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Upregulation of NETO2 gene in colorectal cancer

  • Maria S. Fedorova1,
  • Anastasiya V. Snezhkina1,
  • Elena A. Pudova1,
  • Ivan S. Abramov1,
  • Anastasiya V. Lipatova1,
  • Sergey L. Kharitonov1,
  • Asiya F. Sadritdinova1,
  • Kirill M. Nyushko2,
  • Kseniya M. Klimina3,
  • Mikhail M. Belyakov2,
  • Elena N. Slavnova2,
  • Nataliya V. Melnikova1,
  • Maria A. Chernichenko2,
  • Dmitry V. Sidorov2,
  • Marina V. Kiseleva2,
  • Andrey D. Kaprin2,
  • Boris Y. Alekseev2,
  • Alexey A. Dmitriev1 and
  • Anna V. Kudryavtseva1, 2Email author
Contributed equally
BMC GeneticsBMC series – open, inclusive and trusted201718(Suppl 1):117

https://doi.org/10.1186/s12863-017-0581-8

Published: 28 December 2017

Abstract

Background

Neuropilin and tolloid-like 2 (NETO2) is a single-pass transmembrane protein that has been shown primarily implicated in neuron-specific processes. Upregulation of NETO2 gene was also detected in several cancer types. In colorectal cancer (CRC), it was associated with tumor progression, invasion, and metastasis, and seems to be involved in epithelial-mesenchymal transition (EMT). However, the mechanism of NETO2 action is still poorly understood.

Results

We have revealed significant increase in the expression of NETO2 gene and deregulation of eight EMT-related genes in CRC. Four of them were upregulated (TWIST1, SNAIL1, LEF1, and FOXA2); the mRNA levels of other genes (FOXA1, BMP2, BMP5, and SMAD7) were decreased. Expression of NETO2 gene was weakly correlated with that of genes involved in the EMT process.

Conclusions

We found considerable NETO2 upregulation, but no significant correlation between the expression of NETO2 and EMT-related genes in CRC. Thus, NETO2 may be involved in CRC progression, but is not directly associated with EMT.

Keywords

Colorectal cancerNETO2Epithelial-mesenchymal transitionGene expressionQPCR

Background

Colorectal cancer (CRC) is the third most common malignancy in developed countries, and furthermore, its incidence rate has continuously increased over the past few decades [1]. While early-stage CRC can be effectively treated with radical surgery, approximately 20% of CRC patients present with advanced-stage disease at the time of initial diagnosis. These patients frequently have  metastases  that result in increased risk of death even after radical surgery [2]. CRC is characterized by multiple genetic and epigenetic changes that affect metabolic and signaling pathways [36]. For instance, cancer cells have a higher glycolytic rate than normal ones [79], and, as a consequence, the terminal glycolytic metabolite lactate is exported to the extracellular matrix contributing the extracellular acidosis [10]. The acidic extracellular pH (pH e ), in turn, can induce epithelial-mesenchymal transition (EMT) in carcinoma models and is closely associated with tumor metastasis [11, 12]. Thus, in addition to improving the current understanding of the mechanisms underlying CRC metastasis, it is important to identify novel components of EMT process that may be the potential biomarkers of the disease progression and can further contribute to both the selection of optimal treatment options and effective treatment monitoring for patients with CRC.

NETO2 gene is localized on chromosome 16 and encodes a transmembrane glycoprotein of unknown function. It has been shown that the abundant expression of NETO2 protein in neurons is essential for proper neurological function [13, 14]. Initially, NETO2 was believed to be a brain-specific protein [15, 16]; however, recent studies described overexpression of NETO2 in several types of cancer, including renal, lung, colon, and cervical carcinomas [17]. Accordingly, Hu et al. recently suggested high expression of NETO2 as a potential biomarker of both advanced tumor progression and poor prognosis in patients with CRC [18].

In the present study, we hypothesized that the association of NETO2 overexpression with tumor progression, invasion, and metastasis may be indicative of its involvement in the epithelial-mesenchymal transition in CRC. To investigate the validity of this hypothesis, we evaluated whether NETO2 expression was correlated with that of genes established to mediate the EMT process.

Methods

Tissue samples

A total of 44 CRC and matched morphologically normal tissue samples, which were obtained after surgical resection, but prior to patient treatment with radiation and/or chemotherapy, were frozen and stored in liquid nitrogen until use. All CRC samples were classified according to the American Joint Committee on Cancer (AJCC) staging system [19], and only those samples comprising 70% or more tumor cells were selected for analysis. Written informed consent was obtained from all patients for participation in the present study, which was approved by Herzen Moscow Cancer Research Institute - branch of National Medical Research Radiological Center, Ministry of Health of Russia Federation (Moscow, Russia), and conducted in strict accordance with the principles outlined in the Declaration of Helsinki (1964). Clinicopathologic characteristics of the CRC patients are shown in Table 1.
Table 1

Clinicopathologic characteristics of the CRC patients

Characteristic

Total, n

Gender

 Male

23

 Female

21

Age (years)

 ≤ 60

14

 > 60

30

Clinical stage

 I

2

 II

11

 III

15

 IV

16

Distant metastases (Stage IV)

 Negative

4

 Positive

12

RNA isolation and cDNA synthesis

Total RNA was isolated from the frozen tissue samples using RNeasy Mini kit (Qiagen, Germany) according to manufacturer’s instructions. RNA quality was measured via the RNA Integrity Number (RIN) method using an Agilent RNA Bioanalyzer 2100 (Agilent Technologies, USA). RNA quantification was performed using a NanoDrop 1000 instrument (NanoDrop Technologies, USA). cDNA was synthesized from the isolated RNA using M-MLV Reverse Transcriptase (Thermo Fisher Scientific, USA) and random hexamers.

