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Volume 19 Supplement 3

Selected articles from Belyaev Conference 2017: genomics

HK3 overexpression associated with epithelial-mesenchymal transition in colorectal cancer

BMC Genomics volume 19, Article number: 113 (2018) Cite this article

Abstract

Background

Colorectal cancer (CRC) is a common cancer worldwide. The main cause of death in CRC includes tumor progression and metastasis. At molecular level, these processes may be triggered by epithelial-mesenchymal transition (EMT) and necessitates specific alterations in cell metabolism. Although several EMT-related metabolic changes have been described in CRC, the mechanism is still poorly understood.

Results

Using CrossHub software, we analyzed RNA-Seq expression profile data of CRC derived from The Cancer Genome Atlas (TCGA) project. Correlation analysis between the change in the expression of genes involved in glycolysis and EMT was performed. We obtained the set of genes with significant correlation coefficients, which included 21 EMT-related genes and a single glycolytic gene, HK3. The mRNA level of these genes was measured in 78 paired colorectal cancer samples by quantitative polymerase chain reaction (qPCR). Upregulation of HK3 and deregulation of 11 genes (COL1A1, TWIST1, NFATC1, GLIPR2, SFPR1, FLNA, GREM1, SFRP2, ZEB2, SPP1, and RARRES1) involved in EMT were found. The results of correlation study showed that the expression of HK3 demonstrated a strong correlation with 7 of the 21 examined genes (ZEB2, GREM1, TGFB3, TGFB1, SNAI2, TWIST1, and COL1A1) in CRC.

Conclusions

Upregulation of HK3 is associated with EMT in CRC and may be a crucial metabolic adaptation for rapid proliferation, survival, and metastases of CRC cells.

Background

In 2012, an estimated 1.36 million people were diagnosed with colorectal cancer (CRC), including 746,300 men and 614,300 women. The mortality rate of CRC is reported to be approximately half of its global incidence. In the same year, 694,000 people died from CRC [1], making it the third most common cancer and fourth most common cause of death worldwide [2].

The stage of CRC plays a significant role in survival of patients. More than 60% of patients are diagnosed at late stage of CRC (III-IV) and approximately 25% of them display metastatic disease [3, 4]. Development of new drugs and targeted therapies has dramatically improved the survival of these patients over the last decade [5]. However, the survival outcome in patients with metastatic or stage IV CRC still remains poor [6, 7]. For instance, the 5-year relative survival rate for patients with metastatic CRC is about 11%, while that for patients with stage III CRC ranges from 53% to 89% (https://www.cancer.org). CRC is characterized by severe genetic alterations, including microsatellite instability (MSI), epigenetic changes such as DNA methylation (CpG Island Methylator Phenotype [CIMP]), and chromosomal instability (CIN), which are associated with the development of the disease and its aggressive behavior [8, 9]. Thus, identification of the distinct molecular genetic changes related to CRC progression and metastasis is extremely important.

A number of studies have demonstrated that epithelial-mesenchymal transition (EMT) is involved in CRC carcinogenesis and metastasis [10, 11]. EMT is the conversion of epithelial cells into cells with a mesenchymal phenotype, which is accompanied by a decrease in cell-cell cohesion and fundamental reorganization of the cytoskeleton. Consequences include enhanced migratory as well as invasive capacity and resistance to apoptosis [12, 13]. Based on the biological context, three types of EMT have been described as follows: type I observed in embryogenesis [14], type II involved in tissue repair and fibrosis [15], and type III seen in metastatic transformation of cancer cells [16]. A hallmark feature of EMT is the downregulation of E-cadherin, a cell-cell adhesion molecule localized on the surface of epithelial cells [17]. E-cadherin is a well-known tumor suppressor protein and loss of its expression in tumor cells is associated with increased tumor invasiveness and metastasis [18, 19]. In CRC, EMT is characterized by decreased E-cadherin expression and nuclear accumulation of β-catenin, which is an essential protein for correct positioning and function of one [20, 21]. Loss of epithelial and gain of dedifferentiated mesenchyme-like phenotype enable CRC cells to develop invasive and metastatic growth characteristics [22]. The switch from cytoplasmic to nuclear accumulation of β-catenin is a central mechanism related to EMT in CRC [21]. This process may be mediated by the loss-of-function mutations in the adenomatous polyposis coli (APC) tumor suppressor gene or mutations that result in β-catenin stabilization [23]. In the nucleus, β-catenin associates with DNA-binding proteins of the T-cell factor/lymphoid enhancer factor (TCF/Lef) family, leading to a constitutive activation of Wnt/β-catenin signaling target genes, c-Myc [24] and CCND1 [25]. Moreover, β-catenin triggers the expression of other genes such as MMP7 [26, 27], FN [28], CD44 [29], and UPAR [30] involved in invasive cell growth. Except for this mechanism, there are at least ten known signaling pathways and many molecules associated with EMT in CRC [31].

Metabolic reprogramming characterized by upregulated glycolysis is another crucial feature of cancer. It provides cancer cells with energy and metabolites essential for active cell division, large-scale biosynthesis, invasion, and metastasis [32,33,34]. Increased glycolysis has been suggested as an essential component of the malignant phenotype and hallmark of invasive cancers [35]. In CRC, redirection of glucose metabolism is promoted by deregulation of key components involved in glucose transport (GLUT-1) [36], metabolism (hexokinases [HK1, HK2], pyruvate kinase M2 [PKM2], lactate dehydrogenase A [LDHA], aldolase A [ALDOA], etc.) [37,38,39], as well as metabolic regulation [40,41,42]. In addition, variation in the representation of alternative spliced transcripts related to energy metabolism seems to be associated with the prevalence of aerobic glycolysis in cancer cells [43, 44]. EMT in tumors requires the distinct metabolic adaptations for survival, rapid proliferation, and metastasis. However, metabolic changes observed upon EMT induction are uncommon for various cancer types [45,46,47]. Recent studies showed that the glycolytic enzymes aldolase B (ALDOB), PKM2, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) play a role in EMT and metastasis in CRC [48, 49].

In this work, using CrossHub software we analyzed RNA-Seq data from The Cancer Genome Atlas (TCGA) project to estimate the association between the expression of genes participating in EMT and glycolysis in CRC. Hexokinase 3 (HK3) was found to exhibit significant correlation with a set of genes involved in EMT. Thus, we focused on the investigation of HK3 gene expression in CRC samples and its association with EMT.

Methods

Bioinformatic analysis

Using CrossHub software [50], we have analyzed The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov) project RNA-Seq dataset derived from CRC. The dataset contained 287 tumor samples including 26 paired ones. The selection of genes participating in glycolysis and EMT was performed using Gene Ontology database and “glycolytic process” and “epithelial to mesenchymal transition” as keywords. A set of 132 genes was selected for further analysis.

