Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52
https://doi.org/10.1186/s12881-019-0771-5

RESEARCH

Open Access

The CIMP-high phenotype is associatedwith energy metabolism alterations incolon adenocarcinoma
Maria S. Fedorova1†, George S. Krasnov1†, Elena N. Lukyanova1, Andrew R. Zaretsky1, Alexey A. Dmitriev1,
Nataliya V. Melnikova1, Alexey A. Moskalev1, Sergey L. Kharitonov1, Elena A. Pudova1, Zulfiya G. Guvatova1,
Anastasiya A. Kobelyatskaya1, Irina A. Ishina1, Elena N. Slavnova2, Anastasia V. Lipatova1, Maria A. Chernichenko2,
Dmitry V. Sidorov2, Anatoly Y. Popov3, Marina V. Kiseleva2, Andrey D. Kaprin2, Anastasiya V. Snezhkina1
and Anna V. Kudryavtseva1*
From 11th International Multiconference “Bioinformatics of Genome Regulation and Structure\Systems Biology” - BGRS\SB2018
Novosibirsk, Russia. 20-25 August 2018

Abstract
Background: CpG island methylator phenotype (CIMP) is found in 15–20% of malignant colorectal tumors andis characterized by strong CpG hypermethylation over the genome. The molecular mechanisms of thisphenomenon are not still fully understood. The development of CIMP is followed by global gene expression
alterations and metabolic changes. In particular, CIMP-low colon adenocarcinoma (COAD), predominantly
corresponded to consensus molecular subtype 3 (CMS3, “Metabolic”) subgroup according to COAD molecularclassification, is associated with elevated expression of genes participating in metabolic pathways.
Methods: We performed bioinformatics analysis of RNA-Seq data from The Cancer Genome Atlas (TCGA)
project for CIMP-high and non-CIMP COAD samples with DESeq2, clusterProfiler, and topGO R packages.
Obtained results were validated on a set of fourteen COAD samples with matched morphologically normal
tissues using quantitative PCR (qPCR).
Results: Upregulation of multiple genes involved in glycolysis and related processes (ENO2, PFKP, HK3, PKM,
ENO1, HK2, PGAM1, GAPDH, ALDOA, GPI, TPI1, and HK1) was revealed in CIMP-high tumors compared to nonCIMP ones. Most remarkably, the expression of the PKLR gene, encoding for pyruvate kinase participating in
gluconeogenesis, was decreased approximately 20-fold. Up to 8-fold decrease in the expression of OGDHL
gene involved in tricarboxylic acid (TCA) cycle was observed in CIMP-high tumors. Using qPCR, we confirmed
the increase (4-fold) in the ENO2 expression and decrease (2-fold) in the OGDHL mRNA level on a set of
COAD samples.
(Continued on next page)* Correspondence: rhizamoeba@mail.ru†
Maria S. Fedorova and George S. Krasnov contributed equally to this work.
1
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences,
Moscow, Russia
Full list of author information is available at the end of the article© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

Page 16 of 56(Continued from previous page)

Conclusions: We demonstrated the association between CIMP-high status and the energy
metabolism changes at the transcriptomic level in colorectal adenocarcinoma against the background of
immune pathway activation. Differential methylation of at least nine CpG sites in OGDHL promoter region as
well as decreased OGDHL mRNA level can potentially serve as an additional biomarker of the CIMP-highstatus in COAD.
Keywords: Colon adenocarcinoma, CIMP, RNA-Seq, TCGA, Energy metabolism

Introduction
Colon adenocarcinoma (COAD) is a common and socially significant disease, nevertheless, some mechanismsof its onset and progression have been studied insufficiently [1–3]. The advent of high-throughput “omics”
methods has made it possible to detect alterations that
occur during the malignant transformation and progressionof COAD. These include changes in methylation patterns,
mutation spectra, non-coding RNA profiles, cell signaling, as well as metabolic pathways at both mRNA and protein levels [1, 2, 4, 5]. “Omics” data are useful for molecularclassification of malignant tumors, drug discovery, and developing personalized approach to optimize colon adenocarcinoma management.
Traditionally, colorectal cancer (CRC) is characterizedwith several molecular features, these are: the presenceor absence of microsatellite instability (MSI), chromosomal instability (CIN), CpG island methylator phenotype (CIMP), and spectrum of the mutations in
the driver genes. The accumulation of multidimensional"omics" data on colorectal cancer made it possible tolink these features and to develop consensus molecularclassification of CRC. As a result of complex analysis of
six independent classification systems, four consensus
molecular subtypes (CMS) with distinguishing features
were identified: CMS1 (“MSI/Immune”, 14%), CMS2(“Canonical”, 37%), CMS3 (“Metabolic”, 13%), and
CMS4 (“Mesenchymal”, 23%) [6].
Like other malignant neoplasms, COAD is characterized by the alteration of various cell signaling and metabolic pathways, including energy metabolism [7–11].
Mitochondrial dysfunction and activation of glycolysis isa well-known hallmark of cancer [12–15]. The activationof glycolysis is commonly accompanied with increased
expression of hexokinases, which are the enzymes thatparticipate in the first stage of glycolysis. This provides
an opportunity to search for therapeutic targets among
genes involved in cell energy metabolism [16, 17]. However, disturbances in gene expression, signaling andmetabolic pathways can be different depending on particular driver alterations (e.g. mutations in RAS-RAF
cascade). Previously, we found that expression of hexokinases was upregulated at the mRNA level in only 10–

