Prognostic DNA methylation markers for sporadic colorectal cancer: a systematic review
Draht et al. Clinical Epigenetics
Prognostic DNA methylation markers for sporadic colorectal cancer: a systematic review
Muriel X. G. Draht 0
Danny Goudkade 0
Alexander Koch 0
Heike I. Grabsch 0
Matty P. Weijenberg
Manon van Engeland 0
Veerle Melotte 0
Kim M. Smits 0
0 Department of Pathology, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center , Maastricht , The Netherlands
Background: Biomarkers that can predict the prognosis of colorectal cancer (CRC) patients and that can stratify high-risk early stage patients from low-risk early stage patients are urgently needed for better management of CRC. During the last decades, a large variety of prognostic DNA methylation markers has been published in the literature. However, to date, none of these markers are used in clinical practice. Methods: To obtain an overview of the number of published prognostic methylation markers for CRC, the number of markers that was validated independently, and the current level of evidence (LoE), we conducted a systematic review of PubMed, EMBASE, and MEDLINE. In addition, we scored studies based on the REMARK guidelines that were established in order to attain more transparency and complete reporting of prognostic biomarker studies. Eighty-three studies reporting on 123 methylation markers fulfilled the study entry criteria and were scored according to REMARK. Results: Sixty-three studies investigated single methylation markers, whereas 20 studies reported combinations of methylation markers. We observed substantial variation regarding the reporting of sample sizes and patient characteristics, statistical analyses, and methodology. The median (range) REMARK score for the studies was 10.7 points (4.5 to 17.5) out of a maximum of 20 possible points. The median REMARK score was lower in studies, which reported a p value below 0.05 versus those, which did not (p = 0.005). A borderline statistically significant association was observed between the reported p value of the survival analysis and the size of the study population (p = 0.051). Only 23 out of 123 markers (17%) were investigated in two or more study series. For 12 markers, and two multimarker panels, consistent results were reported in two or more study series. For four markers, the current LoE is level II, for all other markers, the LoE is lower. Conclusion: This systematic review reflects that adequate reporting according to REMARK and validation of prognostic methylation markers is absent in the majority of CRC methylation marker studies. However, this systematic review provides a comprehensive overview of published prognostic methylation markers for CRC and highlights the most promising markers that have been published in the last two decades.
Biomarker; DNA methylation; Methylation marker; Colorectal cancer; Colon cancer; Prognosis; Survival; Patient outcome; REMARK
Colorectal cancer (CRC) is the third most common form
of cancer and accounts for more than 500,000 deaths
worldwide each year [
]. Overall, the prognosis of CRC
patients is poor with about half of all diagnosed patients
dying as a result of recurrence, metastasized disease, or
]. CRC often develops without
symptoms until it has reached an advanced stage. Prognostic
markers, which can predict the prognosis of CRC
patients and which can stratify high-risk early stage
patients from low-risk early stage patients are urgently
needed for better management of CRC. Evidence-based
results regarding prognostic markers are therefore
essential for better patient management.
CRC patient survival is highly dependent on the tumor
stage at the time of diagnosis. Therefore, the
tumornode-metastasis (TNM) staging system is the gold
standard to determine the prognosis of a CRC patient [
In addition, clinical markers, such as poor tumor
differentiation, vascular, and/or perineural invasion, as well as
molecular markers, such as microsatellite instability
(MSI) status and KRAS or BRAF mutation status can be
]. Since in many years, a vast amount of
epigenetic biomarkers have been identified and described as
promising cancer biomarkers in the scientific literature
]. However, to date, only a few biomarkers have
been validated for clinical use [
]. CRC, in
particular, has often been the topic of epigenetic biomarker
research, leading to the identification of methylation
markers for early detection of CRC, prediction of
prognosis, and/or treatment response [
]. At the
moment some methylation markers for early detection
of CRC (such as SEPT9, NDRG4, and BMP3) have been
incorporated in the FDA-approved commercial tests, Epi
proColon® and Cologuard, respectively [
However, for prognostic or predictive purposes, no
methylation marker for colon and/or rectal cancer has
made the translation to a clinically applicable
biomarker. The reasons for the lack of translation of
biomarkers, prognostic, or other, into clinical practice,
have already been recognized previously, with many
different research groups providing possible
explanations and/or solutions for these problems, such as
poorly selected biospecimens, not-clinically relevant
sample series, and underpowered sample series, as
well as lack of validation and reproducibility of the
biomarker assay [
]. In 2005, the REporting
recommendations for tumor MARKer prognostic
studies (REMARK) guidelines were published in an
effort to improve the reporting of biomarker studies
and subsequently increase the number of prognostic
biomarkers that can be used in clinical practice [
Nevertheless, there is evidence that adherence to
REMARK is still suboptimal [
A comprehensive overview of potentially promising
prognostic epigenetic biomarkers for CRC is lacking.
Furthermore, the current amount of available
information only leads to more confusion instead of
contributing to answering the question, which biomarker should
be further developed for translation. Here, we provide a
comprehensive overview of the currently available
evidence on prognostic DNA methylation markers for
CRC and review the quality of these studies using the
REMARK guidelines as a tool.