The correlation analysis between the expressions of target genes was performed using Spearman’s rank correlation coefficients (r s ). We carried out two tests: one for paired samples and other for pool of tumor samples. Spearman’s rank correlation coefficients were calculated between (1) fold change values, the ratio of CPM (counts per million) in tumor sample to CPM in conditional normal tissue, (paired samples) and (2) CPM values (pool of tumor samples). Before calculating correlation coefficients, dropping points (no more than 5%) were eliminated using approximate generalized linear model. We focused on the genes with concordant results in paired and pooled tests.

Tissue samples

In total, 78 paired CRC samples, including tumor and adjacent morphologically normal tissues (conditional normal tissue), were collected from patients in Herzen Moscow Cancer Research Institute - branch of National Medical Research Radiological Center, Ministry of Health of Russian Federation (Moscow, Russia). The sample information is presented in Table 1. Samples were obtained after surgical resection prior to radiation or chemotherapy and were stored in liquid nitrogen. The diagnosis was verified by histopathology, and only samples with 70% or more tumor cells were used in the study. The morphological classification of the tumor was performed according to the WHO Classification of Tumours of the Digestive System (WHO/IARC Classification of Tumours, 4th Edition, 2010) and verified according to the American Joint Committee on Cancer (AJCC) staging system (AJCC Cancer Staging Manual, 8th Edition, 2017). Samples were collected in accordance with the guidelines issued by The Ethics committee of Herzen Moscow Cancer Research Institute. Written informed consent was obtained from all patients.

Table 1 Clinicopathologic characteristics of the tumors
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RNA isolation and reverse transcription

Nitrogen-frozen tissues were homogenized using a Mikro-Dismembrator S (Sartorius, Germany). Total RNA was isolated using the RNeasy Mini Kit (Qiagen, Germany) according to manufacturer’s instructions. Purified RNA was quantified using NanoDrop 1000 (NanoDrop Technologies Inc., USA) and the RNA quality measured with the RNA Integrity Number (RIN) method on Agilent RNA Bioanalyzer 2100 (Agilent Technologies, USA). All RNA samples were treated with RNase-free DNase I (Thermo Fisher Scientific, USA). Reverse transcription was performed from 1 μg of total RNA using M-MuLV reverse transcriptase (Thermo Fisher Scientific) and random hexamer primers (Evrogen, Russia). Negative control samples were included in each set of reactions. Reactions were heated at 70 °C for 5 min to melt the secondary structure of mRNA, followed by immediate cooling on ice. Samples were further incubated at 25 °C for 5 min, followed by treatment with M-MuLV reverse transcriptase at 25 °C for 10 min. Then the reaction tubes were incubated at 42 °C for 1 h. Following the incubation, samples were diluted to 48 μL in nuclease-free water and stored at − 20 °C.

Quantitative polymerase chain reaction (qPCR)

Quantitative polymerase chain reaction was performed using commercial primer-probe sets for target genes (HK3: Hs01092850_m1, VASN: Hs01936449_s1, GREM1: Hs01879841_s1, TGFB3: Hs01086000_m1, TGFB1: Hs00998133_m1, LOXL3: Hs01046941_g1, HGF: Hs00300159_m1, SNAI2: Hs00161904_m1, FAM101B: Hs00823804_m1, SFRP2: Hs00293258_m1, FLNA: Hs00924645_m1, WWTR1: Hs00210007_m1, SFRP1: Hs00610060_m1, GLIPR2: Hs01555479_m1, NFATC1: Hs00542675_m1, TWIST1: Hs01675818_s1, COL1A1: Hs00164004_m1, ZEB2: Hs00207691_m1, VIM: Hs00958111_m1, TP53: Hs01034249_m1, SPP1: Hs00959010_m1, RARRES1: Hs00894859_m1) from TaqMan Gene Expression Assays (Thermo Fisher Scientific). Primers and probes for reference genes, GUSB and RPN1, were designed earlier [51]. The reactions were carried out on AB 7500 Real-Time PCR System (Thermo Fisher Scientific) as previously described [52]. Each reaction was repeated three times.

QPCR data were analyzed using relative quantification or ΔΔCt-method as previously described [44, 52]. Relative mRNA level of the genes was calculated using ATG program compatible with relative quantification software (Thermo Fisher Scientific) [52]. Given the variability of mRNA level of the reference gene, an mRNA level change of at least two-fold was considered as significant.

Statistical analysis

Statistical analysis was performed using SPSS 10 software (SPSS Inc., USA). The Wilcoxon/Mann-Whitney and Kruskal-Wallis tests were applied to analyze differences in mRNA expression of target genes in CRC samples. Spearman’s rank correlation coefficient (r s ) was used for revealing correlations. A value of p ≤ 0.05 was considered statistically significant.

Results

Preliminary correlation analysis of gene expression based on TCGA project data

We identified a number of genes that showed high correlations between each other in CRC with the false discovery rate (FDR) ≤ 0.05. In the heat maps, these genes formed a significant correlation module (Additional file 1: Table S1 and Additional file 2: Table S2). Only one glycolytic gene, HK3, was presented in this module. A significantly strong correlation was observed between HK3 gene and 16 EMT-related genes (VASN, GREM1, TGFB3, TGFB1, LOXL3, HGF, SNAI2, FAM101B, SFRP2, FLNA, WWTR1, SFRP1, GLIPR2, NFATC1, TWIST1, and COL1A1), both in paired as well as pool of tumor samples. In addition, literature data analysis revealed several genes that are involved in EMT process. Preliminary calculation of correlation coefficients between the expression of these genes and HK3 was carried out using CrossHub software and RNA-Seq data from TCGA project as described above. The results obtained are shown in Additional file 3: Table S3 (available only for 26 paired CRC samples). A significant correlation was reported between the change in expression of HK3 gene and five additional genes (ZEB2, VIM, TP53, SPP1, and RARRES1) involved in EMT. Thus, HK3 and a set of 21 EMT-related genes were selected for further assessment by qPCR.

Upregulation of HK3 gene expression in CRC

Using qPCR, we quantified the expression of HK3 gene in 78 paired CRC samples. HK3 mRNA level was upregulated from 2 to 12-fold in 41% (32/78, p < 0.01) CRC samples as compared with that in conditional normal tissue (Fig. 1). Over two-fold decrease in the expression of HK3 gene was determined in 14% (11/78) of cases. The mean value of relative mRNA level was 1.7. HK3 overexpression was not correlated with CRC progression stage.