Quantitative PCR (qPCR)

Quantitative polymerase chain reaction was performed using TaqMan Assay (Thermo Fisher Scientific) primers and probes for target genes (NETO2: Hs00983152_m1, TWIST1: Hs00361186_m1, SNAIL1: Hs00195591_m1, SNAIL2: Hs00161904_m1, ZEB1: Hs01566408_m1, ZEB2: Hs00207691_m1, LEF1: Hs01547250_m1, FOXA2: Hs00232764_m1, FOXA1: Hs04187555_m1, CDH1: Hs01023895_m1, STAT1: Hs00374280_m1, BMP2: Hs00154192_m1, BMP5: Hs00234930_m1, VIM: Hs00958111_m1, SMAD2: Hs00998187_m1, SMAD3: Hs00969210_m1, SMAD4: Hs00929647_m1, SMAD7: Hs00998193_m1). Primers and probes for reference genes, GUSB and RPN1, were previously described [20, 21]. All qPCRs were carried out in triplicate in total reaction volume of 20 μL using an AB 7500 Real-Time PCR System (Thermo Fisher Scientific) to achieve cycling conditions comprising 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s, 60 °C for 60 s, and 72 °C for 30 s.

QPCR data were analyzed using Relative Quantitation (Thermo Fisher Scientific) software and ATG program taking into account the efficiency of the PCR amplification [22, 23]. The expression levels of target genes were normalized to those of the reference genes. Finally, relative (T/N) expression level of target genes was calculated using the ΔΔCt method [24]. Since the relative inner variability between the calculated mRNA levels of the reference genes was found to be less than two-fold, a variation in the expression of the target genes of two-fold or greater was considered to be significant.

Statistical analysis

Inter- and intra-group comparisons were performed using non-parametric Wilcoxon/Mann-Whitney and Kruskal-Wallis tests. Spearman’s rank correlation coefficient (r s ) was used for revealing correlations between NETO2 and EMT-related gene expression. All statistical analyses were performed using PASW Statistics 18 (SPSS Inc., USA) software. A p-value < 0.05 was considered to indicate statistical significance.

Results

Upregulation of NETO2 gene in CRC

QPCR analysis of the relative NETO2 mRNA level across the 44 CRC samples revealed that NETO2 expression was increased by a factor of 2–50 in 41% (18/44) of cases (Fig. 1). In contrast, NETO2 expression was decreased by a factor of 2–25 in 14% (6/44) of CRC samples. These results demonstrating a significant increase in the expression of NETO2 in the analyzed CRC samples are consistent with those of the previous study by Oparina et al. [17].
Figure 1
Fig. 1

Relative mRNA level of NETO2 gene in CRC. QPCR data

Deregulation of EMT-related genes in CRC

We performed an analysis of the relevant literature and selected 17 genes related to EMT process in CRC (Table 2). Using qPCR, mRNA levels of these genes were analyzed in 44 CRC samples (Table 3).
Table 2

Genes involved in the epithelial-mesenchymal transition in CRC

Gene

Description

References

TWIST1

TWIST1 is a highly conserved basic helix-loop-helix (bHLH) transcription factor that regulates the EMT required for neural crest migration during vertebrate embryonic development. TWIST1 expression is positively associated with patient survival after curative CRC resection, and thus is a promising candidate biomarker of the disease progression.

[33]

[34]

[35]

SNAIL1

SNAIL1 is a transcriptional regulator of E-cadherin, which suppression is critical to facilitate the EMT process. SNAIL1 mRNA level is not detectable in the normal colon mucosa, but is upregulated in 60–70% of CRC. Importantly, aberrant SNAIL1 expression in CRC has been shown to be associated not only with poor patient prognosis, but also with a reduced relapse-free survival time.

[36]

[37]

[38]

[39]

[40]

SNAIL2

SNAIL2 has been implicated as an anti-apoptotic factor, and is thought to mediate the EMT process by repressing E-cadherin transcription. Accordingly, SNAIL2 expression in human CRC cell lines has been shown to be correlated with critical EMT properties, including the loss of E-cadherin expression and an increase in both cell migration and invasion.

[41]

[42]

ZEB1

ZEB1 mediates the EMT pathway, and in fact has been shown to be not only sufficient to induce the EMT, but also necessary for maintaining the adapted mesenchymal phenotype. ZEB1 contains zinc finger clusters in both its N- and C-terminal regions, and a homeodomain in the central region. In CRC cells, ZEB1 has been shown to critically mediate the EMT, and thus may be an important regulator of CRC metastasis.

[43]

[44]

[45]

ZEB2

ZEB2 is a member of the Zfh1 family of two-handed zinc-finger transcription factors. It is frequently expressed in colon cancer, and has been shown by several previous studies to induce the EMT, and to facilitate cancer-cell metastasis, possibly via the repression and upregulation of E-cadherin and vimentin respectively.

[46]

[47]

[48]

LEF1

LEF1 is critical for tumor-cell adhesion and/or migration, and thus, also for tumor invasion and metastasis. In addition, it plays a pivotal role in carcinogenesis and CRC progression, partly via its function in the LEF1/β-catenin complex, which is a crucial effector of the Wnt signaling pathway. Increased LEF1 expression has been shown to be correlated with node and distant metastasis, and with an advanced tumor stage. Furthermore, LEF1 was shown to be involved in CRC invasion and metastasis.

[49]

[50]

[51]

FOXA1 and FOXA2

Forkhead box (FOX) protein A1 (FOXA1) is a transcription factor belonging to the FOX gene superfamily that mediates fundamental developmental and differentiation processes. Specifically, it modulates transcriptional programs in a tissue-dependent manner by inducing nucleosomal rearrangement, and by altering chromatin accessibility to the transcriptional machinery. FOXA1 has been shown to be overexpressed in CRC, and furthermore, to be positively associated with poor clinicopathological features. This suggests that its expression may be a promising candidate prognostic biomarker for patients with CRC. FOXA2 is a known key regulator of CRC metastasis to the liver.