Search strategy and study eligibility
A literature review was performed covering English
language articles in PubMed, EMBASE, and MEDLINE
until May 2017 using the following search terms: DNA
methylation, biomarker, cancer, colon, colorectum,
colorectal, survival, patients outcome, prognosis
(Additional file 1). Published studies were eligible to be
included in our analysis if colon, rectal, or colorectal
cancer patient prognosis was analyzed stratifying
patients by methylation status of the marker. Only
original articles (no reviews, editorials, conference
abstracts, etc.) were considered (Fig. 1). Studies were
included if overall survival (OS), disease-specific survival
(DSS), disease-free survival (DFS), recurrence-free
survival (RFS), or any other endpoint were reported and
if results were presented as Kaplan-Meier plots, relative
risks, or hazard ratios (HRs) with corresponding 95%
confidence intervals (95% CI). We did not restrict our
search to specific patient characteristics (such as age
group, sex, ethnicity, and tumor type). Studies were
excluded if the tumor was hereditary; prognosis was not
analyzed by one of the abovementioned methods; studies
were focusing on the prognostic influence of the CpG
island methylator phenotype (CIMP and microsatellite
instability (including MINT loci)), as this has been the
topic of multiple systematic reviews of our and other
research groups [
]; and studies were on
methylated miRNAs and LINE-1, as our focus in this review
was directed to CpG islands of protein-coding genes.
Data extraction was performed by two independent
researchers (MD and DG) using a standardized data
registration form in which the following items were
recorded: marker of study, sample size, cancer type
(colon, rectum, or CRC), sample type (primary tissue,
serum, mucosa, blood, lymph node tissue, peritoneal
lavage, or stool), stage (tumor-node-metastasis (TNM)
staging, according to editions mentioned in original
paper, or Dukes’ staging), study design, year of collection
of samples, number of patients in survival analyses,
endpoints, subgroup analysis, p value, and hazard ratio
(HR) with corresponding confidence interval. This
systematic review was conducted according to the Preferred
Reporting Items for Systematic Reviews and
MetaAnalyses (PRISMA) statement, where applicable [
Eligible studies were scored (MD and DG) based on the
REMARK criteria [
], which summarizes 20 items for good
reporting of a prognostic biomarker study (Additional file 2).
In case of complete reporting according to the guidelines, a
study was given 1 point, in case of incomplete reporting, a
study was given 0.5 points, and in case of lack of reporting
any aspect of the guideline item, a study was given 0 points.
The maximum score was 20 points (all items adequately
reported). Interobserver variation of scores was solved by
mutual consensus. The risk of potential bias and
confounders was analyzed per study using the information
obtained with the REMARK scores. If a study obtained ≥ 1.5
points for REMARK criterion #2 (“patient characteristics”)
and #6 (“sample selection and follow-up”), the risk of
selection bias was low. In case of less than 1.5 points, the risk of
potential selection bias is increased. Bias regarding the assay
method (measurement bias) was assessed similarly using
REMARK criteria #5 (“assay method”) and #11 (“handling
of marker values”). The risk of bias regarding outcome
assessment (measurement bias) was scored based on
REMARK criterion #7 (“clinical endpoint definition”). In
case of complete reporting (score = 1), the risk of bias is low,
as compared to partial or lack of reporting, which increases
a potential risk of bias. The presence of potential
confounding factors was assessed using REMARK criterion
#16 (“multivariable analysis”). In case of 1 point, the risk of
confounding factors is lower as compared to studies that did
not perform or report a multivariable analysis. In order to
investigate whether the REMARK score or the total number
of patients included in the survival analysis correlates with
the reported significance of the marker (p value), we
performed a regression analysis and determined the
Pearson’s correlation coefficient (r). We compared the
REMARK scores of studies reporting a significant
finding versus studies reporting a non-significant
finding using a Mann-Whitney test. We used the
statistical programming language R (version 3.3.1) to
perform all analyses.
We prepared forest plots for methylation markers that
were investigated in two or more study series. If
available, we reported HRs for overall and subgroup
analysis, such as single TNM stages or mutation status.
Univariate HRs were used, unless multivariate HRs were
available. If multiple HRs were available, the most
adjusted HR was depicted in the plot. In order to give a
complete overview, p values are depicted in the forest
plot, if only Kaplan-Meier results were available. We
used the statistical programming language R (version
3.3.1) to perform all analyses and generate the figures.
Level of evidence
The level of evidence (LoE) can be determined using an
evidence-ranking scheme such as GRADE [
] or the
OCEBM levels [
] in which level I represents definitive
evidence, level IV represents (very) weak evidence, and
the remaining levels a degree in between. Even though
these rankings do not provide a definitive judgment on
the quality of the provided evidence, they do offer a
valuable indication. To give an overview of the current
evidence on prognostic epigenetic biomarkers in CRC,
we classified a LoE to each marker, or marker panel,
investigated in two or more independent study series,
using a ranking scheme adapted for biomarkers [
the OCEBM schemes [
We initially identified 2063 studies for potential
inclusion using our search strategy (Additional file 1). We
excluded 1869 studies mainly because they were either
not original studies or not relevant to prognosis or
colorectal cancer, colon cancer, or rectal cancer. We checked
full-text articles of the remaining 194 studies, of which
66 were excluded, for prognosis was not pertinent to
methylation, prognosis was associated with CIMP,
methylation of non-protein-coding genes was
investigated (miRNAs, LINE-1, MINT loci), or only in silico
data were described (Fig. 1). A total of 83 studies were
included in this systematic review.