Fig. 1
figure 1

Relative mRNA level of HK3 gene in colorectal cancer (qPCR data). The mRNA level of HK3 was normalized by those of two reference genes, GUSB and RPN1. Data were presented according to CRC progression stage

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In addition, we verified the expression of all selected EMT-related genes using qPCR. Deregulation in the expression of 11 genes (COL1A1, TWIST1, NFATC1, GLIPR2, SFPR1, FLNA, GREM1, SFRP2, ZEB2, SPP1, and RARRES1) was detected in CRC (Table 2). A significant increase (p < 0.05) in the mRNA level of COL1A1, TWIST1, CREM1, and SPP1 genes was observed in most examined samples. Moreover, NFATC1, GLIPR2, SFPR1, FLNA, SFRP2, ZEB2, and RARRES1 genes were downregulated by over two-fold (p < 0.05) in more than 40% of CRC cases.

Table 2 Frequency and mRNA level changes of eleven EMT-related genes in CRC
Full size table

Correlation study between HK3 and EMT-related genes in CRC

In order to determine the correlation between HK3 and EMT-related genes, Spearman’s rank correlation coefficients were calculated. The results observed are shown in Table 3. We found a significantly strong correlation (> 0.5 or < − 0.5, p < 0.05) between HK3 mRNA level and expression of seven of 21 examined genes (ZEB2, GREM1, TGFB3, TGFB1, SNAI2, TWIST1, and COL1A1) in CRC. Generally, HK3 gene expression was positively correlated with almost all tested genes at moderate or weak levels. A negative correlation was detected between HK3 and TP53 genes only.

Table 3 Results of Spearman’s correlation analysis between the mRNA levels of HK3 and EMT-related genes
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Discussion

In the 1920s, Otto Warburg and co-workers observed the altered energy metabolism in proliferating tumor cells. These cells were shown to exhibit increased glycolysis with lactate secretion even in the presence of oxygen — a phenomenon known as the “Warburg effect” [53]. Increased glucose metabolism, now considered as a hallmark of cancer, is thought to be essential for tumor cell growth, survival, proliferation, and long-term maintenance [54]. The first step of glycolysis is catalyzed by hexokinases (HKs). There are four HK isoenzymes encoded by separate genes, HK1, HK2, HK3, and HK4 (or GCK) [55]. HKs phosphorylate glucose to glucose 6-phosphate using ATP molecule as the phosphoryl donor [56]. HKs are the first rate-limiting enzymes of glycolysis. Alterations in their expression promote changes in glucose flux. Overexpression of HK1 and HK2 genes was reported in many tumors including colorectal, prostate, breast, lung, gastrointestinal, and pancreatic cancers [39, 57,58,59,60,61]. In particular, HK2 expression was suggested to be responsible for accelerated glycolysis in cancer cells [62]. Nevertheless, activities of both HK1 and HK2 are essential for their survival [63]. Association of HK4 expression with tumorigenesis is still unclear. However, polymorphisms in HK4 and HK2 affect the risk and clinical outcome in pancreatic cancer [64, 65]. A recent study reported functional role of HK3 in acute promyelocytic leukemia [66, 67]. No other studies have described the role of HK3 in human cancers. This is the first study to report HK3 upregulation in CRC and its potential association with EMT.

The loss of E-cadherin expression during EMT is a major event in cancer. Downregulation of E-cadherin is mediated by a series of transcription repressors, including proteins of the SNAIL superfamily (SNAI1, SNAI2, and SNAI3) and ZEB family (ZEB1 and ZEB2), forkhead-box protein FOXC2, factors E47 and KLF8, and TWIST bHLH proteins (TWIST1 and TWIST2) [68]. Upregulation of major EMT transcription factors such as TWIST1, ZEB1, ZEB2, SNAI1, and SNAI2 has been observed in many tumors [69,70,71,72,73,74,75,76,77]. We showed an increased expression of TWIST1 gene in CRC, as reported in a previous study [78]. Furthermore, overexpression of TWIST1 was demonstrated to promote migration and invasion of CRC cells and induce EMT. Although no significant upregulation in SNAI2 mRNA level was observed in this work, SNAI2 protein expression was earlier shown to be upregulated in colorectal tumors [77]. Shioiri and co-authors demonstrated the localization of overexpressed SNAI2 protein in the cytoplasm, wherein its gene-repressive function is absent [79]. We also reported downregulation of ZEB2 gene in more than half of CRC samples, while its overexpression was observed at the invasion front of CRC in another study [80]. However, ZEB2 protein promotes EMT progression through direct suppression of the transcription of genes involved in epithelial dedifferentiation [81, 82]. Thus, ZEB2 overexpression may not be generally observed after EMT and may not always correlate with the mesenchymal phenotype [83]. In addition, we found significant positive correlations between the expression of HK3 and TWIST1, SNAI2, and ZEB2 genes in CRC. The association of HK3 with important EMT inducers may indicate its involvement in EMT in colorectal cancer.

The genes TGFB1 and TGFB3 encode signaling proteins, transforming growth factor-beta 1 and 3 (TGFβ1 and TGFβ3). These are secreted ligands of specific TGFβ membrane receptors that play an essential role during differentiation, proliferation, and embryonic development in normal tissues. In general, the binding of the ligand to its receptor initiates phosphorylation of proteins belonging to the SMAD family. Activated SMAD proteins assemble into complexes with transcription factors to directly regulate gene expression [84]. Many studies have demonstrated the involvement of TGFβs in EMT during normal development and pathological processes [85,86,87,88]. Alterations in the components of TGFβ pathway are often observed during CRC progression. Approximately 40–50% of all CRC cases display mutational inactivation of this signaling pathway [89, 90]. Although TGFβ pathway exhibits a tumor-suppressive role, increased level of TGFβ1 in plasma of patients with CRC was associated with the development of metastasis [91]. Moreover, elevated TGFβ1 level has been reported to increase the metastasis capability of CRC cells and inhibition of TGFβ receptor 1 resulted in metastasis formation in animal model [92]. No significant change in the expression of TGFB1 and TGFB3 genes in CRC was observed in our study. However, these genes demonstrated a strong positive correlation with HK3 mRNA level.

We observed a significant upregulation in GREM1 and COL1A1 gene expression in CRC. A positive correlation was observed between these genes and HK3. GREM1 gene encodes a member of the BMP antagonist family and is known as a mediator of EMT in non-cancerous pathologies and cancer [93,94,95,96]. GREM1 was shown to be involved in the migration of CRC cells in vitro and in silico [97]. However, a few studies have demonstrated an association between GREM1 expression and overall survival of CRC patients [98, 99]. COL1A1 gene encodes for alpha 1 type I collagen, a target protein for TGFβ1 transcription factor. Type I collagen is associated with EMT in normal and cancer tissues [100,101,102]. It may downregulate E-cadherin and β-catenin and promote EMT-like phenotype in CRC cells [103]. In the study by Zou and co-workers, overexpression of COL1A1 gene was observed in tissues and serum samples of patients with CRC. The serum level of COL1A1 correlated with the staging and poor survival rate of CRC [104].

