of 64 colorectal tumor biopsies
(adenocarcinomas) and 41 healthy mucosa biopsies, measured with
the Infinium HumanMethylation450 BeadChip array
], was used as a test cohort. This independent
evaluation was limited to the probes shared by 450K and
EPIC arrays due to the absence of colorectal tumor and
mucosa EPIC public datasets.
final cfDNA concentration of the six pooled samples
ranged from 135 to 250 ng.
Results and discussion
DNA pooling methodology
To our knowledge, this is the first pooling-based study
that analyzes the methylation patterns in cfDNA, aiming
to assess the feasibility of liquid biopsy methylation
biomarker discovery using microarray technology in a more
affordable manner compared to individual samples.
DNA sample pooling has been reported as an
efficient tool for genome-wide and high-throughput
association studies [
]. More recently, its potential
utility was highlighted in microarray-based epigenome-wide
association studies (EWAS), as Gallego-Fabrega et al.
reported high correlation of the methylation levels between
pools and individual DNA samples using the Infinium
HumanMethylation450 BeadChip . Taking into account
the limitation that only mean methylation levels can be
obtained from pooled samples, pooling strategy is an accurate
and affordable alternative that can significantly reduce costs
in large EWAS. DNA pooling is also an efficient alternative
when small amounts of DNA are available and when
working with precious samples.
For sample pooling, accurate construction is critical,
and each DNA sample must be equally represented in
the pool. To guarantee the most precise pool
construction, we first tested two different pooling
strategies: diluting all samples to a common concentration
and then mixing equal volumes in a tube as in
previous works [
] or directly adding the same
nanogram of DNA (calculated corresponding volume from
each sample) into the tube. Once test pools were
constructed and DNA concentration measured, we
checked for discrepancies between the actual and the
expected concentration. Variations inferior to 5% of the
expected pooled DNA concentration were found when
using the second protocol; therefore, sample pooling was
performed as described [see Additional file 1].
We prepared two pooled samples for each
pathological group (two pools of individuals with NCF,
two pools of AA patients, and two pools of CRC
patients stages I and II). We included 10 individuals
per pool to ensure an acceptable amount of DNA
input for the microarray analyses and also to reduce
population stratification and the presence of
unobserved confounding variables. The categories
considered to match between pools were gender, age
(median 63.5, range 51–72 years), and recruitment
hospital. The age range was selected based on the
USPSTF guideline recommendation for CRC
screening, targeting individuals from 50 to 75 years [
No statistically significant difference was found in
the mean age between pools (ANOVA, p < 0.05). The
Quality control of methylation data
The methylation levels of 866,836 CpG positions across
the genome in the six pooled samples were measured
using the MethylationEPIC BeadChip. The quality
control based on the internal control probes present on
the array, which include bisulfite conversion efficiency,
hybridization, extension, and staining, among others,
indicates that pooled serum cfDNA methylation data
meets the quality requirements. The QC report is
presented [see Additional file 2] and shows that the signals
observed are much higher than the background signal,
coinciding with what is expected for high-quality DNA.
In relation to CpG detection, all the pools showed more
than 99% of CpG detected correctly (only 2811 probes
presented a detection p value > 0.01 at least in one
sample, and were discarded). The number of probes detected
in each pool was 866,497; 866,021; 865,865; 865,501;
865,778; and 866,463 for pools A, B, C, D, E, and F,
respectively. These results are indicative of a uniform
amplification and hybridization in all the pooled
samples. The proportion of CpG detection observed in our
samples exceeded Illumina Infinium methylation data
quality metrics of the number of sites detected (> 96%
for genomic DNA and > 90% for FFPE samples). A
probable explanation could be the pooling design, as
measuring methylation of 10 individuals in the same assay
would increase the representation of each CpG in the
input DNA. Therefore, no samples were discarded due to
The distribution of methylation levels in pooled
samples presented the expected bimodal distribution for
both beta and M values, with the two peaks indicating
fully methylated and unmethylated states characteristic
of DNA methylation data (Fig. 1a). Then, we evaluated
the distribution of beta values by type I and II probes
separately. As observed in Fig. 1b, all the pools showed
the distribution of type II probes shifted in relation to
type I, as previously reported in the 450K and EPIC data
Once the technical quality of the six pooled sample data is
verified, we performed a differential methylation analysis. In
order to detect differentially methylated positions (DMPs),
we compared the two NCF pools (colonoscopically
confirmed controls) with the two AA pools together with the
two CRC pools (considered as AN). The differential
methylation analysis was performed on the 703,653 probes left
after the filtering step (see the “Methods” section). We first
assessed the global methylation in the NCF and AN groups,
and in the AA and CRC groups. As shown in Fig. 2a, a
lower content of global methylation was observed in AN,
AA, and CRC compared to NCF. In addition, we found
there is no difference in terms of global methylation
between AA and CRC cfDNA pooled samples.