Study characteristics are summarized in Additional file 3.
Sixty-three (76%) studies reported results from single
methylation markers, and 20 (24%) studies included
multiple markers. In total, 123 different methylation
markers were investigated (Additional file 3). Studies
were published between 1999 and 2017. Median (range)
sample size was 127 patients (30 to 1105 patients).
Seventy-four (89%) studies investigated CRC, six (7%)
studies investigated colon cancer only, and three (4%)
studies investigated rectal cancer only. Sixty-eight (82%)
studies used formalin-fixed or fresh-frozen primary
tissue for biomarker analyses, 10 (12%) studies used
blood (serum or plasma), two (3%) studies used normal
mucosa, one study (1%) used peritoneal lavage fluid, and
in one study (1%), the tissue used in the analyses was
not specified. Thirteen (16%) studies included patients
with the same single TNM (or Dukes) stage, 68 (82%)
studies included patients with two or more disease
stages. For two (3%) studies, the TNM (or Dukes) stage
of the included patients was not specified. Eighty-two
studies (99%) used Cox proportional hazard analyses,
Kaplan-Meier plots, or both to assess the relation with
overall, regression-free, or disease-specific survival. For
one study (1%), the statistical method was not described.
We evaluated studies according to the REMARK
checklist and assigned a score between 0 and 20 to each study
(Additional file 4). The scores ranged from 4.5 points to
17.5 points with a median score of 10.7 (Fig. 2a). Among
the 83 studies that were scored according to the
REMARK criteria, we observed a large variation in the
amount of information given for the specific criterion.
For only two criteria (#1“state marker, objectives, and
hypotheses” and #5 “specify assay details”), complete or
partial information was given for all studies. For the
other criteria, these numbers ranged from 10% (#11
“specify marker values in analyses and discuss cut-off
points”) to 99% (#19 “interpretation of results and study
limitations”) (Fig. 2b). Full quality scores could only be
given for one REMARK criterion (#1 “state marker,
objectives, and hypotheses”) for the majority of the
studies (98%). For all other criteria, the percentage of studies
obtaining full quality scores ranged from 1% (#11
“specify marker values in analyses and discuss cutoff points”)
to 70% (#4 “describe biological material”) (Fig. 2b).
Almost none of the studies sufficiently addressed how
marker values were handled in the analyses or presented
cutoffs (10%). Less than half of the studies provided
complete or partial information on candidate markers
initially considered for the study (28%), reported a
rationale for sample size (28%), reported estimated
effects with corresponding confidence intervals of the
marker and other prognostic variables in the analyses
(47%), or reported further investigations such as
checking assumptions of proportional hazards (31%). A
complete overview of the REMARK scores for the
different studies per REMARK criterion is presented in
Additional file 4. The risk of bias of each included study
is summarized in Additional file 5.
Since it is more likely that studies reporting
statistically significant results get published [
were interested whether there is an association
between REMARK score and reported p value and
whether inadequately reported studies tend to more
frequently report significant results. Although
REMARK scores varied between the individual
studies, p values < 0.05 for the association between the
methylation marker and prognosis were more often
reported in studies with lower REMARK scores, as
compared to studies with average to high REMARK
scores (Fig. 3a, p = 0.005), although this was not
seen in the Pearson’s correlation coefficient (Fig. 3b;
r = 0.0543, p = 0.499). Whereas almost half (46%) of
the 83 selected studies exclusively reported
significant results, 24% of all studies reported
nonsignificant methylation marker results and 30%
described statistically significant, as well as
nonsignificant results. Often, methylation markers were
reported in small study populations (median n =
127.5), increasing the possibility that reported
prognostic effects cannot be validated in other study
populations. Therefore, we were interested whether
there is an association between the reported p value
and the number of patients included in the survival
analysis. A borderline statistically significant
correlation was observed between the reported p value of
the survival analysis and the size of the study
population (n) (Fig. 3c; p = 0.051).
Prognostic marker findings
Additional file 3 shows the impact of methylation
markers on prognosis in the included studies [
The majority of markers were investigated in a single
study without any internal or external validation. As
unvalidated results are at higher risk to represent chance
findings, validation in at least one independent
population is needed to draw any conclusion, even preliminary,
for these markers. Therefore, we only prepared forest
plots for methylation markers that were investigated by
two or more studies and/or where internal validation in
an independent series was performed (i.e., IGFBP3,
CDKN2A (p16), WNT5a, HPP1, RET, TFPA2E, HLTF,
EVL, CD109, NRCAM, FLNC, BNIP3, MLH1, MGMT,
RASSF1A, CDKN2A (p14), APC, CHFR, SEPT9, and one
multimarker panel; Fig. 4).