It should to be noted that TP53 gene encodes a tumor suppressor protein, p53. One of the most important functions of p53 is the activation of apoptosis. TP53 is frequently mutated in human cancers, and over 50% of all tumors display somatic mutations in TP53 gene [105]. Loss of p53 activity is also associated with EMT phenotype. It has been shown that downregulation of p53 promotes proliferation, EMT-mediated migration, and invasion of CRC cells [106]. In addition, p53 was recently found to be involved in cancer cell metabolism [107, 108]. p53 displays an ability to suppress glycolysis and stimulate oxidative phosphorylation through transcriptional regulation of several glycolysis-related genes [109,110,111]. We observed a significant negative correlation between mRNA levels of HK3 and TP53 genes. Thus, HK3 gene could be a potential p53 target and suppression of p53 may not only contribute to increased glycolysis but also provide some advantages for EMT in CRC cells. It is interesting that the upregulation of HK3 expression was observed in breast cancer samples with TP53 mutations, as per TCGA data [112]. In this study, CRC samples were not tested for TP53 mutations, but could also be positive for ones.

Conclusions

We showed the upregulation of HK3 gene in CRC and confirmed its involvement in tumorigenesis. Expression of HK3 gene significantly correlated with mRNA levels of important EMT transcriptional factors (ZEB2, TGFB3, TGFB1, SNAI2, and TWIST1) and components (GREM1 and COL1A1). Thus, our study suggests that HK3 may participate in EMT process and that its upregulation could be one of the crucial changes for adaptation of glucose metabolism to EMT in CRC.

References

  1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87–108.

    Article  PubMed  Google Scholar 

  2. Haggar FA, Boushey RP. Colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clinics in colon and rectal surgery. 2009;22(4):191–7.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Field K, Lipton L. Metastatic colorectal cancer-past, progress and future. World J Gastroenterol. 2007;13(28):3806–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Van Cutsem E, Oliveira J, Group EGW. Advanced colorectal cancer: ESMO clinical recommendations for diagnosis, treatment and follow-up. Annals of oncology: official journal of the European Society for Med Oncol. 2009;20(Suppl 4):61–3.

    Google Scholar 

  5. De Divitiis C, Nasti G, Montano M, Fisichella R, Iaffaioli RV, Berretta M. Prognostic and predictive response factors in colorectal cancer patients: between hope and reality. World J Gastroenterol. 2014;20(41):15049–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Goldberg RM, Rothenberg ML, Van Cutsem E, Benson AB, 3rd, Blanke CD, Diasio RB, Grothey A, Lenz HJ, Meropol NJ, Ramanathan RK et al: The continuum of care: a paradigm for the management of metastatic colorectal cancer. Oncologist 2007, 12(1):38–50.

  7. Zacharakis M, Xynos ID, Lazaris A, Smaro T, Kosmas C, Dokou A, Felekouras E, Antoniou E, Polyzos A, Sarantonis J et al: Predictors of survival in stage IV metastatic colorectal cancer. Anticancer Res 2010, 30(2):653–660.

  8. Armaghany T, Wilson JD, Chu Q, Mills G. Genetic alterations in colorectal cancer. Gastrointestinal cancer research : GCR. 2012;5(1):19–27.

    PubMed  PubMed Central  Google Scholar 

  9. Kudryavtseva AV, Lipatova AV, Zaretsky AR, Moskalev AA, Fedorova MS, Rasskazova AS, Shibukhova GA, Snezhkina AV, Kaprin AD, Alekseev BY, et al. Important molecular genetic markers of colorectal cancer. Oncotarget. 2016;7(33):53959–83.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gurzu S, Turdean S, Kovecsi A, Contac AO, Jung I. Epithelial-mesenchymal, mesenchymal-epithelial, and endothelial-mesenchymal transitions in malignant tumors: an update. World journal of clinical cases. 2015;3(5):393–404.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Kraus S, Vay C, Baldus S, Knoefel WT, Stoecklein NH, Vallbohmer D. Expression of wingless-type mouse mammary tumor virus integration site family pathway effectors in lymphatic and hepatic metastases of patients with colorectal cancer: associations with the primary tumor. Oncol Lett. 2015;10(2):863–8.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Guarino M, Rubino B, Ballabio G. The role of epithelial-mesenchymal transition in cancer pathology. Pathology. 2007;39(3):305–18.

    Article  CAS  PubMed  Google Scholar 

  13. Kalluri R, Neilson EG. Epithelial-mesenchymal transition and its implications for fibrosis. J Clin Invest. 2003;112(12):1776–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hay ED. An overview of epithelio-mesenchymal transformation. Acta Anat. 1995;154(1):8–20.

    Article  CAS  PubMed  Google Scholar 

  15. Stone RC, Pastar I, Ojeh N, Chen V, Liu S, Garzon KI, Tomic-Canic M. Epithelial-mesenchymal transition in tissue repair and fibrosis. Cell Tissue Res. 2016;365(3):495–506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Thiery JP. Epithelial-mesenchymal transitions in tumour progression. Nat Rev Cancer. 2002;2(6):442–54.

    Article  CAS  PubMed  Google Scholar 

  17. Gumbiner BM. Cell adhesion: the molecular basis of tissue architecture and morphogenesis. Cell. 1996;84(3):345–57.

    Article  CAS  PubMed  Google Scholar 

  18. Vleminckx K, Vakaet L Jr, Mareel M, Fiers W, van Roy F. Genetic manipulation of E-cadherin expression by epithelial tumor cells reveals an invasion suppressor role. Cell. 1991;66(1):107–19.

    Article  CAS  PubMed  Google Scholar 

  19. Pecina-Slaus N. Tumor suppressor gene E-cadherin and its role in normal and malignant cells. Cancer Cell Int. 2003;3(1):17.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Barth AI, Nathke IS, Nelson WJ. Cadherins, catenins and APC protein: interplay between cytoskeletal complexes and signaling pathways. Curr Opin Cell Biol. 1997;9(5):683–90.

