IMR Press / EJGO / Volume 40 / Issue 4 / DOI: 10.12892/ejgo4765.2019
Open Access Original Research
Genomic copy number alteration of glycolytic pathway in endometrial cancer
Show Less
1 Department of Biology, Mustafa Kemal University, Hatay, Turkey
2 Department of Medical Genetics, Kocaeli University, Kocaeli, Turkey
3 Department of Obstetrics and Gynecology, Kocaeli University, Kocaeli, Turkey
Eur. J. Gynaecol. Oncol. 2019, 40(4), 640–646;
Revised: 23 January 2019 | Published: 10 August 2019

Metabolic reprogramming is one of the hallmarks of cancer cells, but very little is known about the difference in the expression of metabolic genes between cancer and normal tissues. The degree to which different cancer types display similar metabolic alteration is poorly understood. The best-known example of metabolic disturbance in cancer cells is the Warburg effect. The Warburg effect is the phenomenon of the cancer cells favoring the anaerobic glycolysis even in the presence of oxygen. It is displayed by most cancer cells. Although the genomic, transcriptomic, and proteomic studies have been published, metalobomic differences are still a major gap in our knowledge. Among women the most common malignancy is endometrial cancer. In this retrospective study, the authors investigated the genomic instability of glycolytic genes in endometrial carcinoma patients using array-based comparative genomic hybridization (aCGH) reports. The results indicate that among 54 patients diagnosed with endometrial carcinoma, in 21 patients, pathogenic genomic instability was detected which are linked with the disease. Among the 21 patients who had genomic instability, 19 of them (90.5%) displayed copy number variations of at least one or more glycolysis genes based on the genomic laboratory reports of the patients.

Copy number variation
Copy number alteration
Endometrial cancer
Glycolytic pathway
Comparative genomic hybridization (aCGH)
Genomic instability

Cancer is a cellular disturbance with complex molecular interactions. Cancer hallmarks are categorized as: 1) strengthening the proliferative signaling, 2) unsuppressing the molecular inhibitions of cellular growth, 3) activating invasion and metastasis, 4) switching to replicative immortality, 5) increasing angiogenesis, and 6) suppressing programmed cell death [1]. Although cancer cells must have replication potential to become macroscopic tumors, it is known that genomic replication is also perturbed. Replication stress is a broad terminology and can be described as a failure of an efficient DNA replication and may lead to genomic instability which is the recurrent patterns in cancer [2].

The effect of genomic instability on tumor metabolism has begun to attract interest, and the missing links between tumor biology and genetics and metabolism have begun to be sought. The Warburg effect is one of the oldest concepts of cancer. This phenomenon is based on the experiments of Otto Warburg in the 1920s when he observed a shift from oxidative to fermentative metabolism as a common physiological feature of cancer cells [3]. Warburg used an animal model to study the rapidly growing cancer cells and compared them with normal cells; later, he developed a hypothesis that the source of cancer is a metabolism-based disorder. For a long time, the Warburg effect was not accepted. Despite the early insight into cancer metabolism, the Warburg effect is still a phenomenon. To find missing links between tumor biology including genetics and metabolism, it is necessary to analyze the cancer genome, while considering the general metabolic pathways [4]. In this retrospective study, the authors analyzed the copy number variations of glycolytic metabolic pathways in endometrial cancer patients using the genomic laboratory report of the patients including comparative genomic hybridization array (aCGH) data. The analysis of aCGH of the metabolic enzymes in endometrial cancer patients indicates that >90% of the patients have copy number variations of glycolytic genes among the patients who have genomic aberrations within their genomes based on their genomic laboratory reports.

Materials and Methods

Microarray-based comparative genomic hybridization results of patients with endometrial cancer were evaluated and analyzed in terms of glycolytic pathways, retrospectively. The genomic profile of the patients was the only data used and analyzed to explore whether there is a common pattern of gain or loss of the any glycolytic gene(s) locus in endometrial cancer.

Microarray-based comparative genomic hybridization (aCGH) has been routinely used for assessing the gain and loss of genomic regions in Kocaeli University Medical Genetics department. For this purpose, genomic DNA was isolated from patients using the DNeasy blood and tissue kit. After agarose gel and nanodrop measuring of the isolated DNA, qualified ones have been used along with the reference human DNA. Both reference and sample genomic materials were labelled following the CytoChip protocol and hybridized with aCGH microchips (Cytochip Focus Constitutional microarrays). The microchips were read by a microarray scanner and analyzed using BlueFuse Multi v2.2 software program CytoChip algorithm with fixed threshold. NCBI36 assembly data was used by the software. The array quality was checked by the software based on SD autosome/robust, percentage included clones, Mean Spot, Amplitude Ch1/Ch2, SBR Ch1/Ch2, and DLR Raw/Fused.

Glycolytic genes and their positions on the chromosomes were identified and listed from the NCBI website and the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. Patient genomic gain and loss was analyzed using the glycolytic pathway genes’ locations taken from the NCBI database using NCBI36 assembly data. Statistic measurements were applied using the copy numbers from the array and standard deviations were calculated.