Overall, studies assessing IGFBP3 methylation showed
similar correlations with a poor prognosis in CRC
patients. Yi et al. firstly investigated IGFBP3
hypermethylation as a prognostic marker in three different study
]. Although a significant association was
found with poorer OS in the two smaller populations
(n = 147, n = 72; HR 2.58 95% CI 1.37–4.87, HR 2.06
95% CI 1.04–4.09, respectively), no association was
found in a cohort of 558 patients (data not shown in
original article). In a subgroup analysis, IGFBP3
hypermethylation was found to be a prognostic factor in three
independent studies, all focusing on TNM stages II–III,
even though every study used a different endpoint (RFS,
DFS, and OS) (HR 6.46 95% CI 1.51–27.70; HR 2.40
95% CI 1.10–5.25; HR 0.48 95% CI 0.26–0.87; HR 2.04
95% CI 1.34–3.09) [
48, 89, 90
CDKN2A (p16) was by far the most often studied
biomarker, with ten studies in TNM stage I-IV [
59–61, 63–65, 70, 73
] and 13 studies in different
50–53, 55, 56, 58, 62, 69, 73
]. Only one study in
TNM stages I–IV showed a statistically significant
association between CDKN2A methylation and outcome
(HR 1.66 95% CI 1.2–2.3) [
]. Studies including
subgroup analyses however showed statistically significant
associations between CDKN2A methylation and a poor
prognosis in several different subgroups. The studies by
Kohonen-Corish et al., Wettergren et al. 2008, and
Esteller et al. 2001 in TNM stages I–III and Dukes’ stage
A–C patients describe a statistically significant association
(HR 2.60 95% CI 1.20–5.70; HR 2.90 95% CI 1.30–6.20;
HR 3.00 95% CI 1.10–8.10, respectively) [
51, 55, 62
although the study of Sanz-Casla et al. could not confirm
this (p = 0.09) [
]. The studies of Liang et al. [
Wettergren et al. [
], Mitomi et al. [
], and Maeda et al.
], focusing on TNM stage II or Dukes’ B patients alone
or in a larger subgroup of TNM stages II–IV or Dukes’
B–C patients, also show statistically significant
associations with a poor prognosis (p = 0.0001; HR 4.70 95% CI
1.10–19.50; HR 3.38 95% CI 1.67–6.84; p = 0.022,
respectively). This could not be confirmed by the study of Cleven
et al., which was conducted in TNM stage II,
microsatellite stable (MSS), and BRAFwt patients [
]. A significant
association with a poor prognosis was also reported for
Dukes’ C patients with mutated KRAS (HR 2.60 95% CI
], but not in another study focusing on
Dukes’ C patients or TNM stage III, MSS, and BRAFwt
Methylation of WNT5a was only studied in one
publication. Rawson et al. investigated methylation of WNT5a
in two large independent series, showing no association
between methylation of WNT5a and prognosis (HR 1.0
95% CI 0.6–1.7; HR 0.9 95% CI 0.6–1.3) [
Four studies reported on the association between
HPP1 hypermethylation and prognosis with conflicting
]. In TNM stages I–IV, a statistically
significant association was shown with OS (HR 5.10 95%
CI 2.20–11.60 and Kaplan-Meier p value < 0.0001)
]. Subgroup analyses of the studies by Philipp
et al. showed a statistically significant association between
OS and HPP1 hypermethylation in TNM stage IV only
(Kaplan-Meier p value 0.0003 and < 0.0001, respectively)
]. The study by Herbst et al. only showed a
(See figure on previous page.)
Fig. 4 Forest plots of reported methylation markers in colorectal cancer studies. Forest plots were prepared for methylation markers that were
reported in two or more publications or study populations. The hazard ratios (HR) are sorted according to the REMARK score. HRs with a
statistically significant association are depicted with a solid line; HRs of reported markers with no significant association are depicted with a
dotted line; HRs of subgroup analyses are depicted in blue. Univariate HRs and confidence intervals (CI) are reported unless multivariate HRs were
available (a). As for IGFBP3 and TFAP2E the HRs of the study of Perez-Carbonell et al.  and Zhang et al. [
], respectively, were both associated
with worse survival. For this figure, the HR was reversed for visualization purposes (b). A multivariate HR for BNIP3 methylation was available in
the study of Shimizu et al., however was not statistically significant (c)
borderline statistically significant association with RFS
(HR 2.90 95% CI 1.00–8.30) [
]. A recent prospective
study by Herbst et al., including 467 TNM stage IV
patients, reported a significant poorer OS in patients with
HPP1 methylation (HR 1.86 95% CI 1.37–2.53 for
univariate analysis) [
]. After one administration with
combination chemotherapy, a multivariate analysis still predicted
a poorer outcome for HPP1 methylated patients (HR 2.08
95% CI 1.54–2.80).
Methylation of RET was studied in three independent
patient series reported in one publication [
there was no association with disease-specific survival in
the total population of TNM stage I–IV patients, a
significant association with poorer OS was found in two
TNM stage II patient series (HR 2.51 95% CI 1.42–4.43;
HR 1.91 95% CI 1.04–3.53) and one TNM stage III
patient series (HR 2.04 95%-CI 1.23–3.37).