    Article  CAS  PubMed  Google Scholar 

  21. Brabletz T, Jung A, Reu S, Porzner M, Hlubek F, Kunz-Schughart LA, Knuechel R, Kirchner T. Variable beta-catenin expression in colorectal cancers indicates tumor progression driven by the tumor environment. Proc Natl Acad Sci U S A. 2001;98(18):10356–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kirchner T, Brabletz T. Patterning and nuclear beta-catenin expression in the colonic adenoma-carcinoma sequence. Analogies with embryonic gastrulation. Am J Pathol. 2000;157(4):1113–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Korinek V, Barker N, Morin PJ, van Wichen D, de Weger R, Kinzler KW, Vogelstein B, Clevers H. Constitutive transcriptional activation by a beta-catenin-Tcf complex in APC−/− colon carcinoma. Science. 1997;275(5307):1784–7.

    Article  CAS  PubMed  Google Scholar 

  24. He TC, Sparks AB, Rago C, Hermeking H, Zawel L, da Costa LT, Morin PJ, Vogelstein B, Kinzler KW. Identification of c-MYC as a target of the APC pathway. Science. 1998;281(5382):1509–12.

    Article  CAS  PubMed  Google Scholar 

  25. Tetsu O, McCormick F: Beta-catenin regulates expression of cyclin D1 in colon carcinoma cells. Nature 1999, 398(6726):422–426.

  26. Brabletz T, Jung A, Dag S, Hlubek F, Kirchner T. Beta-catenin regulates the expression of the matrix metalloproteinase-7 in human colorectal cancer. Am J Pathol. 1999;155(4):1033–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Crawford HC, Fingleton BM, Rudolph-Owen LA, Goss KJ, Rubinfeld B, Polakis P, Matrisian LM. The metalloproteinase matrilysin is a target of beta-catenin transactivation in intestinal tumors. Oncogene. 1999;18(18):2883–91.

    Article  CAS  PubMed  Google Scholar 

  28. Gradl D, Kuhl M, Wedlich D. The Wnt/Wg signal transducer beta-catenin controls fibronectin expression. Mol Cell Biol. 1999;19(8):5576–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wielenga VJ, Smits R, Korinek V, Smit L, Kielman M, Fodde R, Clevers H, Pals ST. Expression of CD44 in Apc and Tcf mutant mice implies regulation by the WNT pathway. Am J Pathol. 1999;154(2):515–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mann B, Gelos M, Siedow A, Hanski ML, Gratchev A, Ilyas M, Bodmer WF, Moyer MP, Riecken EO, Buhr HJ, et al. Target genes of beta-catenin-T cell-factor/lymphoid-enhancer-factor signaling in human colorectal carcinomas. Proc Natl Acad Sci U S A. 1999;96(4):1603–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gurzu S, Silveanu C, Fetyko A, Butiurca V, Kovacs Z, Jung I. Systematic review of the old and new concepts in the epithelial-mesenchymal transition of colorectal cancer. World J Gastroenterol. 2016;22(30):6764–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bonuccelli G, Tsirigos A, Whitaker-Menezes D, Pavlides S, Pestell RG, Chiavarina B, Frank PG, Flomenberg N, Howell A, Martinez-Outschoorn UE, et al. Ketones and lactate "fuel" tumor growth and metastasis: evidence that epithelial cancer cells use oxidative mitochondrial metabolism. Cell Cycle. 2010;9(17):3506–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Deberardinis RJ, Sayed N, Ditsworth D, Thompson CB. Brick by brick: metabolism and tumor cell growth. Curr Opin Genet Dev. 2008;18(1):54–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Phan LM, Yeung SC, Lee MH: Cancer metabolic reprogramming: importance, main features, and potentials for precise targeted anti-cancer therapies. Cancer biology & medicine 2014, 11(1):1–19.

  35. Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis? Nat Rev Cancer. 2004;4(11):891–9.

    Article  CAS  PubMed  Google Scholar 

  36. Martins SF, Amorim R, Viana-Pereira M, Pinheiro C, Costa RF, Silva P, Couto C, Alves S, Fernandes S, Vilaca S, et al. Significance of glycolytic metabolism-related protein expression in colorectal cancer, lymph node and hepatic metastasis. BMC Cancer. 2016;16:535.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Graziano F, Ruzzo A, Giacomini E, Ricciardi T, Aprile G, Loupakis F, Lorenzini P, Ongaro E, Zoratto F, Catalano V, et al. Glycolysis gene expression analysis and selective metabolic advantage in the clinical progression of colorectal cancer. The pharmacogenomics journal. 2017;17(3):258–64.

    Article  CAS  PubMed  Google Scholar 

  38. Kawai K, Uemura M, Munakata K, Takahashi H, Haraguchi N, Nishimura J, Hata T, Matsuda C, Ikenaga M, Murata K, et al. Fructose-bisphosphate aldolase a is a key regulator of hypoxic adaptation in colorectal cancer cells and involved in treatment resistance and poor prognosis. Int J Oncol. 2017;50(2):525–34.

    Article  PubMed  CAS  Google Scholar 

  39. 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.

    CAS  PubMed  Google Scholar 

  40. 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.

    Article  CAS  Google Scholar 

  41. Xiang S, Fang J, Wang S, Deng B, Zhu L. MicroRNA135b regulates the stability of PTEN and promotes glycolysis by targeting USP13 in human colorectal cancers. Oncol Rep. 2015;33(3):1342–8.

    Article  CAS  PubMed  Google Scholar 

  42. Mansour RN, Enderami SE, Ardeshirylajimi A, Fooladsaz K, Fathi M, Ganji SM. Evaluation of hypoxia inducible factor-1 alpha gene expression in colorectal cancer stages of Iranian patients. J Cancer Res Ther. 2016;12(4):1313–7.

    Article  PubMed  Google Scholar 

  43. Talekar M, Boreddy SR, Singh A, Amiji M. Tumor aerobic glycolysis: new insights into therapeutic strategies with targeted delivery. Expert Opin Biol Ther. 2014;14(8):1145–59.

    Article  CAS  PubMed  Google Scholar 

  44. 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 Biol. 2016;16(Suppl 3):237.

    Article  PubMed  CAS  Google Scholar 

  45. Aspuria PJ, Lunt SY, Varemo L, Vergnes L, Gozo M, Beach JA, Salumbides B, Reue K, Wiedemeyer WR, Nielsen J, et al. Succinate dehydrogenase inhibition leads to epithelial-mesenchymal transition and reprogrammed carbon metabolism. Cancer & metabolism. 2014;2:21.

    Article  Google Scholar 

  46. Masin M, Vazquez J, Rossi S, Groeneveld S, Samson N, Schwalie PC, Deplancke B, Frawley LE, Gouttenoire J, Moradpour D, et al. GLUT3 is induced during epithelial-mesenchymal transition and promotes tumor cell proliferation in non-small cell lung cancer. Cancer & metabolism. 2014;2:11.