[52]

[53]

[54]

[55]

CDH1

CDH1 gene encodes a classical cadherin. The E-cadherin-mediated cell adhesion system is required for both the EMT, and for cellular invasion, angiogenesis, and metastatic/tumor progression in many cancers, including CRC.

[56]

STAT1

STAT1 is a signal mediator that controls cell-death functions in the context of both pro-apoptotic and anti-proliferative interferon-dependent signaling. It appears to exhibit tumor suppressive functions, and its activity has been shown to be associated with a favorable patient prognosis in some cancers.

[57]

[58]

BMP2 and BMP5

Bone morphogenetic proteins (BMPs) are the secreted ligands of the proteins belonging to the transforming growth factor beta superfamily (TGFβ), and are important regulators of body-axis patterning during embryogenesis. In adult tissues, they regulate cell growth, apoptosis, and differentiation. The biological effects of BMPs have been predominantly studied in mesoderm-derived cells and tissues, and to a lesser degree, in epithelial cells and tissues. In general, BMPs are involved in the regulation of cancer progression and metastasis possibly through TGF-β-induced SMAD3-dependent EMT. Inactivation of BMP signaling increases the tumorigenicity of normal colon stem cells.

[59]

[60]

[61]

VIM

VIM is a Wnt-targeted gene that is expressed in normal mesenchymal cells, and that encodes the intermediate filament protein, vimentin. Previous studies have shown that vimentin mediates both cellular structure and integrity. Furthermore, vimentin has also been demonstrated to mediate cell shape and motility during the EMT process, which is required for cancer-cell metastasis.

[62]

[63]

[64]

[65]

SMAD2, SMAD3, SMAD4, and SMAD7

The SMADs are a family of structurally related signaling proteins that can be divided into three subgroups according to their respective functions in TGFβ signaling. Specifically, the receptor-activated SMADs, including SMAD2 and SMAD3, are serine-phosphorylated following TGF-receptor complex formation. The unique SMAD4 co-SMAD (which is common to both TGFβ and BMP signaling), then interacts with the phosphorylated SMAD2/SMAD3. The resulting heteropolymer migrates to the nucleus and complexes with tissue-specific transcription factors, thereby inducing the transcription of TGFβ target genes, including SMAD7. Finally, SMAD7, which is the only TGFβ-specific anti-SMAD, prevents SMAD2/3 activation, thereby providing a transient TGFβ response in the form of a negative feedback loop. Immunohistochemical analysis has revealed the expression of SMADs during EMT process in CRC.

[66]

[67]

[68]

[69]

Table 3

Relative mRNA levels of EMT-related genes in CRC

Gene

Frequency of mRNA level changes, %

Median of mRNA level changes, n-fold

↑ increased expression

↓ decreased expression

TWIST1

68 (30/44)

5 (2/44)

2.8↑

SNAIL1

80 (35/44)

2 (1/44)

3.3↑

SNAIL2

11 (5/44)

20 (9/44)

1.2↓

ZEB1

9 (4/44)

36 (16/44)

1.5↓

ZEB2

7 (3/44)

45 (20/44)

1.7↓

LEF1

75 (33/44)

2 (1/44)

3.9↑

FOXA1

7 (3/44)

52 (23/44)

2.1↓

FOXA2

59 (26/44)

5 (2/44)

2.5↑

CDH1

5 (2/44)

16 (7/44)

1.3↓

STAT1

25 (11/44)

5 (2/44)

1.4↑

BMP2

2 (1/44)

75 (33/44)

3.2↓

BMP5

7 (3/44)

84 (37/44)

7.6↓

VIM

18 (8/44)

7 (3/44)

1.3↑

SMAD2

0 (0/44)

11 (5/44)

1.4↓

SMAD3

0 (0/44)

11 (5/44)

1.2↓

SMAD4

0 (0/44)

23 (10/44)

1.5↓

SMAD7

0 (0/44)

43 (19/44)

1.8↓

Note: Significant frequencies (p < 0.05) are marked in bold

TWIST1 gene

Up to 26-fold increase in the expression of TWIST1 gene was revealed in the majority (68%, 30/44) of CRC samples compared to matched normal tissues. In contrast, two CRC samples exhibited decreased TWIST1 expression from 4- to 6-fold. The mean value of relative mRNA level of TWIST1 gene was 2.8.

SNAIL1 and SNAIL2 genes

Quantitative analysis of SNAIL1 expression showed it to be significantly increased in 80% (35/44) of CRC cases. mRNA level of SNAI1 gene was decreased by a factor of 6 only in one sample. The expression of SNAIL2 was found to be decreased by a factor of 2–25 in 20% (9/44) of CRC samples, and increased by a factor of 2–3 in 11% (5/44) of ones. The mean value of relative mRNA levels of SNAI1 and SNAIL2 genes were 3.3 and 1.2, respectively.

ZEB1 and ZEB2 genes

Analysis of ZEB1 gene expression revealed it to be decreased by a factor of 2–48 in 36% (16/44) and increased in 9% (4/44) of CRC samples. The expression of ZEB2 gene was decreased in 45% (19/44) and increased in 7% (3/44) of CRC cases. The mean value of relative mRNA levels of ZEB1 and ZEB2 genes were 1.5 and 1.7, respectively.

LEF1 gene

LEF1 gene expression was increased by a factor of 2–52 in 75% (33/44) of CRC cases, and slightly decreased by a factor of two only in one sample. The mean value of relative mRNA level of LEF1 gene was 3.9.