Hyper- as well as hypomethylation of TFAP2E was
investigated in three studies, of which two studies
strikingly reported similar associations between hyper- and
hypomethylation and prognosis. The study by Zhang et
al. assessed the influence of TFAP2E hypermethylation
on prognosis in TNM stage I-IV patients, showing a
significant association between TFAP2E hypermethylation
and a favorable prognosis (HR 0.49 95%-CI 0.34–0.69)
]. A subgroup analysis by Park et al. of TNM stage
I-III patients confirmed this association (HR 2.24 95%-CI
1.10–4.56; HR converted for figure) [
]. In the study of
Beggs et al. hypermethylation of TFAP2E was not
associated with survival (HR 10.8 95%-CI 1.08–2.83), however a
survival benefit was found in patients with TFAP2E
hypomethylation (HR 0.34 95%-CI 0.12–0.97) [
Methylation of HLTF was investigated in five studies
with conflicting results. A statistically significant
association between HLTF hypermethylation and OS in TNM
stage I-IV CRC patients was suggested by Wallner et al.
and Philipp et al. (HR 3.0 95% CI 1.40–6.40 and
KaplanMeier p value 0.0008) [
], whereas the study of
Cleven et al., using CSS as an endpoint, could not
confirm this finding (HR 1.05 95%-CI 0.64–1.74) .
Subgroup analyses of TNM stage II and III patients
showed no statistically significant association between
HLTF hypermethylation and OS or CSS [
TNM stages I and IV, and I-III however, a statistically
significant association was observed between HLTF
hypermethylation and OS or RFS (Kaplan-Meier
p-values 0.0007 and 0.0005, and HR 2.50 95%-CI 1.10–
5.60, respectively) [
Next to IGFBP3, Yi et al. also investigated four other
genes (EVL, CD109, NRCAM, and FLNC) in three
independent populations [
]. However, only methylation of
EVL appeared to have a significant association with
worse OS in TNM stage I–IV colon cancer patients (HR
2.48 95% CI 1.07–5.72; HR 1.95 95% CI 1.17–3.25; HR
1.41 95% CI 1.05–1.89). Methylation of CD109 was only
significantly associated with poorer OS in 76 TNM stage
II patients (HR 2.41 95% CI 1.14–5.1). Yi et al. also
investigated combinations of these markers with and
without IGFBP3; however, significant results were only
reported for the two smaller populations of TNM stage
I–IV colon cancer patients (n = 147, n = 72) [
Conflicting results were also found for BNIP3
methylation, which was studied in three different studies
47, 65, 73
]. In overall univariate analyses, BNIP3
hypermethylation appeared to be statistically significantly
associated with a poorer survival in the study by Shimizu
et al. (Kaplan-Meier p value 0.012), whereas the other two
studies did not report a statistically significant association
with poor prognosis (HR 0.94 95% CI 0.58–1.55 and HR
2.23 95% CI 0.94–5.28). Subgroup analyses of BNIP3
methylation showed a statistically significant association
with poorer OS in one of two studies (HR 3.74 95% CI
] but not in the other (TNM stages II and
III, MSS, BRAFwt patients HR 0.86 95% CI 0.39–1.88, and
HR 1.08 95% CI 0.38–3.12, respectively) [
Out of six studies assessing the association between
MLH1 methylation and prognosis in TNM stages
I–IV, three studies showed statistically significant
results; however, two showed a better prognosis (HR
0.12 95% CI 0.03–0.56 and HR 0.56 95% CI 0.36–
0.89, respectively) [
], while the study by Iida et
al. showed a borderline significant association with a
poorer DSS (HR 2.23 95% CI 1.00–4.98) . The
studies by Wallner et al., Yang et al., and Wang et al.
showed no association [
70, 78, 84
]. Subgroup analyses
by Kuan et al. and Wang et al. also show these
conflicting results with one reporting a statistically
significant high recurrence risk (HR 8.29 95% CI
], while the other report a better OS
when MLH1 is methylated (p = 0.046) [
For MGMT promoter hypermethylation, four studies
showed no association with prognosis in TNM stages
43, 73, 94, 95
], while the study of Nilsson et al.
suggested an association between MGMT methylation
and a better prognosis (HR 0.36 95% CI 0.15–0.87)
. Subgroup analyses in TNM stages II and III
showed no association with MGMT in the study of
Cleven et al. [
]. Strikingly, a study by Kuan et al.
focused on recurrence and showed a strong association
between recurrence and MGMT methylation in TNM
stages III–IV (HR 11.83 95% CI 3.45–40.12) [
RASSF1A methylation was studied in five different
studies; however, a statistically significant association
was only found in TNM stage III patients (HR 3.89 95%
CI 1.23–12.30) [
]. Four studies in TNM stages I–IV
and another subgroup analysis on TNM stage II did not
show any association between RASSF1 and prognosis
]. Matthaios et al. investigated RASSF1
methylation in Dukes’ A–C and in Dukes’ D patients,
respectively. In both subgroups, RASSF1 methylation was
associated with a worse OS (HR 3.06 95% CI 1.27–7.50;
HR 5.76 95% CI 2.44–14.82, respectively) .