    Article  Google Scholar 

  47. Dong C, Yuan T, Wu Y, Wang Y, Fan TW, Miriyala S, Lin Y, Yao J, Shi J, Kang T, et al. Loss of FBP1 by Snail-mediated repression provides metabolic advantages in basal-like breast cancer. Cancer Cell. 2013;23(3):316–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Liu K, Tang Z, Huang A, Chen P, Liu P, Yang J, Lu W, Liao J, Sun Y, Wen S, et al. Glyceraldehyde-3-phosphate dehydrogenase promotes cancer growth and metastasis through upregulation of SNAIL expression. Int J Oncol. 2017;50(1):252–62.

    Article  PubMed  Google Scholar 

  49. Hamabe A, Konno M, Tanuma N, Shima H, Tsunekuni K, Kawamoto K, Nishida N, Koseki J, Mimori K, Gotoh N, et al. Role of pyruvate kinase M2 in transcriptional regulation leading to epithelial-mesenchymal transition. Proc Natl Acad Sci U S A. 2014;111(43):15526–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 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.

    Article  Google Scholar 

  51. Krasnov GS, Oparina NY, 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.

    Article  CAS  Google Scholar 

  52. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Koppenol WH, Bounds PL, Dang CV. Otto Warburg's contributions to current concepts of cancer metabolism. Nat Rev Cancer. 2011;11(5):325–37.

    Article  CAS  PubMed  Google Scholar 

  54. 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.

    Article  CAS  Google Scholar 

  55. Wilson JE. Isozymes of mammalian hexokinase: structure, subcellular localization and metabolic function. J Exp Biol. 2003;206(Pt 12):2049–57.

    Article  CAS  PubMed  Google Scholar 

  56. Alfarouk KO, Verduzco D, Rauch C, Muddathir AK, Adil HH, Elhassan GO, Ibrahim ME, David Polo Orozco J, Cardone RA, Reshkin SJ, et al. Glycolysis, tumor metabolism, cancer growth and dissemination. A new pH-based etiopathogenic perspective and therapeutic approach to an old cancer question. Oncoscience. 2014;1(12):777–802.

    Article  PubMed  PubMed Central  Google Scholar 

  57. He X, Lin X, Cai M, Zheng X, Lian L, Fan D, Wu X, Lan P, Wang J. Overexpression of hexokinase 1 as a poor prognosticator in human colorectal cancer. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine. 2016;37(3):3887–95.

    Article  CAS  Google Scholar 

  58. Wang L, Xiong H, Wu F, Zhang Y, Wang J, Zhao L, Guo X, Chang LJ, Zhang Y, You MJ, et al. Hexokinase 2-mediated Warburg effect is required for PTEN- and p53-deficiency-driven prostate cancer growth. Cell Rep. 2014;8(5):1461–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Palmieri D, Fitzgerald D, Shreeve SM, Hua E, Bronder JL, Weil RJ, Davis S, Stark AM, Merino MJ, Kurek R, et al. Analyses of resected human brain metastases of breast cancer reveal the association between up-regulation of hexokinase 2 and poor prognosis. Molecular cancer research: MCR. 2009;7(9):1438–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Smith TA. Mammalian hexokinases and their abnormal expression in cancer. Br J Biomed Sci. 2000;57(2):170–8.

    CAS  PubMed  Google Scholar 

  61. 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.

    Article  CAS  PubMed  Google Scholar 

  62. 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.

    Article  CAS  PubMed  Google Scholar 

  63. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Dong X, Tang H, Hess KR, Abbruzzese JL, Li D. Glucose metabolism gene polymorphisms and clinical outcome in pancreatic cancer. Cancer. 2011;117(3):480–91.

    Article  CAS  PubMed  Google Scholar 

  65. Dong X, Li Y, Chang P, Tang H, Hess KR, Abbruzzese JL, Li D. Glucose metabolism gene variants modulate the risk of pancreatic cancer. Cancer Prev Res. 2011;4(5):758–66.

    Article  CAS  Google Scholar 

  66. Federzoni EA, Valk PJ, Torbett BE, Haferlach T, Lowenberg B, Fey MF, Tschan MP. PU.1 is linking the glycolytic enzyme HK3 in neutrophil differentiation and survival of APL cells. Blood. 2012;119(21):4963–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Federzoni EA, Humbert M, Torbett BE, Behre G, Fey MF, Tschan MP. CEBPA-dependent HK3 and KLF5 expression in primary AML and during AML differentiation. Sci Rep. 2014;4:4261.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Puisieux A, Brabletz T, Caramel J. Oncogenic roles of EMT-inducing transcription factors. Nat Cell Biol. 2014;16(6):488–94.

    Article  CAS  PubMed  Google Scholar 

  69. Geradts J, de Herreros AG, Su Z, Burchette J, Broadwater G, Bachelder RE. Nuclear Snail1 and nuclear ZEB1 protein expression in invasive and intraductal human breast carcinomas. Hum Pathol. 2011;42(8):1125–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Spoelstra NS, Manning NG, Higashi Y, Darling D, Singh M, Shroyer KR, Broaddus RR, Horwitz KB, Richer JK. The transcription factor ZEB1 is aberrantly expressed in aggressive uterine cancers. Cancer Res. 2006;66(7):3893–902.

    Article  CAS  PubMed  Google Scholar 

  71. Fang Y, Wei J, Cao J, Zhao H, Liao B, Qiu S, Wang D, Luo J, Chen W. Protein expression of ZEB2 in renal cell carcinoma and its prognostic significance in patient survival. PLoS One. 2013;8(5):e62558.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Galvan JA, Zlobec I, Wartenberg M, Lugli A, Gloor B, Perren A, Karamitopoulou E. Expression of E-cadherin repressors SNAIL, ZEB1 and ZEB2 by tumour and stromal cells influences tumour-budding phenotype and suggests heterogeneity of stromal cells in pancreatic cancer. Br J Cancer. 2015;112(12):1944–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Mironchik Y, Winnard PT, Jr., Vesuna F, Kato Y, Wildes F, Pathak AP, Kominsky S, Artemov D, Bhujwalla Z, Van Diest P et al: Twist overexpression induces in vivo angiogenesis and correlates with chromosomal instability in breast cancer. Cancer Res 2005, 65(23):10801–10809.

  74. Bhardwaj M, Sen S, Sharma A, Kashyap S, Chosdol K, Pushker N, Bajaj MS, Bakhshi S. ZEB2/SIP1 as novel prognostic indicator in eyelid sebaceous gland carcinoma. Hum Pathol. 2015;46(10):1437–42.