15% of COAD samples [18]. Furthermore, it has beenshown that increased expression of HK1 and HK2 in
COAD tissue is associated with an unfavorable prognosis[19, 20].
Unsupervised clustering of the promoter DNA methylation profiles revealed four methylation clusters. Two ofthem contained tumors with elevated methylation rates
and belonged to so called CpG island methylator phenotype (CIMP, CIMP+, and CIMP-positive) discovered in
CRC by Toyota et al. [21, 22]. CIMP phenotype occurs
in 15–20% of CRC samples and characterized by excessive methylation levels predominantly in promoter regions across the genome. CIMP-positive group can bedivided into two subtypes, CIMP-high (CIMP-H) and
CIMP-low (CIMP-L), according to the level of hypermethylation and some other features. A CIMP-high subgroupexhibits an exceedingly high frequency of genome hypermethylation; it is strongly associated with MLH1 promoter
hypermethylation and with the presence of BRAF V600E
mutation. A CIMP-low subgroup is enriched for KRAS
mutations and characterized by DNA hypermethylation of
a subset of CIMP-high associated markers as well as aunique group of CpG sites [1, 23]. Nevertheless,genome-wide methylation profiling revealed at least two
more clusters containing non-CIMP samples. Both clusters predominantly included tumors that originated fromdifferent locations [1, 23]. Unfortunately, neither
mRNA-Seq data nor miRNA profiling can help to differentiate these two non-CIMP clusters. Comparison of
methylation clustering analysis with other quantitative“omics” data identified significantly overlapping only for
one group: one out of three mRNA clusters partially corresponded to CIMP-high tumors, it was also enriched withhypermutated tumors. The absence of clear criteria defining
CIMP-positive and non-CIMP tumors, especially theirmore detailed classification, makes them difficult to be
identified in routine laboratory practice. Currently there areseveral approaches for the identification of CIMP-positive
samples [24–26].
In terms of novel consensus classification, CIMP-positive COAD samples are mostly included in CMS1 and
CMS3 subgroups. CMS3 is characterized by metabolic
dysregulation and contains predominantly CIMP-low

Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

tumors. The most common alterations in CMS1 subgroup, which is mostly comprised of CIMP-high samples, are hypermutations, MSI, and strong immune
activation. It is assumed that CIMP-high status is associated with MSI through the methylation of MLH1 promoter regions. Being inactivated, MLH1, a participant of
the mismatch repair system, does not prevent the accumulation of DNA damage. However, the particularmechanisms of association between energy metabolicchanges and CIMP status are still unclear [27].
Therefore it is expected that the development of CIMP
status is a part of more complex process. This involves
specific driver mutations in the genes such as KRAS,
BRAF, IDH1, and IDH2, the activation of glycolysis andgeneral metabolic changes, as well as deregulation in
many other cell signaling pathways [28–30]. Moreover,in some malignant tumors, the driver mutations in
the genes encoding “metabolic” enzymes, such as IDH1,
SDHx, and others, are sufficient to further establish
methylator phenotype [31–33]. To summarize, thehypermethylation of cancer cell genome is considered tobe commonly associated with the alterations in energy
metabolism [27]. Nevertheless, strong metabolic alterations (e.g. energy metabolism shift) are postulated only
for CMS3 subgroup mostly including CIMP-low samples
with KRAS mutations [6]. Remarkably, CMS1 subgroup,
predominantly represented by CIMP-high tumors, is notreferred to be characterized with the metabolic alterations, first of all, by the energy metabolism shift. This
fact does not play into the current understanding of the
carcinogenesis mechanisms and empirically establishedcommon rules and tendencies in different tumor types.
In the present work, we analyzed the association between CIMP-high status and alterations of energy metabolism in colon adenocarcinoma using RNA-Seq datafrom The Cancer Genome Atlas (TCGA) project. Obtained results were verified with quantitative
PCR (qPCR) on a set of COAD samples derived from
Russian patients. We revealed deregulation in the expression of several genes involved in the energy metabolism as well as activation of immune-associated pathways
in CIMP-high colorectal adenocarcinoma.

Materials and methods
Bioinformatics analysis

We performed differential gene expression analysis of
TCGA RNA-Seq data (colon adenocarcinoma dataset).
We focused on the patients of the Caucasian race (163
samples), identified in the database as “white”, as they
most closely genetically resemble the Russian population. We carried out two comparisons: between pools oftumor and normal tissues, and between CIMP-high andnon-CIMP tumor samples. CIMP status was included in
the annotation provided by TCGA consortium [1].

Page 17 of 56

Differentially expressed genes (DEGs) were identified
using the DESeq2 Bioconductor package [34]. We selected top DEGs that passed DESeq2 FDR < 0.05 andMann-Whitney p < 0.05 thresholds. Next, we performed
over-representation (OR) and gene set enrichment(GSEA) tests for KEGG pathways and top DEGs using
the clusterProfiler package [35]. Then, we visualized differential expression profiles of genes involved in several
KEGG pathways, including “Glycolysis/Gluconeogenesis”
and “TCA cycle”, using the pathview Bioconductor package [36]. Some KEGG nodes represent a set of proteins
with similar function or isozymes. For example, lactate
dehydrogenase (LDH) corresponds both to LDHA and
LDHB. This means that the sum of bidirectional gene
expression changes within the same KEGG node will bevisualized as the retention in mRNA level. Therefore, we
edited the automatically generated diagrams to reveal
the expression alterations of each gene within the KEGG
nodes if its differentially expressed.
For detailed analysis of epigenetic characteristics
in OGDHL promoter region across TCGA cohort,
we compared CIMP-high and non-CIMP tumors incontext of differential CpG methylation. TCGA
dataset derived with Illumina Infinium HumanMethylation450 microarrays (17 CpG sites per gene, on the average) was used. Unfortunately, the original TCGA research
paper [1] published in 2012 contains annotation on CIMP
status only for samples analyzed with the previous generation of methylation-sensitive microarrays, HumanMethylation27 (only 1 CpG site per gene, on the average).
Hence, we developed an approach of identifying
CIMP-like samples based on genome-wide methylation
profiling data. First, we selected CpG sites located in genepromoter regions using ENCODE genomic segments annotation (consensus of ChromHMM and SegWay
methods; 110,000 of 300,000 CpG sites passed this filter).
Next, for each sample we calculated MA, an average of
80th, 85th, 90th, and 95th percentiles (we used the averagevalue in order to reduce stochastic noise) of β-values
across passed CpG sites (β-value is the ratio of methylated
alleles for a current CpG). The derived MA values indicated the overall hypermethylation level for each sample.
According to MA distribution, all the samples were divided into three groups: “non-CIMP”, “CIMP-low”, and“CIMP-high”.
As expected, CIMP-high status (versus non-CIMP)
demonstrated statistically significant Spearman(anti)correlations with MSI-high (r = 0.53), hypermutation phenotype (r = 0.49), mutations in BRAF
(r = 0.46), APC (r = − 0.27), and p53 (r = − 0.24) that
perfectly agrees with the observations made forcommon CIMP-high samples, which had been selected regarding to annotation in TCGA consortium
paper [1].

Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

Page 18 of 56

Tissue specimens

In total, fourteen COAD samples (seven CIMP-highsamples and seven non-CIMP ones) and matched morphologically normal tissues were obtained after surgicalresection prior to radiation or chemotherapy. The samples were frozen and stored in liquid nitrogen. Morphological classification of the tumors was performed
according to the WHO Classification of Tumors of theDigestive System [37]. Only samples comprised of ≥70%
tumor cells were included in the analysis. Written informed consent was obtained from all patients. The
study was approved by The Ethics committee of Herzen
Moscow Cancer Research Institute, a branch of the National Medical Research Radiological Center, Ministry of
Health of the Russian Federation and was performed inaccordance with the principles outlined in the Declaration of Helsinki (1964). Information on the patientsand characteristics of the COAD tumors, including age,gender, grade, TNM staging, KRAS, NRAS, andBRAF mutations, microsatellite instability (MSI), and
CIMP status, were determined (Table 1).

RNA integrity number was measured using an Agilent
Bioanalyzer 2100 (Agilent Technologies, USA). cDNA
synthesis was performed using M-MLV Reverse Transcriptase (Thermo Fisher Scientific) and random
hexamers.
Microsatellite instability testing and identification of
mutation status in KRAS, NRAS, and BRAF genes

Sanger sequencing was performed for KRAS (exons 2, 3,
and 4), NRAS (exons 2, 3, and 4), and BRAF (exon 15)[38]. MSI status was analyzed using five common
markers (D2S123, D5S346, D17S250, BAT25, andBAT26). MSI-high (MSI-H) status was defined as instability in two or more of the five markers, andMSI-low (MSI-L) status was detected as instability inonly one of the five markers [39]. The distinction between microsatellite stable (MSS) and MSI-low can only
be accomplished if a greater number of markers is estimated. A unique clinical and pathological phenotype isidentified for the MSI-high tumors (approximately 15%
of malignant colorectal tumors). Tumors with MSI-low
and MSS status appear to be phenotypically similar.

Nucleic acid isolation and cDNA synthesis

Genomic DNA and total RNA were extracted from the
fresh-frozen tissue. DNA was isolated using a MagNA
Pure Compact Nucleic Acid Isolation Kit I - Large Volume (Roche, Switzerland). RNA was isolated with a
MagNA Pure Compact RNA Isolation Kit (Roche). DNA
and RNA quantification was performed on a Quibit
2.0 fluorometer (Thermo Fisher Scientific, USA). The

CIMP status testing

The first step of this analysis was represented by sodium
bisulfite conversion of genomic DNA (1 μg) using a
Zymo Research EZ DNA Methylation Kit (Zymo Research, USA) with a final eluted volume of 20 μl. The
converted DNA was diluted as 1:10 for MethyLight analysis, and the methylated and unmethylated controls

Table 1 Clinicopathological and molecular genetic characteristics of the COAD patients.
Age

Gender

Grade

Patient

TNM
stage

KRAS
statusNRAS
status

BRAF status

MSI
statusRelative mRNA level
ENO2

OGDHL

PKLR

CIMP-high COAD patients
Pat1

63

F

2

T3N1M0WT

WT

c. 1799 T → A (p.Val600Glu)

MSI-H

25↓

1.9↑

1.1↑

Pat2

76

M

2

T3N0M0WT

WT

c. 1799 T → A (p.Val600Glu)

MSI-H

25↓

2.6↑

2.1↑

Pat3

69

F

2

T3N0M0WT

WT

c. 1799 T → A (p.Val600Glu)

MSI-H

25↓

4.0↑

2.4↑

Pat4

62

F

2

T3N1M0WT

WT

c. 1799 T → A (p.Val600Glu)

MSI-H

20↓

2.0↑

1.4↑

Pat5

77

F

3

T2N0M0WT

WT

c. 1799 T → A (p.Val600Glu)

MSI-H

1.1↓

1.1↑

1.3↓

Pat6

55

F

2

T3N0M0WT

WT

c. 1799 T → A (p.Val600Glu)

MSS

4.3↑

2.8↓

1.2↑

Pat7

76

F

2

T3N1M0

WT

WT

WT

MSS

2.5↓

1.7↑

2.4↑

1.1↓

Non-CIMP COAD patients
Pat8

85

M

2

T3N0M0

WT

WT

WT

MSS

1.1↓

3.5↑

Pat9

83

M

2

T3N0M0

WT

WT

WT

MSS

7.4↑

1.7↑

5.1↑

Pat10

67

F

2

T3N0M0

WT

WT

WT

MSS

3.2↑

1.0

5.7↑

Pat11

77

F

2

T3N0M0

WT

WT

WT

MSS

24.8↑

14.3↓

10.2↑

Pat12

59

F

3

T3N1M0

WT

WT

WT

MSS

5.7↑

4.8↓

1.7↓

Pat13

69

F

2

T4N1M0

WT

WT

WT

MSS

3.8↑

4.8↓

7.7↑

Pat14

55

F

2

T3N0M0

WT

WT

WT

MSS

30.0↑

2.9↓

13.3↑

Note: ↑ - expression increase; ↓ - expression decrease.

Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52(CpGenome Universal Methylated/Unmethylated DNA,
Millipore, USA) were diluted as 1:80. A total of 5 μl of
diluted DNA was used per PCR reaction. Additionally,
serial dilutions of the methylated DNA control were runin each PCR plate for standard curve generation.
Testing for CIMP status was performed using
methyl-specific qPCR for eight markers localized in promoter regions of the CDKN2A (NM_000077.4 transcript,
traditionally called p16), CACNA1G, IGF2, NEUROG1,
RUNX3, SOCS1, CRABP1, and hMLH1 genes. Ogino etal. suggested using these eight markers for the routine
laboratory stratification of colorectal cancer samples.
Depending on amount of positive markers, samples weredivided into three groups: CIMP-high, CIMP-low, andnon-CIMP. A sample is considered to be CIMP-low if
one to five out of eight markers are methylated, and to
be CIMP-high if six to eight markers are methylated.
The promoter regions of ACTB and COL2A1 genes wereused for normalization [40, 41].
Quantitative PCR

Gene expression levels were estimated by qPCR. Amplification was performed in triplicates using TaqMan
assays(ThermoFisherScientific):
OGDHL(Hs00971806_m1), ENO2 (Hs00157360_m1), and PKLR
(Hs00176075_m1) on the ABI PRISM® 7500 Sequence
Detection System (Thermo Fisher Scientific) following
the manufacturer’s instructions. RPN1 and GUSB wereused as reference [42, 43] genes. The PCR program was
as follows: 10 min at 95 °C, then 50 two-step cycles of
15 s at 95 °C and 60 s at 60 °C. The total reaction volume
was 20 μL.
The Relative Quantitation software (Thermo Fisher
Scientific) and “Analysis of Transcription of Genes” tool
were used to analyze the qPCR data considering the efficiency of the PCR amplification. Relative expression
levels of the target genes were calculated using the ΔΔCt
method [11, 44, 45].
To analyze the differences in relative (tumor-to-normal) gene expression levels between groups of
CIMP-high and non-CIMP samples, we applied the nonparametric Mann-Whitney U-test and considered p <0.05 as statistical significant event.

Results
Bioinformatics analysis

First, we performed the comparison between pools of
COAD and normal samples based on TCGA RNA-Seq
data. The analysis revealed the altered expression of
glycolytic genes (a part of “Glycolysis/Gluconeogenesis”
KEGG pathway) occurring along with the downregulation of many genes that participate in the tricarboxylic
acid (TCA) cycle (a part of “TCA cycle” KEGG

Page 19 of 56

pathway). Together, these results suggest that the activation of glycolysis and suppression of mitochondrial respiration may occur in COAD (Additional files 1, 2, 3,
and 4).
Decreased expression was observed for only a few
glycolytic genes, including HK2 (2-fold). Increased expression was revealed in a significantly larger number ofglycolytic genes, including PFKM, ALDOC, LDHA,LDHB, PKM, PGK1, ENO3, GAPDH, PGK1, ENO1, andGPI (1.4-1.8-fold). As expected, the gluconeogenesis was
predominantly downregulated at the transcription level
and was characterized by decreased expression of PCK1
(8-fold) and G6PC (3-fold) genes. It is worth noting that
we report only average changes in gene expression level
between the two groups. Within each group, the expression level values are heterogeneous and take on various
values. Due to the large sampling size, we were able toobserve even slight trends toward up- or downregulation
between the groups (e.g. 1.2-1.5-fold). These differenceswere statistically significant (Mann-Whitney test, p <0.05).
In contrast to glycolytic genes, the expression of genes
involved in the TCA cycle and related processes was
mostly characterized by decrease: SUCLG2 (3-fold);
SDHD, PC, IDH3A, and ACO2 (2-fold); MDH1,
SUCLG1, and IDH1 (1.5-fold). The increased expression (1.5-fold) was noticed for only three genes participating in TCA cycle - ACLY, IDH2, and MDH2.
Next, we tested the hypothesis whether the disturbance of energy metabolism is related to increased genome methylation level (CIMP-high) and is not limitedby “Metabolic” molecular subtype (CMS3) according toconsensus classification [6]. Bioinformatics analysis of
TCGA data revealed the increased expression of theglycolytic genes in CIMP-high COAD samples compared
to non-CIMP ones. The same cohort represented in
TCGA database was used for this comparison as for described above. It is important that CIMP status description was already included in the annotation provided by
TCGA consortium. These results are presented in the
Additional files 5, 6, 7, and 8.
Most remarkably, the expression of the PKLR gene, encoding for pyruvate kinase involved in gluconeogenesis,
was decreased approximately 20-fold. In all CIMP-highsamples, the expression of PKLR was extremely low(only few reads per sample). Increased expression (1.5-3.0-fold) was observed for ENO2, PFKP, HK3,
PKM, ENO1, HK2, PGAM1, and GAPDH genes. A lessmarked increase in expression (approximately 1.2-fold)
was observed for ALDOA, GPI, TPI1, and HK1. Amongthe genes encoding TCA cycle enzymes, we observedthat the expression of OGDHL was decreased 8-fold.
Like PKLR gene, the OGDHL expression was very weakin 95% of CIMP-high samples. Several genes showed aFedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

slight increase in the expression, in particular, mRNAlevel of SDHA and DLST was increased 1.5-fold, while
CS expression was increased 1.2-fold. It should be notedthat in CIMP-high tumors we observed significant upregulation of dozens of genes involved in the immune
response including chemokines, cytokines and manyothers that are predominantly expressed by immune
cells.
Validation of the gene expression in Russian patients