For CDKN2A (p14) methylation, the study of Nilsson
et al. suggests a worse prognosis in TNM stages I–IV
(p = 0.036) [
], while the studies of Cleven et al. and
Chaar et al. do not confirm these findings [
Subgroup analyses also do not show a statistically
significant influence of CDKN2A (p14) methylation on
Hypermethylation of APC was reported to be
positively associated with survival by Chen et al. (HR 0.426
95% CI 0.190–0.957) [
]; however, two independent
studies did not confirm these results [
contrast, in a subgroup of 66 stage II MSS and BRAF
wildtype patients, APC methylation was associated with
worse cancer-specific survival (HR 2.63 95% CI 1.21–
5.68) . Matthaios et al. investigated APC methylation
in Dukes’ A–C and in Dukes’ D patients, respectively. In
both subgroups, APC methylation was associated with a
worse OS (HR 7.88 95% CI 2.73–22.73; HR 3.47 95% CI
1.35–8.92, respectively) [
Methylation of CHFR as a prognostic marker was
assessed in only two independent studies. In the study
by Tanaka et al., CHFR methylation was associated with
poorer RFS in TNM stage I–IV patients (HR 2.99 95%
CI 1.23–7.23) [
]. This was not confirmed by the study
of Cleven et al., which reported no association with CSS
in TNM stage I–IV patients (HR 1.33 95% CI 0.81–2.20)
]. Subgroup analysis for TNM stage II and III patients
was done in both studies. The study of Tanaka et al.
reported no association between CHFR methylation and
RFS in TNM stage II patients (HR 1.45 95% CI 0.30–
6.88), but a significant association between CHFR
methylation and RFS in TNM stage III patients (HR 4.31
95% CI 1.34–12.8) [
]. In the study of Cleven et al.
subgroup analyses of TNM stage II, MSS and BRAFwt
patients showed a significant association between CHFR
methylation and CSS (HR 3.89 95% CI 1.58–9.60), but
results were not validated in an independent patient
series. Subgroup analysis of TNM stage III, MSS, and
BRAFwt patients did not show a significant association
with prognosis [
SEPT9 was assessed as a biomarker in two studies. Liu
et al. did not find a statistically significant association
between methylation and prognosis in TNM stage I–IV
or TNM stage I–III patients [
]. The study of Tham
et al. reports an association between SEPT9 methylation
and worse OS in TNM stage I–III patients (HR 3.50
95% CI 1.67–7.32) [
]. Although they appeared to
have assessed SEPT9 methylation as a biomarker, the
study of Perez-Carbonell did not give any specific
information on the outcome of this analysis [
For three markers (ID4, MYOD1, and SFRP2) and two
marker panels (AXIN2 & DKK1 and CDKN2A &
hMLH1), analyses were performed in two or more
independent studies or patient populations, but reported
results were too limited to construct a forest plot
(Additional file 6). Methylation of ID4 was assessed by
two studies. Umetani et al. reported a significant
association between ID4 methylation in stage I–IV CRC
patients and poorer OS (HR 1.82 95% CI 1.09–3.43)
], but the study of Tanaka et al. did not confirm this
(p = 0.118) [
]. MYOD1 was suggested as a prognostic
biomarker in the study of Hiranuma et al. (HR 3.16 95%
CI 1.25–8.02) [
], but this was not seen in the study of
Shannon et al. (p = 0.14) [
]. Also, for SFRP2
methylation, survival data were only reported in one out of two
]. Tang et al. observed a statistically
significant association with OS in stage I–IV CRC
patients (HR 3.06 95% CI 1.12–8.40) .
Of 83 included studies, 20 studies assessed the
prognostic influence of multimarker panels. Although the
same markers were included in several multimarker
panels, only three panels were assessed in two or more
independent studies or patient populations. For one
panel consisting of MLH1 and CDKN2A, a better
prognosis in TNM stage I–IV patients was reported in the
study of Veganzones et al. (p = 0.04) [
]. However, in
the study by Aoyagi et al., methylation of both genes
was associated with worse survival in TNM stage IV
patients (p = 0.03; Additional file 6) [
]. The study of
Gaedcke et al. described a panel of markers (ADAP1,
BARHL2, CABLES2, DOT1L, ERAS, ESRG, RNF220,
ST6GALNAC5, TAF4, SLC20A2) that was associated
with poorer DFS in two independent study series (HR
3.57 95% CI 1.01–12.55; HR 3.78 95% CI 1.26–11.37;
Fig. 2p) [
]. Kandimalla et al. studied another panel,
combing the markers AXIN2 and DKK1 (Additional file 6),
in two independent TNM stage II populations (n = 65 and
n = 79, respectively). In both populations, an association
with poorer RFS was found (HR 3.84 95% CI 1.14–12.43;
p < 0.0004, respectively) [
For a definite conclusion on validity of a (prognostic)
biomarker, a sufficient level of evidence (LoE) is needed.