    Article  CAS  PubMed  Google Scholar 

  75. Montserrat N, Mozos A, Llobet D, Dolcet X, Pons C, de Herreros AG, Matias-Guiu X, Prat J. Epithelial to mesenchymal transition in early stage endometrioid endometrial carcinoma. Hum Pathol. 2012;43(5):632–43.

    Article  PubMed  Google Scholar 

  76. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Larriba MJ, Martin-Villar E, Garcia JM, Pereira F, Pena C, de Herreros AG, Bonilla F, Munoz A. Snail2 cooperates with Snail1 in the repression of vitamin D receptor in colon cancer. Carcinogenesis. 2009;30(8):1459–68.

    Article  CAS  PubMed  Google Scholar 

  78. Deng JJ, Zhang W, Xu XM, Zhang F, Tao WP, Ye JJ, Ge W. Twist mediates an aggressive phenotype in human colorectal cancer cells. Int J Oncol. 2016;48(3):1117–24.

    Article  CAS  PubMed  Google Scholar 

  79. Shioiri M, Shida T, Koda K, Oda K, Seike K, Nishimura M, Takano S, Miyazaki M. Slug expression is an independent prognostic parameter for poor survival in colorectal carcinoma patients. Br J Cancer. 2006;94(12):1816–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Kahlert C, Lahes S, Radhakrishnan P, Dutta S, Mogler C, Herpel E, Brand K, Steinert G, Schneider M, Mollenhauer M, et al. Overexpression of ZEB2 at the invasion front of colorectal cancer is an independent prognostic marker and regulates tumor invasion in vitro. Clinical cancer research : an official journal of the American Association for Cancer Research. 2011;17(24):7654–63.

    Article  CAS  Google Scholar 

  81. Vandewalle C, Comijn J, De Craene B, Vermassen P, Bruyneel E, Andersen H, Tulchinsky E, Van Roy F, Berx G. SIP1/ZEB2 induces EMT by repressing genes of different epithelial cell-cell junctions. Nucleic Acids Res. 2005;33(20):6566–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 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.

    Article  CAS  PubMed  Google Scholar 

  83. Beltran M, Puig I, Pena C, Garcia JM, Alvarez AB, Pena R, Bonilla F, de Herreros AG. A natural antisense transcript regulates Zeb2/Sip1 gene expression during Snail1-induced epithelial-mesenchymal transition. Genes Dev. 2008;22(6):756–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Massague J. How cells read TGF-beta signals. Nat Rev Mol Cell Biol. 2000;1(3):169–78.

    Article  CAS  PubMed  Google Scholar 

  85. Xu J, Lamouille S, Derynck R. TGF-beta-induced epithelial to mesenchymal transition. Cell Res. 2009;19(2):156–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Ignotz RA, Massague J. Cell adhesion protein receptors as targets for transforming growth factor-beta action. Cell. 1987;51(2):189–97.

    Article  CAS  PubMed  Google Scholar 

  87. Raghow R, Postlethwaite AE, Keski-Oja J, Moses HL, Kang AH. Transforming growth factor-beta increases steady state levels of type I procollagen and fibronectin messenger RNAs posttranscriptionally in cultured human dermal fibroblasts. J Clin Invest. 1987;79(4):1285–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Gorsch SM, Memoli VA, Stukel TA, Gold LI, Arrick BA. Immunohistochemical staining for transforming growth factor beta 1 associates with disease progression in human breast cancer. Cancer Res. 1992;52(24):6949–52.

    CAS  PubMed  Google Scholar 

  89. Markowitz S, Wang J, Myeroff L, Parsons R, Sun L, Lutterbaugh J, Fan RS, Zborowska E, Kinzler KW, Vogelstein B, et al. Inactivation of the type II TGF-beta receptor in colon cancer cells with microsatellite instability. Science. 1995;268(5215):1336–8.

    Article  CAS  PubMed  Google Scholar 

  90. Markowitz SD, Bertagnolli MM. Molecular origins of cancer: molecular basis of colorectal cancer. N Engl J Med. 2009;361(25):2449–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Tsushima H, Ito N, Tamura S, Matsuda Y, Inada M, Yabuuchi I, Imai Y, Nagashima R, Misawa H, Takeda H, et al. Circulating transforming growth factor beta 1 as a predictor of liver metastasis after resection in colorectal cancer. Clinical cancer research: an official journal of the American Association for Cancer Research. 2001;7(5):1258–62.

    CAS  Google Scholar 

  92. Calon A, Espinet E, Palomo-Ponce S, Tauriello DV, Iglesias M, Cespedes MV, Sevillano M, Nadal C, Jung P, Zhang XH, et al. Dependency of colorectal cancer on a TGF-beta-driven program in stromal cells for metastasis initiation. Cancer Cell. 2012;22(5):571–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Carvajal G, Droguett A, Burgos ME, Aros C, Ardiles L, Flores C, Carpio D, Ruiz-Ortega M, Egido J, Mezzano S. Gremlin: a novel mediator of epithelial mesenchymal transition and fibrosis in chronic allograft nephropathy. Transplant Proc. 2008;40(3):734–9.

    Article  CAS  PubMed  Google Scholar 

  94. Worthley DL, Churchill M, Compton JT, Tailor Y, Rao M, Si Y, Levin D, Schwartz MG, Uygur A, Hayakawa Y, et al. Gremlin 1 identifies a skeletal stem cell with bone, cartilage, and reticular stromal potential. Cell. 2015;160(1–2):269–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Kim HS, Shin MS, Cheon MS, Kim JW, Lee C, Kim WH, Kim YS, Jang BG. GREM1 is expressed in the cancer-associated myofibroblasts of basal cell carcinomas. PLoS One. 2017;12(3):e0174565.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Han EJ, Yoo SA, Kim GM, Hwang D, Cho CS, You S, Kim WU. GREM1 is a key regulator of Synoviocyte hyperplasia and invasiveness. J Rheumatol. 2016;43(3):474–85.

    Article  PubMed  Google Scholar 

  97. Karagiannis GS, Berk A, Dimitromanolakis A, Diamandis EP. Enrichment map profiling of the cancer invasion front suggests regulation of colorectal cancer progression by the bone morphogenetic protein antagonist, gremlin-1. Mol Oncol. 2013;7(4):826–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Jang BG, Kim HS, Chang WY, Bae JM, Oh HJ, Wen X, Jeong S, Cho NY, Kim WH, Kang GH. Prognostic significance of stromal GREM1 expression in colorectal cancer. Hum Pathol. 2017;62:56–65.