FOXA1 and FOXA2 genes

The analysis of FOXA1 and FOXA2 gene expression showed that while FOXA1 expression was decreased by a factor of 2–79 in 52% (23/44) of CRC samples, FOXA2 expression was increased from 2- to 23-fold in 59% (26/44) of cases. Up to 4-fold increase in the expression of FOXA1 gene was detected in 7% (3/44) of examined samples. FOXA2 gene was downregulated by a factor of 2–70 in 5% (2/44) of CRC cases. The mean value of relative mRNA levels of FOXA1 and FOXA2 genes were 2.1 and 2.5, respectively.

CDH1 gene

The analysis of CDH1 gene expression showed it to be decreased by a factor of 2–86 in 16% (7/44) of CRC samples, and increased by a factor of two in 5% (2/44) of cases. The mean value of relative mRNA level of CDH1 gene was 1.3.

STAT1 gene

Quantification of STAT1 gene expression revealed it to be increased by a factor of 2–4 in 25% (11/44) of cases, and decreased by a factor of 3 in 5% (2/44) of CRC samples. The mean value of relative mRNA level of STAT1 gene was 1.4.

BMP2 and BMP5 genes

The expression of both BMP2 and BMP5 genes was suppressed in 75% (33/44) and 84% (37/44) of examined CRC samples, respectively. Increase in the BMP2 gene expression was shown in only one sample (2%), while that of BMP5 gene was detected in 7% (3/44) of CRC cases. The mean value of relative mRNA levels of BMP2 and BMP5 genes were 3.2 and 7.6, respectively.

VIM gene

The analysis of VIM expression showed it to be increased by a factor of 2–6 in 18% (8/44) of CRC samples, and decreased by a factor of 2–4 in 7% (3/44) of cases. The mean value of relative mRNA level of VIM gene was 1.3.

SMAD2, SMAD3, SMAD4, and SMAD7 genes

QPCR analysis showed SMAD2, SMAD3, SMAD4, and SMAD7 mRNA levels to be decreased by a factor of 2–10 in 11–43% of the examined CRC samples. The mean value of relative mRNA levels of SMAD2, SMAD3, SMAD4, and SMAD7 genes were 1.4, 1.2, 1.5, and 1.8, respectively.

mRNA level of NETO2 is not correlated with that of EMT-related genes in CRC

We used the Spearman’s correlation coefficient to test the proposed hypothesis that NETO2 mRNA level in CRC correlates with that of the EMT-related genes. The results of this analysis showed that across the 44 analyzed CRC samples, 17 association pairs were identified between NETO2 and various genes involved in EMT, all of which exhibited weak relationship (Table 4). The most significant correlations were determined between NETO2 and SMAD7 expression (r s  = 0.25, p < 0.05) and between NETO2 and TWIST1 expression (r s  = −0.24, p < 0.05). These results indicate that the expression of NETO2 in CRC is only weakly correlated with that of EMT-related genes.
Table 4

Spearman’s correlation coefficients between mRNA levels of NETO2 and EMT-related genes

Gene

Spearman’s correlation coefficient, r s

NETO2

TWIST1

−0.24

SNAIL1

−0.12

SNAIL2

−0.07

ZEB1

−0.05

ZEB2

0.03

LEF1

0.06

FOXA1

0.06

FOXA2

0.06

CDH1

0.11

STAT1

0.12

BMP2

0.12

BMP5

0.14

VIM

0.18

SMAD2

0.21

SMAD3

0.23

SMAD4

0.24

SMAD7

0.25

Discussion

The NETO2 gene encodes a transmembrane protein that is predominantly expressed in normal brain and retinal tissues. Thus, previous studies have primarily focused on NETO2 function in the context of neurobiology; in vitro analyses have revealed that NETO2 interacts with the GluK2 and GluK5 subunits of kainate receptors to significantly enhance kainate receptor-mediated signaling [25]. Recently, NETO2 has been shown to be involved in carcinogenesis. In a mutant cell line overexpressing metastasis-suppressor gene NM23-H1, which can reduce the metastatic potential of various types of cancer cells, NETO2 was amongst the nine genes identified to exhibit increased mRNA level [26]. NETO2 expression was reported to be upregulated in proliferating pediatric hemangiomas [27]. Notably, we previously demonstrated that NETO2 mRNA level is frequently overexpressed in kidney and lung cancers, and resultantly suggested it as a potential marker to early diagnosis of these diseases [17]. Hu and co-authors suggested both the potential significance of NETO2 expression in CRC carcinogenesis and its clinical relevance to the disease progression, invasion, and metastasis [18].

The EMT process is well established to be required not only for embryonic development, but also for cancer progression and metastasis, since it facilitates the acquisition of invasive properties that allow cancer cells to enter the surrounding stroma and thereby generate a favorable tumor microenvironment [2830]. Moreover, EMT process is known to be closely associated with cancer recurrence and chemoresistance. Nevertheless, the mechanisms underlying the involvement of EMT process in these events seem to vary significantly between cancer types.

To date, NETO2 is known to be associated with poor prognosis and metastasis in CRC, but not with the occurrence of EMT in this context. Thus, the present study investigated whether NETO2 expression in CRC was correlated with that of key genes involved in the EMT, including TWIST1, SNAIL1, SNAIL2, ZEB1, ZEB2, LEF1, FOXA2, FOXA1, CDH1, STAT1, BMP2, BMP5, VIM, SMAD2, SMAD3, SMAD4, and SMAD7. The results obtained in the work confirmed that NETO2 is overexpressed in CRC. It has also been demonstrated that several genes involved in the EMT process were upregulated in CRC compared to matched normal tissues, including TWIST1, SNAIL1, LEF1, and FOXA2, which mRNA levels were increased by an average factor of 2.8, 3.3, 3.9, and 2.5 (median) respectively. Conversely, the mRNA levels of FOXA1, BMP2, BMP5, and SMAD7 genes were found to be decreased by a factor of 2.1, 3.2, 7.6, and 1.8 (median) respectively that is again in concordance with the results of recently studies [31, 32].