Methylation marker results (i.e., similar conclusions
drawn in two or more independent study series) were
ranked according to two established ranking schemes to
obtain a comprehensive summary of the current
evidence on prognostic epigenetic biomarkers in CRC
]. For 12 single markers, and two multimarker
panels, consistent results were reported in two or more
publications or populations (Table 1). For four markers,
the current LoE is level II, and for the other markers,
LoE is lower. For 11 other markers and one multimarker
panel, reported results are still too inconclusive to draw
any conclusion on a possible prognostic biomarker effect
In this review, we summarized published studies on
prognostic DNA methylation markers for CRC. Although a
large number of studies were identified and included in
this review, the results from individual studies are difficult
a≥ 2 studies in similar populations showing consistent results
b“Summarized definition”; subgroup definitions differ between specific studies
cAll studies included that contribute to LoE, or all overall results if LoE was inconclusive
No influence on
to compare due to the variation in study design,
methodology, and survival endpoints. The number of prognostic
biomarkers that were considered in multiple independent
studies or patient populations is low, and promising results
observed in one study are often not validated in another.
In 2005, with the publication of the REMARK
guidelines, an attempt was made to improve the reporting
quality of biomarker studies [
]. However, the observed
variation in reporting sample series characteristics,
statistical analyses, and sample sizes indicates that the
REMARK guidelines are still not completely adapted
and that accurate reporting of prognostic DNA
methylation markers needs improvement (median REMARK
score 10.7 out of 20). As we observed that studies which
reported significant findings had lower REMARK scores
(Fig. 3a, p = 0.005) than studies which did not, a stricter
adherence to the REMARK guidelines might be helpful,
if we ever want to draw definitive conclusions on the
role of a possible biomarker [
]. A more rigorous peer
review by the scientific journals might be justified, in
order to achieve this [
]. However, the REMARK
guidelines are open to subjective interpretation, just as
the scoring of the REMARK criteria. An inadequately
reported methylation marker study does not imply that
the methylation marker itself is not valuable, but it
might hinder reproducibility.
The observed inconsistencies in individual study
results might have various reasons, such as differences
in sample collection, sample preparation, methods of
DNA methylation analysis, and the genomic location of
the assay [
]. The lack of standardization of
different methods is a major issue in DNA methylation
research. Differences in one or more technical aspects of
the detection method used, including primer design,
reagents, equipment, and protocols, can result in
different DNA methylation measurements, even for the exact
same genomic location, and can therefore have a
substantial impact on the prognostic value of a test [
]. Therefore, even consistently reported methylation
marker results from this review should be treated
carefully and validated in powered prospective studies.
Comparing the results of methylation markers obtained
with different methodology was not within the scope of
this review, however should be addressed in a
metaanalysis of the markers with most evidence.
The same lack of standardization holds true for the
statistical analysis and the choice of study endpoints [
Whereas many studies focus on overall survival, other
studies use cause-specific, disease-specific, or recurrence-free
survival or do not specify the endpoints that were
considered. As there are no uniform definitions of these endpoints,
it is difficult to compare individual study results .
It is generally accepted that CRC is a heterogeneous
disease with diverse subgroups, both on histological and
molecular level [
]. Analyzing all CRC patients as
one group will therefore obscure the true potential of
some biomarkers. The majority of studies in this review
(68 studies; 82%) performed an analysis in TNM stages
I–IV. As to date, TNM stage is one of the most
important prognostic factors in cancer and the choice to
include all TNM stages in one analysis will most likely
influence the final conclusion. To overcome this, most
studies also included one or more subgroup analyses,
e.g., subgroups based on MSI, KRAS, or TNM stage.
However, the definition of these subgroups is often very
detailed or specific, thereby hampering the comparability
of individual study results . For example, 13 different
subgroup analyses were identified for CDKN2A (p16) in
this systematic review that could not be combined in a
meta-analysis. In addition, different subgroups may have
different baseline risks of death, thereby even further
hindering comparison between different studies. Thus,
on the one hand, analyzing CRC as a homogenous group
could hinder the discovery of subgroup-specific
biomarkers; however, on the other hand, analyzing
subgroups that are too specific hinders the possibility of
validation or meta-research.
Although the risk of introducing selection bias when
selecting patients solely based on the availability of tissue
is recognized [
], most studies in this review were
retrospective (67 studies; 81%) and conducted in a small
number of patients (64 studies included < 200 patients;
77%). Often, those study populations are often patient
series that have been collected in research laboratories
and University Hospitals based on the availability of
samples. This, in addition to the availability of follow-up
data, often determines the size of the study population.