Since the purpose of screening programs include
the detection of early stage CRC together with the
identification and removal of premalignant AA [
grouped AA and CRC in the single group AN for the
analyses. We found a total of 5808 significant DMPs
between the NCF and AN groups, identified with a
FDR of 5% (Fig. 2b). Of these, 1384 presented at least
10% difference in the methylation level between NCF
and AN (|Δbeta| > 0.1): 135 (9.75%) were found
hypermethylated in AN, while 1249 (90.25%) appeared
hypomethylated (Fig. 2c). The distribution of the DMPs
identified according to their location relative to CpG islands
(CGI) and promoter regions is represented in Fig. 2d. The
majority of the differentially hypomethylated CpGs in AN
are located in opensea (78.08%), outside gene promoters,
within regions with no enrichment in CpG content
followed by CGI-shore (10.23%), CGI-shelf (7.50%), and
DNA hypomethylation was the first aberrant
methylation alteration described in several human cancers
(reviewed in [
]). This global loss of genome-wide
methylation was also described long time ago in both
CRC and colorectal adenomas [
], indicating that
global hypomethylation is characteristic of early stages
of colorectal carcinogenesis [
10, 14, 43
hypomethylated blocks were also identified by Timp and
colleagues (2014) using the 450K array. Among the six
different tumor types analyzed, colon cancer tissue
showed the highest proportions of hypomethylation in
opensea, CGI-shelf, CGI-shore, and CGI [
]. In our
work, using pooled serum samples, we found that more than
90% of the DMPs appeared hypomethylated in AN, agreeing
with these previous reports, and suggesting that perhaps
efforts should be centered on hypomethylated candidates to
accomplish a greater discrimination capacity.
Unsupervised clustering performed on DNA
methylation values for the top 1384 DMPs identified is presented
in Fig. 3a, b. These results highlight the differences
between AN and NCF pooled samples and suggest that
differential cfDNA methylation profiles obtained with pooled
samples can discriminate AN from NCF controls.
We further evaluated the DMPs identified in our pooled
serum cfDNA samples with dataset GSE48684 consisting
of tumor and mucosa tissue samples [
] as a test cohort,
restricting the analysis to the 518 DMPs between AN and
NCF with |Δbeta| > 0.1 targeted by probes shared by 450K
and EPIC arrays. The unsupervised clustering shown in
Fig. 4, performed on tumor and mucosa samples from
GSE48684 based on our DMPs, reveals that the
differential methylation patterns found between AN and NCF
cfDNA can also separate tumor tissue from healthy
mucosa samples. It is worth to mention that 24 healthy
mucosa samples from GSE48684 were normal colon
concurrent with CRC and were obtained from the
normal-appearing resection margin of the colorectal
tumor biopsy [
], as represented in Fig. 4b. This can be
related to the fact that a subcluster of mucosa samples
partially overlaps with CRC samples. Though this in silico
verification is limited, a considerable degree of
concordance can be deduced. It should also be mentioned that
discrepancy in the frequencies of methylation alterations
have been reported in tumor and cfDNA, showing the
latter considerably lower frequencies [
arraybased strategies that rely on tissue samples for cfDNA
methylation marker discovery have the inconvenience of
resulting in decreased sensitivity of the selected candidate
markers once tested in serum or plasma, limiting their
utility as non-invasive tests [
]. An alternative
approach for biomarker discovery was accomplished by
Heiss et al. that used whole blood. However, these authors
indicate that the methylation signature identified in
leukocyte DNA may not be specific for CRC, reflecting
immune responses .