Notably, we found no significant correlation between the expression of NETO2 gene and that of the analyzed EMT-related genes in CRC. Thus, it is likely that NETO2 is involved in CRC progression, but is not directly associated with EMT.

Conclusions

NETO2 expression was found to be considerably increased, but not significantly correlated with the mRNA levels of EMT-related genes in CRC. Thus, while NETO2 overexpression may be indicative of poor clinical prognosis and metastasis, this is unlikely to be a direct result of alterations in the EMT process. Certainly, the molecular basis for and biological relevance of NETO2 upregulation in CRC requires further investigation.

Declarations

Acknowledgments

Authors thank National Medical Research Center of Radiology for supplying tumor samples and clinicopathologic data; Vavilov Institute of General Genetics for the help with data analysis; Initium-Pharm, LTD for providing computational resources.

This work was performed using the equipment of EIMB RAS “Genome” center (http://www.eimb.ru/rus/ckp/ccu_genome_c.php).

Funding

This work and publication costs were funded by the Russian Science Foundation, grant 14–15-01083.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

About this supplement

This article has been published as part of BMC Genetics Volume 18 Supplement 1, 2017: Selected articles from Belyaev Conference 2017: genetics. The full contents of the supplement are available online at https://bmcgenet.biomedcentral.com/articles/supplements/volume-18-supplement-1.

Authors’ contributions

MSF, AVS, and AVK conceived and designed the work; MSF, AVS, ISA, AVL, SLK, AFS, KMN, MMB, ENS, MAC, and DVS performed the experiments; MSF, AVS, EAP, KMK, NVM, AAD, AVK, and MVK analyzed the data; MSF, EAP, AAD, AVK, ADK, and BYA wrote the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study was approved by The Ethics committee of Herzen Moscow Cancer Research Institute - branch of National Medical Research Radiological Center, Ministry of Health of Russia Federation. The study was done in accordance with the principles outlined in the Declaration of Helsinki (1964).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis 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, and reproduction 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.

Authors’ Affiliations

(1)
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
(2)
National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia
(3)
Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia

References

  1. Markowitz SD, Bertagnolli MM. Molecular origins of cancer: molecular basis of colorectal cancer. N Engl J Med. 2009;361(25):2449–60.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Manfredi S, Lepage C, Hatem C, Coatmeur O, Faivre J, Bouvier AM. Epidemiology and management of liver metastases from colorectal cancer. Ann Surg. 2006;244(2):254–9.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Snezhkina AV, Krasnov GS, Zaretsky AR, Zhavoronkov A, Nyushko KM, Moskalev AA, Karpova IY, Afremova AI, Lipatova AV, Kochetkov DV, et al. Differential expression of alternatively spliced transcripts related to energy metabolism in colorectal cancer. BMC Genomics. 2016;17(Suppl 14):1011.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Kudryavtseva AV, Fedorova MS, Zhavoronkov A, Moskalev AA, Zasedatelev AS, Dmitriev AA, Sadritdinova AF, Karpova IY, Nyushko KM, Kalinin DV, et al. Effect of lentivirus-mediated shRNA inactivation of HK1, HK2, and HK3 genes in colorectal cancer and melanoma cells. BMC Genet. 2016;17(Suppl 3):156.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Snezhkina AV, Krasnov GS, Lipatova AV, Sadritdinova AF, Kardymon OL, Fedorova MS, Melnikova NV, Stepanov OA, Zaretsky AR, Kaprin AD, et al. The Dysregulation of polyamine metabolism in colorectal cancer is associated with Overexpression of c-Myc and C/EBPbeta rather than Enterotoxigenic Bacteroides Fragilis infection. Oxidative Med Cell Longev. 2016;2016:2353560.View ArticleGoogle Scholar
  6. Fedorova MS, Kudryavtseva AV, Lakunina VA, Snezhkina AV, Volchenko NN, Slavnova EN, Danilova TV, Sadritdinova AF, Melnikova NV, Belova AA, et al. Downregulation of OGDHL expression is associated with promoter hypermethylation in colorectal cancer. Mol Biol. 2015;49(4):608–17.View ArticleGoogle Scholar
  7. Oparina NY, Snezhkina AV, Sadritdinova AF, Veselovskii VA, Dmitriev AA, Senchenko VN, Mel'nikova NV, Speranskaya AS, Darii MV, Stepanov OA, et al. Differential expression of genes that encode glycolysis enzymes in kidney and lung cancer in humans. Russ J Genet. 2013;49(7):707–16.View ArticleGoogle Scholar
  8. Krasnov GS, Dmitriev AA, Snezhkina AV, Kudryavtseva AV. Deregulation of glycolysis in cancer: glyceraldehyde-3-phosphate dehydrogenase as a therapeutic target. Expert Opin Ther Targets. 2013;17(6):681–93.View ArticlePubMedGoogle Scholar
  9. Krasnov GS, Dmitriev AA, Lakunina VA, Kirpiy AA, Kudryavtseva AV. Targeting VDAC-bound hexokinase II: a promising approach for concomitant anti-cancer therapy. Expert Opin Ther Targets. 2013;17(10):1221–33.View ArticlePubMedGoogle Scholar
  10. Huang R, Zong X. Aberrant cancer metabolism in epithelial-mesenchymal transition and cancer metastasis: mechanisms in cancer progression. Crit Rev Oncol Hematol. 2017;115:13–22.View ArticlePubMedGoogle Scholar
  11. Suzuki A, Maeda T, Baba Y, Shimamura K, Kato Y. Acidic extracellular pH promotes epithelial mesenchymal transition in Lewis lung carcinoma model. Cancer Cell Int. 2014;14(1):129.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Kato Y, Ozawa S, Miyamoto C, Maehata Y, Suzuki A, Maeda T, Baba Y. Acidic extracellular microenvironment and cancer. Cancer Cell Int. 2013;13(1):89.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Michishita M, Ikeda T, Nakashiba T, Ogawa M, Tashiro K, Honjo T, Doi K, Itohara S, Endo S. Expression of Btcl2, a novel member of Btcl gene family, during development of the central nervous system. Brain Res Dev Brain Res. 2004;153(1):135–42.View ArticlePubMedGoogle Scholar
  14. Finelli P, Sirchia SM, Masciadri M, Crippa M, Recalcati MP, Rusconi D, Giardino D, Monti L, Cogliati F, Faravelli F, et al. Juxtaposition of heterochromatic and euchromatic regions by chromosomal translocation mediates a heterochromatic long-range position effect associated with a severe neurological phenotype. Mol Cytogenet. 2012;5:16.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Stohr H, Berger C, Frohlich S, Weber BH. A novel gene encoding a putative transmembrane protein with two extracellular CUB domains and a low-density lipoprotein class a module: isolation of alternatively spliced isoforms in retina and brain. Gene. 2002;286(2):223–31.View ArticlePubMedGoogle Scholar
  16. Zhang W, St-Gelais F, Grabner CP, Trinidad JC, Sumioka A, Morimoto-Tomita M, Kim KS, Straub C, Burlingame AL, Howe JR, et al. A transmembrane accessory subunit that modulates kainate-type glutamate receptors. Neuron. 2009;61(3):385–96.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Oparina NYu, Sadritdinova AF, Snezhkina AV, Dmitriev AA, Krasnov GS, Senchenko VN, Melnikova NV, Belenikin MS, Lakunina VA, Veselovsky VA, et al. Increase in gene expression is a potential molecular genetic marker in renal and lung cancers. Russ J Genet. 2012;48(5):506–12.View ArticleGoogle Scholar
  18. Hu L, Chen HY, Cai J, Yang GZ, Feng D, Zhai YX, Gong H, Qi CY, Zhang Y, Fu H, et al. Upregulation of NETO2 expression correlates with tumor progression and poor prognosis in colorectal carcinoma. BMC Cancer. 2015;15:1006.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Hari DM, Leung AM, Lee JH, Sim MS, Vuong B, Chiu CG, Bilchik AJ. AJCC cancer staging manual 7th edition criteria for colon cancer: do the complex modifications improve prognostic assessment? J Am Coll Surg. 2013;217(2):181–90.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Krasnov GS, Dmitriev AA, Sadtritdinova AF, Fedorova MS, Snezhkina AV, Melnikova NV, Poteryakhina AV, Nyushko KM, Belyakov MM, Kaprin AD, et al. Evaluation of gene expression of Hexokinases in colorectal cancer with the use of bioinformatics methods. Biofizika. 2015;60(6):1050–6.PubMedGoogle Scholar
  21. Krasnov GS, Oparina NYu, Dmitriev AA, Kudryavtseva AV, Anedchenko EA, Kondrat’eva TT, Zabarovsky ER, Senchenko VN. RPN1, a new reference gene for quantitative data normalization in lung and kidney cancer. Mol Biol. 2011;45(2):211–20.View ArticleGoogle Scholar