This approach however does not sufficiently contribute
to answering the question whether a biomarker had
prognostic value and if it should be implemented to
improve patient care. It has already been shown that the
prognostic effect of DNA methylation markers assessed
in small sample series are often chance findings that
cannot be reproduced in independent series [
Also in this review, we observed that statistically
significant p values tend to be reported more often in studies
with a small population size (Fig. 3c). In order to
increase the LoE of prognostic methylation markers,
large-scale prospectively collected study populations are
required. Therefore, we need a more structured
approach, with collaborations between research groups,
to obtain sufficient numbers of patient samples and
validation populations to draw final conclusions on the
prognostic relevance of a biomarker . Twenty-three
single markers and two multimarker panels have been
investigated in two or more independent studies or
patient populations. For eight biomarkers (IGFBP3,
CDKN2A (p16), WNT5a, HPP1, RET, TFPA2E, HLTF,
CD109, NRCAM, FLNC, and EVL) and the marker
panels proposed by Gaedcke et al. (ADAP1, BARHL2,
CABLES2, DOT1L, ERAS, ESRG, RNF220,
ST6GALNAC5, TAF4, SLC20A2) and Kandimalla et al. (AXIN2,
DKK1), the results were consistent and a statistically
significant association with prognosis was observed
despite differences in study design and methodology. For
five biomarkers (IGFBP3, CDKN2A (p16), WNT5a,
HPP1, and RET), the current LoE was II–III, indicating
that a definitive conclusion on the prognostic influence
of these markers is within reach. However, before a
definitive conclusion on these markers can be made, a
large, prospective study aimed at studying the clinical
validity or a meta-analysis of studies with LoE II, which
will be difficult given the differences in study design and
methodology, is needed. Solely for WNT5a, additional
studies assessing the prognostic influence might be
omitted as results show no prognostic influence with a
current LoE of II. For the other markers (TFPA2E, HLTF,
HPP1, and the multimarker panel proposed by Gaedcke
et al.), current LoE is greater than or equal to III,
indicating that even more validation is needed. To increase
the LoE, these validation studies should preferably be
prospectively designed, aimed at studying the biomarker
effect, instead of retrospective case-series as these
contribute little to increasing the LoE.
Reporting according to guidelines such as REMARK is
important but not sufficient for successful translation of
prognostic DNA methylation markers to clinical
practice. Promising prognostic DNA methylation markers
should be evaluated in multivariable prediction models
to study their added prognostic value compared to the
current reference standard (TNM stage) and other novel
strategies to predict CRC prognosis [
]. Few of
the included prognostic methylation marker studies in
this review have assessed the incremental value of their
prognostic DNA methylation marker in addition to the
golden standard TNM staging system or other suggested
prognostic markers such as grade of differentiation or
microsatellite instability (MSI) [
]. In addition, other
potential prognostic tools, such as histologic and
molecular markers [
], the immunoscore ,
circulating tumor DNA (ctDNA) [
], or the consensus
molecular subtype (CMS) classification [
] should be
taken into account in prediction models, as it is likely
that a combination of several different types of markers
will eventually yield the best predictive power.
Despite the widespread acceptance of epigenetic
alterations as possible important biomarkers for CRC
prognosis, very few biomarkers reach the point of usability in
daily patient care and comprehensive overviews of the
abundantly available biomarker results are lacking. In
this review, we identified several promising markers that
all require different amounts of further validation before
definitive conclusions on their clinical applicability can
be drawn. We also identified multiple problems
hampering the comparison of individual study results including
problems with population selection, study design,
technical issues, and validation problems. Adhering to the
REMARK guidelines might partly overcome these
problems, and a more rigorous peer-review process
specifically focusing on these reporting issues might be an
essential step towards reducing the number of chance
findings. In addition, biomarker research would benefit
from a more structured approach in multidisciplinary
collaborations, including clinicians, epidemiologists,
statisticians, technicians, and molecular biologists, aiming to
perform large, well-designed, and validated biomarker
]. Only then will we be able to ultimately
assess the clinical value of a biomarker.
Additional file 1: Table S1. Search terms used for systematic review.
(DOCX 83 kb)
Additional file 2: Table S2. REMARK checklist and description for
scoring the reviewed studies. (DOCX 94 kb)
Additional file 3: Table S3. Characteristics of 83 included studies.
(XLSX 95 kb)
Additional file 4: Table S4. Scoring of 83 included studies according to
REMARK. (DOCX 114 kb)
Additional file 5: Table S5. Risk of potential bias and confounders of
the included studies. Studies indicated by a “X” potentially have an
increased risk of bias, whereas studies indicated by a “√” potentially have
a decreased risk of bias. (DOCX 108 kb)
Additional file 6: Table S6. Single markers and their characteristics that
have been investigated in more than one study series, however, of which Cox
regression survival analysis was not available for all markers. (DOCX 63 kb)
Availability of data and materials
All data generated or analyzed during this study are included in this
published article and its supplementary information files.
MD, DG, HG, MW, MvE, VM, and KS contributed to the conception and
design of the manuscript. MD and DG performed the systematic search, data
extraction, and scoring of all articles. MD, AK, and KS analyzed and
interpreted the data. MD and KS drafted the manuscript, and all authors
critically revised the manuscript. All authors read and approved the final
manuscript for publication.
Ethics approval and consent to participate
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
1Department of Pathology, GROW - School for Oncology and Developmental
Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
2Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology,
University of Leeds, Leeds, UK. 3Department of Epidemiology, GROW
School for Oncology and Developmental Biology, Maastricht University
Medical Center, Maastricht, The Netherlands. 4Department of Clinical
Genetics, University of Rotterdam, Rotterdam, The Netherlands.
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