Based on the results of bioinformatics analysis, we selected several DEGs involved in energy metabolism (ENO2, PKLR, and OGDHL) for experimental
validation with qPCR in CIMP-high and non-CIMP
COAD samples. Clinicopathological and molecular genetic characteristics of COAD samples are represented inTable 1.
Downregulation of OGDHL expression (p = 0.007) was
observed in CIMP-high COAD samples (Tables 1,2,
Fig. 1). Statistically significant (p = 0.049) upregulation of
ENO2 mRNA level was also found in CIMP-high group.
Additionally, it was obseved a tendency for PKLR downregulation, nevertheless, p-value was not statistically significant (p = 0.165).
Analysis of OGDHL promoter region methylation

We found a significant decrease in OGDHL mRNA levelin CIMP-high COAD samples compared to non-CIMP
ones. As the aberrant methylation of promoter regionis one of the most frequent mechanisms of expression decrease, we assessed if this does contribute to OGDHL
downregulation in COAD. CpG methylation status wasanalysed based on TCGA data derived with Illumina Infinium HumanMethylation450 microarrays. Whereas thesesamples didn’t have information about CIMP status in the
TCGA annotation, we used the developed approach (seeMaterials and Methods) of identifying “non-CIMP”,“CIMP-low”, and “CIMP-high” groups based on methylation profiling with the microarrays.
For at least nine CpG sites in OGDHL promoter region, we found a differential methylation between “non-CIMP” and “CIMP-high” samples (Fig. 2). This factdemonstrates that some loci could serve as additional
markers of high methylation level across the genome.

Page 20 of 56

Discussion
Glycolysis and TCA cycle are interconnected to various
cell signaling and metabolic pathways that are importantin the context of carcinogenesis and found to be frequently disturbed in tumors: fatty acid and lipid biosynthesis, synthesis of amino acids and nucleotides,
stabilization and degradation of HIF1-a, and others.
Many genes involved in glycolysis and TCA cycle areconsidered to be prognostic markers for different cancer
types. In the present work, we found a clear trend towards upregulation of glycolytic genes in colon adenocarcinoma, especially CIMP-high tumors. Indeed,
increased glycolytic rate, a hallmark of cancer cells, represents not only a mechanism of adaptation to hypoxic
conditions but provide a source of ATP and building
material that met needs of intensively proliferating cells[46]. We focused on two KEGG pathways, hsa00010(“Glycolysis/Gluconeogenesis”) and hsa00020 (“TCAcycle”). Despite the names, these KEGG pathways partially intersect to each other and contain additional
metabolic branches.
“Glycolysis/gluconeogenesis” KEGG pathway

Cancer cells are characterized by activation of glucose
catabolism together with mitochondrial dysfunction andcommonly inactivated glucose synthesis. Thus, ATP and
the intermediate products required for active growthand proliferation are produced intensively [47, 48]. We
found expression upregulation of ten genes (1.5–3-fold)
and downregulation of three genes (1.3–2-fold) participating in glycolytic process.
The first stage of this process is the phosphorylation of glucose catalyzes by hexokinases (HK1, HK2,and HK3). Traditionally, it has been believed that theexpression level of hexokinases, especially HK2, is increased in colorectal cancer [49–51]. However, wehave previously demonstrated a decrease in the expression of HK1 and HK2 in majority of colorectal
cancer samples [18].
In the present study, we observed decreased HK2
expression, and upregulation of HK3 expression in
COAD. In hepatocellular carcinoma, overexpressionof hexokinase 2 is associated with HK2 CpG island
hypermethylation and hypomethylation of surrounding
promoter regions. These methylation state changes

Table 2 Expression of ENO2, PKLR, and OGDHL genes in CIMP-high and non-CIMP COAD samples
CIMP-high samples
Genes
ENO2

Non-CIMP samples

Frequency of mRNA level changes, %↑↓57 (4/7)

14 (1/7)

Median of mRNAlevel changes, n-foldFrequency of mRNA level changes, %
↑

↓

1.2↑

14 (1/7)

57 (4/7)

Median of mRNAlevel changes, n-fold
1.0

OGDHL

14 (1/7)

71 (5/7)

2.0↓

85 (6/7)

0 (0/7)

10.8↑

PKLR

43 (3/7)

0 (0/7)

1.6↑

71 (5/7)

14 (1/7)

6.2↑

Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

Page 21 of 56

Fig. 1 Boxplots illustrating relative mRNA levels (tumor versus matched normal tissue) of ENO2, OGDHL, and PKLR genes in two groups of COAD
samples (with CIMP-high and non-CIMP status) according to qPCR data. The boxes show quartile range (25th-75th percentiles). The central lines
in boxes show a median value. Among these genes, OGDHL and ENO2 differential expression is statistically significant between CIMP-high andnon-CIMP tumors (p < 0.05)

can serve as a prognostic marker [52]. The HK2 promoter region contains binding sites for AP-1 transcription factors, as well for the Myc proto-oncogene
and mutant forms of p53 [53]. Mutant forms of p53
activate HK2 expression, and can account for the relationship between the loss of cell cycle control in
rapidly growing tumors and activation of glycolysis.
High levels of HK2 expression were observed in theinvasive margins of colorectal tumors [20]. In combination with decreased p-PDH expression, HK2 overexpression may represent a marker of unfavorable
prognosis, tumor aggressiveness, and recurrence inpatients with colorectal cancer [54].