  22. Melnikova NV, Dmitriev AA, Belenikin MS, Koroban NV, Speranskaya AS, Krinitsina AA, Krasnov GS, Lakunina VA, Snezhkina AV, Sadritdinova AF, et al. Identification, expression analysis, and target prediction of flax Genotroph MicroRNAs under normal and nutrient stress conditions. Front Plant Sci. 2016;7:399.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Senchenko VN, Krasnov GS, Dmitriev AA, Kudryavtseva AV, Anedchenko EA, Braga EA, Pronina IV, Kondratieva TT, Ivanov SV, Zabarovsky ER, et al. Differential expression of CHL1 gene during development of major human cancers. PLoS One. 2011;6(3):e15612.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Dmitriev AA, Kudryavtseva AV, Krasnov GS, Koroban NV, Speranskaya AS, Krinitsina AA, Belenikin MS, Snezhkina AV, Sadritdinova AF, Kishlyan NV, et al. Gene expression profiling of flax (Linum usitatissimum L.) under edaphic stress. BMC plant biology. 2016;16(Suppl 3):237.View ArticlePubMedGoogle Scholar
  25. Straub C, Zhang W, Howe JR. Neto2 modulation of kainate receptors with different subunit compositions. J Neurosci. 2011;31(22):8078–82.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Horak CE, Lee JH, Elkahloun AG, Boissan M, Dumont S, Maga TK, Arnaud-Dabernat S, Palmieri D, Stetler-Stevenson WG, Lacombe ML, et al. Nm23-H1 suppresses tumor cell motility by down-regulating the lysophosphatidic acid receptor EDG2. Cancer Res. 2007;67(15):7238–46.View ArticlePubMedGoogle Scholar
  27. Calicchio ML, Collins T, Kozakewich HP. Identification of signaling systems in proliferating and involuting phase infantile hemangiomas by genome-wide transcriptional profiling. Am J Pathol. 2009;174(5):1638–49.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Ye X, Weinberg RA. Epithelial-Mesenchymal plasticity: a central regulator of cancer progression. Trends Cell Biol. 2015;25(11):675–86.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Iwatsuki M, Mimori K, Yokobori T, Ishi H, Beppu T, Nakamori S, Baba H, Mori M. Epithelial-mesenchymal transition in cancer development and its clinical significance. Cancer Sci. 2010;101(2):293–9.View ArticlePubMedGoogle Scholar
  30. Micalizzi DS, Farabaugh SM, Ford HL. Epithelial-mesenchymal transition in cancer: parallels between normal development and tumor progression. J Mammary Gland Biol Neoplasia. 2010;15(2):117–34.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Wang WJ, Yao Y, Jiang LL, Hu TH, Ma JQ, Ruan ZP, Tian T, Guo H, Wang SH, Nan KJ. Increased LEF1 expression and decreased Notch2 expression are strong predictors of poor outcomes in colorectal cancer patients. Dis Markers. 2013;35(5):395–405.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Ji Q, Liu X, Han Z, Zhou L, Sui H, Yan L, Jiang H, Ren J, Cai J, Li Q. Resveratrol suppresses epithelial-to-mesenchymal transition in colorectal cancer through TGF-beta1/Smads signaling pathway mediated snail/E-cadherin expression. BMC Cancer. 2015;15:97.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Yusup A, Huji B, Fang C, Wang F, Dadihan T, Wang HJ, Upur H. Expression of trefoil factors and TWIST1 in colorectal cancer and their correlation with metastatic potential and prognosis. World J Gastroenterol. 2017;23(1):110–20.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Hebrok M, Wertz K, Fuchtbauer EM. M-twist is an inhibitor of muscle differentiation. Dev Biol. 1994;165(2):537–44.View ArticlePubMedGoogle Scholar
  35. Ansieau S, Bastid J, Doreau A, Morel AP, Bouchet BP, Thomas C, Fauvet F, Puisieux I, Doglioni C, Piccinin S, et al. Induction of EMT by twist proteins as a collateral effect of tumor-promoting inactivation of premature senescence. Cancer Cell. 2008;14(1):79–89.View ArticlePubMedGoogle Scholar
  36. Kroepil F, Fluegen G, Vallbohmer D, Baldus SE, Dizdar L, Raffel AM, Hafner D, Stoecklein NH, Knoefel WT. Snail1 expression in colorectal cancer and its correlation with clinical and pathological parameters. BMC Cancer. 2013;13:145.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Franci C, Gallen M, Alameda F, Baro T, Iglesias M, Virtanen I, Garcia de Herreros A. Snail1 protein in the stroma as a new putative prognosis marker for colon tumours. PLoS One. 2009;4(5):e5595.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Loboda A, Nebozhyn MV, Watters JW, Buser CA, Shaw PM, Huang PS, Van't Veer L, Tollenaar RA, Jackson DB, Agrawal D, et al. EMT is the dominant program in human colon cancer. BMC Med Genet. 2011;4:9.Google Scholar
  39. Kroepil F, Fluegen G, Totikov Z, Baldus SE, Vay C, Schauer M, Topp SA, Esch JS, Knoefel WT, Stoecklein NH. Down-regulation of CDH1 is associated with expression of SNAI1 in colorectal adenomas. PLoS One. 2012;7(9):e46665.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Roy HK, Smyrk TC, Koetsier J, Victor TA, Wali RK. The transcriptional repressor SNAIL is overexpressed in human colon cancer. Dig Dis Sci. 2005;50(1):42–6.View ArticlePubMedGoogle Scholar
  41. Tribulo C, Aybar MJ, Sanchez SS, Mayor R. A balance between the anti-apoptotic activity of slug and the apoptotic activity of msx1 is required for the proper development of the neural crest. Dev Biol. 2004;275(2):325–42.View ArticlePubMedGoogle Scholar
  42. Kajita M, McClinic KN, Wade PA. Aberrant expression of the transcription factors snail and slug alters the response to genotoxic stress. Mol Cell Biol. 2004;24(17):7559–66.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Sun J, Ding W, Zhi J, Chen W. MiR-200 suppresses metastases of colorectal cancer through ZEB1. Tumour Biol. 2015;37(12):15501–7.View ArticleGoogle Scholar
  44. Schmalhofer O, Brabletz S, Brabletz T. E-cadherin, beta-catenin, and ZEB1 in malignant progression of cancer. Cancer Metastasis Rev. 2009;28(1–2):151–66.View ArticlePubMedGoogle Scholar