The exploratory nature of this study, with a reduced
number of samples, limits further analyses, but offers a
new affordable strategy for biomarker discovery,
providing an alternative approach to tissue biopsy, reducing
costs in microarray-based EWAS. This work should be
followed by new studies that include a greater number
of pooled serum cfDNA samples and a greater range of
colorectal pathologies, allowing a more robust comparison
between methylation profiles. Furthermore, differential
methylation profiles must be validated in independent
serum cfDNA individual samples, using quantitative
realtime techniques, with the aim of finding a serum
methylation panel for CRC diagnosis and screening.
As far as we are concerned, this proof-of-principle study
is the first to evaluate pooled serum cfDNA profiling on
an epigenome-wide scale for CRC biomarker discovery
using the MethylationEPIC array. Our data, although
preliminary, revealed that the whole epigenome is
represented in pooled serum cfDNA samples and that
differentially methylated cfDNA profiles can discriminate
NCF controls from AN cases (AA or CRC). These
results suggest that a pooling strategy using cfDNA may
be a valuable source of novel non-invasive methylation
biomarkers for CRC early detection and screening. Also,
our approach can be translated to the search of
biomarkers for other types of tumors, as an affordable
alternative approach to tissue biopsy.
Additional file 1: Graphical description of the protocol for DNA sample
pooling. aExpected DNA concentration of the pool was calculated as
follows: ðtotal ðvloimluimtinegonfgtÞhen poolÞ where n is the number of individuals
included in each pool (10). (PDF 311 kb)
Additional file 2: GenomeStudio software quality control report based
on the internal control probes present on the array. (PDF 97 kb)
AA: Advanced adenoma; AN: Advanced neoplasia; cfDNA: Circulating cell-free
DNA; CRC: Colorectal cancer; DMP: Differentially methylated position;
FDR: False discovery rate; NCF: No colorectal findings
We would like to thank Leticia Barcia for her support in daily laboratory work
and also Dr. Jezabel Varadé for her advice and tips about the results
This work received funding from Plan Nacional I +D +I 2015-2018 (Acción
Estratégica en Salud) Instituto de Salud Carlos III (Spain)-FEDER (PI15/02007),
“Fundación Científica de la Asociación Española contra el Cáncer”
(GCB13131592CAST), and support from Centro Singular de Investigación de
Galicia (Consellería de Cultura, Educación e Ordenación Universitaria) (ED431G/02,
Xunta de Galicia and FEDER-European Union). María Gallardo-Gómez is supported
by a predoctoral fellowship from Ministerio de Educación, Cultura y Deporte
(Spanish Government) (FPU15/02350).
Availability of data and materials
The EPIC data from all the pooled samples generated and analyzed during
this study has been deposited in the NCBI Gene Expression Omnibus (GEO)
(www.ncbi.nlm.nih.gov/geo) and are accessible through GEO Series
accession number GSE110185.
LD, VSMZ, and SM conceived and designed the study. LD, MP, FJRB, and ME
supervised the study. JC, LB, AC, FB, and RJ clinical advise for the study
design, collection, and management of clinical data. MGG, MRG, and MP
contributed to the experimental design. MGG, LD, and SM contributed to
the sample preparation and data acquisition. LD, MGG, VSMZ, and SM
performed the analysis and interpretation of data. MGG, LD, FJRB, and MP
prepared the manuscript. All authors critically reviewed and approved the
Ethics approval and consent to participate
All individuals provided written informed consent, and the study followed
the ethical and clinical practices of the Spanish Government and the Helsinki
Declaration, and was approved by the Galician Ethical Committee for Clinical
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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