HK3 is expressed at high levels in the bone marrowand spleen. Previously, we have shown a frequent andsignificant increase in HK3 expression (more than
100-fold) in colorectal cancer [55]. However, the absolute number of HK3 transcripts in cells is significantly
lower than that of the other hexokinases.
We also found 1.5-fold (on the average) upregulation of
GAPDH, encoding for glyceraldehyde-3-phosphate dehydrogenase, a key glycolytic enzyme that is present in all
tissues and has multiple functions [56, 57]. GAPDH is
traditionally referred as an endogenous control gene for
qPCR, however, its increased expression has been reported for various malignant tumors [58–64]. GAPDH

Fig. 2 Heatmap illustrating distribution of beta-values of 13 CpG sites in the promoter region of OGDHL gene in “CIMP-high” and“non-CIMP” samples

Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

plays an important role in the formation of malignant
tumor phenotypes and is considered as a promising target
for therapy [16, 17].
Glycolysis rate is regulated both at gene expression
level and allosterically (enzymes can be inhibited with
their products). It can be assumed that the observed
overexpression of PFKM, ALDOC, GAPDH, andENO1 contribute to the activation of glycolysis. For
several tumor types, the main activator of glycolysis is
PFKM, and inhibition of this enzyme significantly reduces the growth and invasion of tumor cells [65,66]. Increased ALDOB and ALDOC expression canlead to Wnt-signaling pathway activation in tumor
cells, which contributes to disease progression [67].
However, in gastric cancer and hepatocellular carcinoma, the opposite effect has been demonstrated, anddecreased ALDOB expression was found to be associated with poor prognosis [68, 69].
Increased ENO1 expression is observed in many typesof malignant tumors and is associated with aggressive
phenotypes [70–72]. Enolase is considered a promisingtarget for anti-cancer therapy [73].
Upregulation of PGK1 is associated with the rapid progression and metastasis of stomach carcinoma [74, 75], as
well as with the progression of breast, prostate, pancreatic,
and ovarian cancers [76–79]. Inhibition of PGK1 may also
represent a method of antitumor therapy [74, 80].
Traditionally, lactate dehydrogenase is thought to be
associated with the progression and aggressiveness of tumors, as lactate accumulation causes microenvironmental acidification, destroys the intercellular matrix, andenhances metastatic spread [81]. Hypermethylation of
the LDHB promoter and decreased gene expression are
observed in various tumor cell lines. These events may
be associated with intensive proliferation, migration, andinvasion [82, 83]. In contrast, in triple-negative breast
cancer cells, increased LDHB expression is observed,
and associations between LHDB overexpression and unfavorable prognosis were demonstrated. However, despite contradictory results, LDHB is an importantparticipant in carcinogenesis and is considered as a potential target for anti-cancer therapy [84].
We revealed activation of some glycolytic genes as wellas suppression of genes involved in gluconeogenesis including G6PC and PCK1. The G6PC enzyme catalyzes
the synthesis of glucose from glucose-6-phosphate,
whereas PCK1 encodes a key gluconeogenesis enzyme
catalyzing the conversion of oxaloacetate to phosphoenolpyruvate. Downregulation of G6PC gene probably
leads to the accumulation of glucose-6-phosphate, theexcess of which can be converted to ribose-5-phosphate
and used for the synthesis of nucleotides [85, 86].
We found the significant activation of ALDH3B2 expression in COAD samples. This gene encodes one ofPage 22 of 56

the isozymes of aldehyde dehydrogenase. It is possiblethat the product of the ALDH3B2 gene could serve as
the enzyme protecting tumor colon cells from ROS (reactive oxygen species), considering that expression ofother alcohol dehydrogenase isozymes (ADH1B and
ADH1C) is decreased [14].
“TCA cycle” KEGG pathway

We found decreased expression of a number of genes involved in the TCA cycle and related processes. Decreased expression of aconitase, encoded by ACO1 andACO2, is described for many types of tumor cells, andrepresents an unfavorable prognostic marker in gastriccancer [87]. The decreased activity of these enzymes increases the amount of citrate in the nucleus, which canthen be sent to the cytosol. Excessive citrate is usuallyconverted to acetyl-CoA, which is actively used bytumor cells for de novo fatty acid biosynthesis. Elevatedcitrate lyase (ACLY) expression indicates active
acetyl-CoA synthesis. A characteristic feature of various
types of malignant tumors is the rapid growth of tumor
biomass and the related activation of lipid biosynthesis[88]. Suppression of ACLY expression reduces the population of cancer stem cells in many cell lines with a wide
range of genetic differences [89].
Decreased expression of genes encoding subunits ofsuccinate dehydrogenase (SDHx) can result in the accumulation of succinate, which helps to stabilize HIF1-a[90]. The HIF1-a transcription factor regulates many
genes involved in carcinogenesis and tumor angiogenesis[91]. Disturbances in the expression of oncogenes andtumor growth suppressor genes, such as p53 andHIF1-a, can directly or indirectly affect the expression ofvarious components of the TCA cycle [92].
In many types of tumor cells, the degree of TCA cycle
productivity is related to glutamine enzyme activity,
which directs glutamine to the cycle in the form ofalpha-ketoglutarate [93], explaining the extreme dependence of certain types of tumors on glutamine [94].
Tumor cells need to maintain a high level of TCA cycle
metabolite biosynthesis, including that of alpha-ketoglutarate and isocitrate, which can be transported to the cytoplasm to participate in the synthesis of nucleotides and anumber of amino acids [95, 96]. Increased expression of
IDH2, encoding for a mitochondrial form of NADP+-dependent isocitrate dehydrogenase, is associated with this.
The IDH2 enzyme is able to perform both direct and reverse reactions, converting isocitrate to alphaketoglutarate and vice versa. Thus, deficiency of any ofthese important intermediates can be compensated.
NAD+-dependent isocitrate dehydrogenase (IDH3) and its
isozymes catalyze the TCA cycle limiting step and theirrole in the development of malignant tumors has been actively investigated [97–99]. Only the IDH3 enzyme onlyFedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

catalyzes a direct reaction, but it is allosterically regulated
and adjusts activity to the needs of the cell [100].
Association of CIMP phenotype with alterations of energy
metabolism