    Article  CAS  PubMed  Google Scholar 

  99. Pelli A, Vayrynen JP, Klintrup K, Makela J, Makinen MJ, Tuomisto A, Karttunen TJ. Gremlin1 expression associates with serrated pathway and favourable prognosis in colorectal cancer. Histopathology. 2016;69(5):831–8.

    Article  PubMed  Google Scholar 

  100. Hosper NA, van den Berg PP, de Rond S, Popa ER, Wilmer MJ, Masereeuw R, Bank RA. Epithelial-to-mesenchymal transition in fibrosis: collagen type I expression is highly upregulated after EMT, but does not contribute to collagen deposition. Exp Cell Res. 2013;319(19):3000–9.

    Article  CAS  PubMed  Google Scholar 

  101. Liu J, Eischeid AN, Chen XM. Col1A1 production and apoptotic resistance in TGF-beta1-induced epithelial-to-mesenchymal transition-like phenotype of 603B cells. PLoS One. 2012;7(12):e51371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Sato Y, Harada K, Itatsu K, Ikeda H, Kakuda Y, Shimomura S, Shan Ren X, Yoneda N, Sasaki M, Nakanuma Y. Epithelial-mesenchymal transition induced by transforming growth factor-{beta}1/Snail activation aggravates invasive growth of cholangiocarcinoma. Am J Pathol. 2010;177(1):141–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Kirkland SC. Type I collagen inhibits differentiation and promotes a stem cell-like phenotype in human colorectal carcinoma cells. Br J Cancer. 2009;101(2):320–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Zou X, Feng B, Dong T, Yan G, Tan B, Shen H, Huang A, Zhang X, Zhang M, Yang P, et al. Up-regulation of type I collagen during tumorigenesis of colorectal cancer revealed by quantitative proteomic analysis. J Proteome. 2013;94:473–85.

    Article  CAS  Google Scholar 

  105. Olivier M, Hollstein M, Hainaut P. TP53 mutations in human cancers: origins, consequences, and clinical use. Cold Spring Harb Perspect Biol. 2010;2(1):a001008.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Wang L, Yu P. miR-300 promotes proliferation and EMT-mediated colorectal cancer migration and invasion by targeting p53. Oncol Rep. 2016;36(6):3225–32.

    Article  CAS  PubMed  Google Scholar 

  107. Matoba S, Kang JG, Patino WD, Wragg A, Boehm M, Gavrilova O, Hurley PJ, Bunz F, Hwang PM. p53 regulates mitochondrial respiration. Science. 2006;312(5780):1650–3.

    Article  CAS  PubMed  Google Scholar 

  108. Kawauchi K, Araki K, Tobiume K, Tanaka N. p53 regulates glucose metabolism through an IKK-NF-kappaB pathway and inhibits cell transformation. Nat Cell Biol. 2008;10(5):611–8.

    Article  CAS  PubMed  Google Scholar 

  109. Vousden KH, Ryan KM. p53 and metabolism. Nat Rev Cancer. 2009;9(10):691–700.

    Article  CAS  PubMed  Google Scholar 

  110. Liu J, Zhang C, Hu W, Feng Z: Tumor suppressor p53 and its mutants in cancer metabolism. Cancer letters 2015, 356(2 Pt A):197–203.

  111. Al-Khayal K, Abdulla M, Al-Obeed O, Al Kattan W, Zubaidi A, Vaali-Mohammed MA, Alsheikh A, Ahmad R. Identification of the TP53-induced glycolysis and apoptosis regulator in various stages of colorectal cancer patients. Oncol Rep. 2016;35(3):1281–6.

    Article  CAS  PubMed  Google Scholar 

  112. Harami-Papp H, Pongor LS, Munkacsy G, Horvath G, Nagy AM, Ambrus A, Hauser P, Szabo A, Tretter L, Gyorffy B. TP53 mutation hits energy metabolism and increases glycolysis in breast cancer. Oncotarget. 2016;7(41):67183–95.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

Authors thank National Medical Research Center of Radiology, Pletnev State Hospital, and Pirogov Russian National Research Medical University for supplying samples; Vavilov Institute of General Genetics for the assistance in 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 is included in this published article and its supplementary information files.

About this supplement

This article has been published as part of BMC Genomics Volume 19 Supplement 3, 2018: Selected articles from Belyaev Conference 2017: genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume-19-supplement-3.

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    1. Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia

      Elena A. Pudova, Anna V. Kudryavtseva, Maria S. Fedorova, Elena N. Lukyanova, Asiya F. Sadritdinova, Ivan S. Abramov, Sergey L. Kharitonov, George S. Krasnov, Nataliya V. Melnikova, Alexey A. Dmitriev & Anastasiya V. Snezhkina

    2. National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia

      Anna V. Kudryavtseva, Nadezhda V. Koroban, Nadezhda N. Volchenko, Kirill M. Nyushko, Maria A. Chernichenko, Dmitry V. Sidorov, Boris Y. Alekseev, Marina V. Kiseleva & Andrey D. Kaprin

    3. Pirogov Russian National Research Medical University, Moscow, Russia

      Andrew R. Zaretsky & Dmitry S. Shcherbo

    4. Vavilov Institute of General Genetics, Russian Academy of Sciences Moscow, Moscow, Russia

      Elena N. Lukyanova & Kseniya M. Klimina

    5. Pletnev State Hospital, Moscow, Russia

      Anatoly Y. Popov

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    Contributions

    EAP, AVK, ARZ, AAD, and AVS conceived and designed the work; EAP, MSF, AFS, ISA, SLK, NNV, KMN, MAC, DSS, DVS, and AVS performed the experiments; ENL, EAP, AVK, ARZ, GSK, NVM, AAD, MSF, KMK, AYP, MVK, NVK, and AVS analyzed the data; EAP, AVK, ADK, BYA, AAD, and AVS wrote the manuscript. All authors read and approved the final manuscript.

    Corresponding author

    Correspondence to Anastasiya V. Snezhkina.

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    Ethics approval and consent to participate

    The study was approved by The Ethics committee of Herzen Moscow Oncology Research Institute, Ministry of Health of the Russian Federation. The study was done in accordance with the principles outlined in the Declaration of Helsinki (1964).

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    Not applicable.

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    The authors declare that they have no competing interests.

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    Pudova, E.A., Kudryavtseva, A.V., Fedorova, M.S. et al. HK3 overexpression associated with epithelial-mesenchymal transition in colorectal cancer. BMC Genomics 19 (Suppl 3), 113 (2018). https://doi.org/10.1186/s12864-018-4477-4

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    • DOI: https://doi.org/10.1186/s12864-018-4477-4

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    BMC Genomics

    ISSN: 1471-2164

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