  45. Guo Y, Lang X, Lu Z, Wang J, Li T, Liao Y, Jia C, Zhao W, Fang H. MiR-10b directly targets ZEB1 and PIK3CA to curb Adenomyotic epithelial cell invasiveness via Upregulation of E-Cadherin and inhibition of Akt Phosphorylation. Cell Physiol Biochem. 2015;35(6):2169–80.View ArticlePubMedGoogle Scholar
  46. Bindels S, Mestdagt M, Vandewalle C, Jacobs N, Volders L, Noel A, van Roy F, Berx G, Foidart JM, Gilles C. Regulation of vimentin by SIP1 in human epithelial breast tumor cells. Oncogene. 2006;25(36):4975–85.View ArticlePubMedGoogle Scholar
  47. Peinado H, Olmeda D, Cano A. Snail, Zeb and bHLH factors in tumour progression: an alliance against the epithelial phenotype? Nat Rev Cancer. 2007;7(6):415–28.View ArticlePubMedGoogle Scholar
  48. Comijn J, Berx G, Vermassen P, Verschueren K, van Grunsven L, Bruyneel E, Mareel M, Huylebroeck D, van Roy F. The two-handed E box binding zinc finger protein SIP1 downregulates E-cadherin and induces invasion. Mol Cell. 2001;7(6):1267–78.View ArticlePubMedGoogle Scholar
  49. Lin AY, Chua MS, Choi YL, Yeh W, Kim YH, Azzi R, Adams GA, Sainani K, van de Rijn M, So SK, et al. Comparative profiling of primary colorectal carcinomas and liver metastases identifies LEF1 as a prognostic biomarker. PLoS One. 2011;6(2):e16636.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Hovanes K, Li TW, Munguia JE, Truong T, Milovanovic T, Lawrence Marsh J, Holcombe RF, Waterman ML. Beta-catenin-sensitive isoforms of lymphoid enhancer factor-1 are selectively expressed in colon cancer. Nat Genet. 2001;28(1):53–7.PubMedGoogle Scholar
  51. Moon RT, Kohn AD, De Ferrari GV, Kaykas A. WNT and beta-catenin signalling: diseases and therapies. Nat Rev Genet. 2004;5(9):691–701.View ArticlePubMedGoogle Scholar
  52. Katoh M, Igarashi M, Fukuda H, Nakagama H, Katoh M. Cancer genetics and genomics of human FOX family genes. Cancer Lett. 2013;328(2):198–206.View ArticlePubMedGoogle Scholar
  53. Hurtado A, Holmes KA, Ross-Innes CS, Schmidt D, Carroll JS. FOXA1 is a key determinant of estrogen receptor function and endocrine response. Nat Genet. 2011;43(1):27–33.View ArticlePubMedGoogle Scholar
  54. Ma W, Jiang J, Li M, Wang H, Zhang H, He X, Huang L, Zhou Q. The clinical significance of forkhead box protein A1 and its role in colorectal cancer. Mol Med Rep. 2016;14(3):2625–31.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Lehner F, Kulik U, Klempnauer J, Borlak J. The hepatocyte nuclear factor 6 (HNF6) and FOXA2 are key regulators in colorectal liver metastases. FASEB J. 2007;21(7):1445–62.View ArticlePubMedGoogle Scholar
  56. Palaghia M, Mihai C, Lozneanu L, Ciobanu D, Trofin AM, Rotariu A, Tarcoveanu F, Cijevschi Prelipcean C. E-cadherin expression in primary colorectal cancer and metastatic lymph nodes. Romanian J Morphol Embryol. 2016;57(1):205–9.Google Scholar
  57. Klampfer L. The role of signal transducers and activators of transcription in colon cancer. Front Biosci. 2008;13:2888–99.View ArticlePubMedGoogle Scholar
  58. Simpson JA, Al-Attar A, Watson NF, Scholefield JH, Ilyas M, Durrant LG. Intratumoral T cell infiltration, MHC class I and STAT1 as biomarkers of good prognosis in colorectal cancer. Gut. 2010;59(7):926–33.View ArticlePubMedGoogle Scholar
  59. Vishnubalaji R, Yue S, Alfayez M, Kassem M, Liu FF, Aldahmash A, Alajez NM. Bone morphogenetic protein 2 (BMP2) induces growth suppression and enhances chemosensitivity of human colon cancer cells. Cancer Cell Int. 2016;16:77.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Vishnubalaji R, Hamam R, Abdulla MH, Mohammed MA, Kassem M, Al-Obeed O, Aldahmash A, Alajez NM. Genome-wide mRNA and miRNA expression profiling reveal multiple regulatory networks in colorectal cancer. Cell Death Dis. 2015;6:e1614.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Fanale D, Barraco N, Listi A, Bazan V, Russo A. Non-coding RNAs functioning in colorectal cancer stem cells. Adv Exp Med Biol. 2016;937:93–108.View ArticlePubMedGoogle Scholar
  62. Lazarova DL, Bordonaro M. Vimentin, colon cancer progression and resistance to butyrate and other HDACis. J Cell Mol Med. 2016;20(6):989–93.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Gilles C, Polette M, Mestdagt M, Nawrocki-Raby B, Ruggeri P, Birembaut P, Foidart JM. Transactivation of vimentin by beta-catenin in human breast cancer cells. Cancer Res. 2003;63(10):2658–64.PubMedGoogle Scholar
  64. Mendez MG, Kojima S, Goldman RD. Vimentin induces changes in cell shape, motility, and adhesion during the epithelial to mesenchymal transition. FASEB J. 2010;24(6):1838–51.View ArticlePubMedPubMed CentralGoogle Scholar
  65. Lahat G, Zhu QS, Huang KL, Wang S, Bolshakov S, Liu J, Torres K, Langley RR, Lazar AJ, Hung MC, et al. Vimentin is a novel anti-cancer therapeutic target; insights from in vitro and in vivo mice xenograft studies. PLoS One. 2010;5(4):e10105.View ArticlePubMedPubMed CentralGoogle Scholar
  66. Boulay JL, Mild G, Lowy A, Reuter J, Lagrange M, Terracciano L, Laffer U, Herrmann R, Rochlitz C. SMAD7 is a prognostic marker in patients with colorectal cancer. Int J Cancer. 2003;104(4):446–9.View ArticlePubMedGoogle Scholar
  67. Boulay JL, Mild G, Lowy A, Reuter J, Lagrange M, Terracciano L, Laffer U, Herrmann R, Rochlitz C. SMAD4 is a predictive marker for 5-fluorouracil-based chemotherapy in patients with colorectal cancer. Br J Cancer. 2002;87(6):630–4.View ArticlePubMedPubMed CentralGoogle Scholar
  68. Kretzschmar M, Massague J. SMADs: mediators and regulators of TGF-beta signaling. Curr Opin Genet Dev. 1998;8(1):103–11.View ArticlePubMedGoogle Scholar
  69. Lee SJ, Yang CS, Kim DD, Kang YN, Kwak SG, Park JB, Cho CH, Park KK. Microenvironmental interactions and expression of molecular markers associated with epithelial-to-mesenchymal transition in colorectal carcinoma. Int J Clin Exp Pathol. 2015;8(11):14270–82.PubMedPubMed CentralGoogle Scholar

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