The existence of tumors characterized by simultaneous
hypermethylation of a large number of CpG islands was
described in colorectal cancer and called the CpG island
methylator phenotype [3]. This molecular phenotype occurs in 15–20% of malignant colorectal tumors [101].
Development of CIMP-positive tumors is associated withelderly age, female gender, higher degree of differentiation and mucinous histological type, localization in theproximal colon, mutations in KRAS or BRAF, andwild-type TP53 [25, 102]. The mechanisms, throughwhich aberrant DNA hypermethylation is induced in
CIMP-positive tumors, are not absolutely clear, but there
several factors may be associated with this process.
These include metabolic shift towards energy production
via glycolysis or the development of more pronounced
Warburg effect. Our study revealed CIMP-high associated changes in the expression of genes involved in energy metabolism, including participants of glycolysis,
TCA cycle, as well as related processes. For CIMP-low
COAD, metabolic alterations were reported in different
samplings [6].
Methylation pattern as well as mutational status andother genetic characteristics allowed creating a consensus molecular classification system consisting of four robust subtypes with clear biological interpretability [6].
Distinct differences in the intrinsic biological underpinnings of each molecular group provide the new taxonomy of this disease. Important associations were
found between CMS subgroups and clinical variables, aswell as prognosis. CMS1 tumors were frequently diagnosed in females with right-sided tumors with higher
grade and had very poor survival after relapse. It is likelythat this phenomenon is associated with the presence ofsamples with MSI-high status and BRAF-mutations as
the main part of CMS1 subgroup. CMS2 tumors weremainly left-sided and had superior survival after relapse.
CMS4 tumors tended to be diagnosed at more advanced
stages; they are more aggressive and displayed worse
overall survival [6].
The analysis of TCGA RNA-Seq data in CIMP-highversus non-CIMP COAD samples revealed altered expression levels of many genes involved in energy metabolism, including ENO2, PKLR, and OGDHL. Alterations
observed in ENO2 and OGDHL genes were validated by
qPCR.
Enolase is a glycolytic enzyme that catalyzes the conversion of 2-phosphoglycerate into phosphoenolpyruvate. In mammalian tissues, enolase is represented bythree tissue-specific isozymes [103, 104]. Enolase 2

Page 23 of 56(ENO2) is induced by hypoxia, which typically occurs intumors. Along with other glycolytic genes, ENO2 waspreviously found to be overexpressed in colorectal cancer
and other cancer types [105–107]. ENO2 (alpha-enolase)
is significantly upregulated in a metastatic colon cancer
cell line, suggesting a possible association with the metastatic process in vitro and in vivo [108]. Moreover,ENO2 is a prognostic factor of small cell lung, breast,and prostate cancer [109–112]. qPCR analysis revealed
statistically significant differences in ENO2 expression in
CIMP-high and non-CIMP COAD samples (p = 0.049).
Pyruvate kinase, PKLR, is highly expressed in liver tissues and red blood cells. Analysis of PKLR expression
revealed its association with metastases. PKLR promotes
colon cancer cell metastases to the liver, but does notpromote basal cell growth in culture [113]. PKLR provides cell survival in the central parts of a tumor, where
the cells are in conditions of high density and hypoxia.
Under these conditions, PKLR is required to maintainlevels of the major endogenous antioxidant, a glutathione, and support cancer cell survival [113]. Our results
show that the average mRNA level of PKLR in
CIMP-high tumors is approximately 5-fold lower thanthat in non-CIMP COAD samples. HoweverMann-Whitney test have not been demonstrated statistically significant p-value for this gene (p = 0.165).
OGDHL expression demonstrated the most striking
differences between CIMP-high and non-CIMP samples,
based on both TCGA and qPCR data. This gene encodes
a subunit of the oxoglutarate dehydrogenase complex,
which is involved in the TCA cycle as well as in the induction of apoptosis. Various pathways and factors causing
apoptosis can be classified as either extrinsic or intrinsic.
The extrinsic pathway begins from the activation of
apoptosis-associated receptors by various ligands, including Fas and TNF. Intrinsic apoptotic pathways are induced
by DNA damage, oxidative stress, and other factors [114].
Mitochondria play a central role in both cases. The mitochondrial intermembrane space contains a number of
pro-apoptotic proteins including cytochrome c (Cyto c),endonuclease G, and apoptosis-associated factor (AIF).
Bax/Bak are involved in the formation of the mitochondrial outer membrane permeabilization pore, which leads
to the release of cytochrome c and other proteins into thecytoplasm [115, 116]. In turn, cytochrome c causes
oligomerization of the Apaf-1 and further assembly of theapoptosome, which is responsible for cleavage of
pro-caspase 9, a key step in apoptosis [16, 115, 117]. Mitochondria and the glutarate dehydrogenase complex in particular, are the main source of free radicals within the cell[118]. It was shown that OGDHL, and its close homolog
OGDH, are localized in the mitochondria, and that overexpression of OGDHL results in increased free radical
content and lipid peroxidation [119]. In present work a

Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

significant decrease was found in OGDHL expression in
CIMP-high COAD samples compared to non-CIMP ones.
And at least nine CpG sites have sufficiently different
methylation pattern in these two groups. Probably theseparticular CpG sites along with decrease expression level
could serve as additional markers of high methylationlevel across the genome.
Therefore, we have demonstrated that COAD samples
characterized by CIMP-high status (mostly represented
with CMS1 group) carry metabolic alterations together
with activation of immune-related pathways. Partially,
these changes could be explained by the presence oftranscripts originated from the tumor infiltrating
lymphocytes.

Conclusion
We have revealed that the energy metabolic changes
manifested in the overexpression of glycolytic genes
along with the downregulation of genes participating in
TCA cycle are associated with the CIMP-high status in
COAD. This results are consistent with the conception
assuming that hypermethylation of cancer genome iscorrelated with the increased glycolytic rate. At the sametime, we also found significant activation of the genes involved in immune-related pathways in CIMP-high
COAD samples, which is a hallmark of the "Immune"
subtype (CMS1) and represents an important prognostic
factor of colorectal cancer. Probably, all common
CRC molecular characteristics are intertwined and occur
in several CMS subtypes.
Additional files
Additional file 1: Figure S1. Differential expression profiles of“Glycolysis/Gluconeogenesis” KEGG pathway genes in colon
adenocarcinomas compared to normal tissues (TCGA data). The color
scale reflects the base-2 logarithm of the ratio of the gene expression
level in tumor tissues to the expression level in normal tissues. Red color
indicates upregulated genes, green - downregulated. The scale is relative;
minimal and maximal values in the color scale are corresponding to thegreatest decreased or increased expression level among the all analyzed
genes. (PDF 5115 kb)
Additional file 2: Table S1. Differential expression profiles of“Glycolysis/Gluconeogenesis” KEGG pathway genes in colon
adenocarcinomas compared to normal tissues (TCGA data). The color
scale indicates the degree of change. (XLSX 836 kb)
Additional file 3: Figure S2. Differential expression profiles of “TCA
cycle” KEGG pathway genes in colon adenocarcinomas compared tonormal tissues (TCGA data). The color scale reflects the base-2 logarithmof the ratio of the gene expression level in tumor tissues to theexpression level in normal tissues. Red color indicates upregulated genes,
green - downregulated. The scale is relative; minimal and maximal values
in the color scale are corresponding to the greatest decreased orincreased expression level among the all analyzed genes. (PDF 173 kb)
Additional file 4: Table S2. Differential expression profiles of “TCA
cycle” KEGG pathway genes and transcripts in colon adenocarcinomas
compared to normal tissues (TCGA data). The color scale indicates thedegree of change. (XLSX 516 kb)

Page 24 of 56

Additional file 5: Figure S3. Differential expression profiles of“Glycolysis/Gluconeogenesis” KEGG pathway genes in CIMP-high colon
adenocarcinomas compared to non-CIMP ones (TCGA data). The color scalereflects the base-2 logarithm of the ratio of the gene expression level in
CIMP-high tumors to the expression level in non-CIMP tumors. Red color
indicates upregulated genes, green - downregulated. The scale is relative;
minimal and maximal values in the color scale are corresponding to thegreatest decreased or increased expression level among the all analyzed
genes. (PDF 4613 kb)
Additional file 6: Table S3. Differential expression profiles of“Glycolysis/Gluconeogenesis” KEGG pathway genes in CIMP-high colon
adenocarcinomas compared to non-CIMP ones (TCGA data). The color
scale indicates the degree of change. (XLSX 266 kb)
Additional file 7: Figure S4. Differential expression profiles of “TCA
cycle” KEGG pathway genes in CIMP-high colon adenocarcinomas
compared to non-CIMP (TCGA data). The color scale reflects the base-2
logarithm of the ratio of the gene expression level in CIMP-high tumors
to the expression level in non-CIMP tumors. Red color indicates
upregulated genes, green - downregulated. The scale is relative; minimaland maximal values in the color scale are corresponding to the greatest
decreased or increased expression level among the all analyzed genes.
(PDF 928 kb)
Additional file 8: Table S4. Differential expression profiles of “TCA
cycle” KEGG pathway genes in CIMP-high colon adenocarcinomas
compared to non-CIMP tumors (TCGA data). The color scale indicates thedegree of change. (XLSX 166 kb)

Abbreviations
CIMP: CpG island methylator phenotype; COAD: Colon adenocarcinoma;
GSEA: Gene set enrichment test; MSI: Microsatellite instability; TCGA: The
Cancer Genome Atlas
Acknowledgments
Authors thank National Medical Research Radiological Center and State
Hospital №57 for tissue samples. This work was performed using theequipment of EIMB RAS “Genome” center (http://www.eimb.ru/rus/ckp/
ccu_genome_c.php).
Funding
This work and publication costs were financially supported by the Russian
Science Foundation, grant no. 14–15-01083.
Availability of data and materials
All data generated or analyzed during this study are included in thispublished article and its Additional files.
About this supplement
This article has been published as part of BMC Medical Genetics Volume 20
Supplement 1, 2019: Selected articles from BGRS\SB-2018: medical genetics. The
full contents of the supplement are available online at https://
bmcmedgenet.biomedcentral.com/articles/supplements/volume-20supplement-1.
Authors’ contributions
AVK, MSF, GSK, ADK, DVS, and IAI wrote the manuscript; AVK, ARZ, NVM,
AYP, and AAM conceived and designed the work; MSF, SLK, EAP, and ZGG
performed the experiments; ENS, AAK, AVL, MAC, and MVK analyzed theexperiments; ENL, AVS, AAD, GSK, and IAI performed bioinformatics analysis.
All authors read and approved the final manuscript.
Ethics approval and consent to participate
The study was approved by The Ethics committee of Hertzen Moscow
Cancer Research Institute, branch of the National Medical ResearchRadiological Center, Ministry of Health of the Russian Federation. All
participants gave their written informed consent to take part in the study.
The study was done in accordance with the principles outlined in the
Declaration of Helsinki (1964).

Fedorova et al. BMC Medical Genetics 2019, 20(Suppl 1):52

Page 25 of 56

Consent for publication
Not applicable.
16.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences,
Moscow, Russia. 2National Medical Research Radiological Center, Ministry of
Health of the Russian Federation, Moscow, Russia. 3State Hospital №57,
Moscow, Russia.

17.

18.

19.

20.
Published: 9 April 2019
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