The number of genes involved in glycolysis based their locations in the genome were identified and listed in Table 1 according to the KEGG and NCBI databases. According to this list, chromosome 8 and chromosome Y do not contain any glycolytic genes, whereas chromosome 1 and chromosome 11 contain the highest number of glycolytic genes. The introduction of the oxygen into the atmosphere was about two billion years ago, and this suggests the first appearance of eukaryotes with aerobic metabolism [5]; however, it is not known how the chromosome evolution lineage has affected the distributions of positions of the glycolytic genes within the genome.

Table 1Glycolytic genes and their location on the genome. The highest number of the glycolytic genes are located on chromosomes 1 and 11 whereas chromosome 8, 14, and Y have none.

The general summary of chromosomal aberrations in the patients with endometrial cancer is shown in Table 2 based on their genomic laboratory reports. The total number of patients without chromosome aberrations was 33 out of 54 (61.3%) whereas that of with aberrations was 21 out of 54 (35.2%). Among the patients who had chromosomal aberrations, 19 of out of 21 (90.5%) had at least one glycolytic pathway gene(s) aberrations.

Table 2Displays the number of patients diagnosed with endometrial cancer with and without aberrations. Among a total of 33 patients, 33 had no copy number change, 21 had a copy number variation, and a total of 19 had multiple glycolytic gene copy number change.

Within the population of the patients diagnosed with endometrial cancer, the copy number of succinate dehydrogenase C (SDHC), pyruvate kinase LR (PKLR), and fumarate (FH) were the most affected genes in the glycolytic pathway and all displayed duplications rather than deletion. SDHC and PKLR were observed as duplicated in 14 out of 21 (66.6%) patients with aberrations with glycolytic pathway genes, while FH duplication was seen in 12 (57.1%) cases. As depicted in Table 3, hexokinase (HK) forms of HK1 and HK2; phosphofructokinase (PFK) form of PFKP, and glutamate dehydrogenase GLUD1 were also seen as duplicated in endometrial cancer patients.

Table 3Chromosomal locations of glycolytic genes.
Gene Symbol Gene Full Name Gene ID Location
GLUT1 (SLC2A1) solute carrier family 2 member 1 6513 1p34.2
HK1 hexokinase 1 3098 10q22.1
HK2 hexokinase 2 3099 2p12
HK3 hexokinase 3 3101 5q35.2
HKDC1 hexokinase domain containing 1 80201 10q22.1
GCK Glucokinase 2645 7p13
GPI glucose-6-phosphate isomerase 2821 19q13.11
PFKL phosphofructokinase, liver type 5211 21q22.3
PFKM phosphofructokinase, muscle 5213 12q13.11
PFKP phosphofructokinase, platelet 5214 10p15.2
ALDOA aldolase, fructose-bisphosphate A 226 16p11.2
ALDOB aldolase, fructose-bisphosphate B 229 9q31.1
ALDOC aldolase, fructose-bisphosphate C 230 17q11.2
TPI1 triosephosphate isomerase 1 7167 12p13.31
GAPDH glyceraldehyde-3-phosphate dehydrogenase 2597 12p13.31
GAPDHS glyceraldehyde-3-phosphate dehydrogenase, spermatogenic 26330 19q13.12
PGK1 phosphoglycerate kinase 1 5230 Xq21.1
PGK2 phosphoglycerate kinase 2 5232 6p12.3
PGM1 phosphoglucomutase 1 5236 1p31.3
PGM2 phosphoglucomutase 2 55276 4p14
PGM3 phosphoglucomutase 3 5238 6q14.1
PGM5 phosphoglucomutase 5 5239 9q21.11
ENO1 enolase 1 2023 1p36.23
ENO2 enolase 2 2026 12p13.31
ENO3 enolase 3 2027 17p13.2
PKM pyruvate kinase, muscle 5315 15q23
PKLR pyruvate kinase, liver and RBC 5313 1q22
LDHA lactate dehydrogenase A 3939 11p15.1
LDHB lactate dehydrogenase B 3945 12p12.1
LDHC lactate dehydrogenase C 3948 11p15.1
UEVLD UEV and lactate/malate dehyrogenase domains 55293 11p15.1
LDHAL6A lactate dehydrogenase A like 6A 160287 11p15.1
LDHAL6B lactate dehydrogenase A like 6B 92483 15q22.2
SLC16A1 solute carrier family 16 member 1 6566 1p13.2
SLC16A4 solute carrier family 16 member 4 9122 1p13.3
G6PD Glucose-6-phosphate dehydrogenase 2539 Xq28
PGD phosphogluconate dehydrogenase 5226 1p36.22
TKTL1 transketolase like 1 8277 Xq28
TKT Transketolase 7086 3p21.1
TKTL2 transketolase like 2 84076 4q32.2
TALDO1 transaldolase 1 6888 11p15.5
PHGDH phosphoglycerate dehydrogenase 6227 1p12
PSAT1 phosphoserine aminotransferase 1 29968 9q21.2
PSPH phosphoserine phosphatase 5723 7p11.2
PC pyruvate carboxylase 5091 11q13.2
PDHA1 pyruvate dehydrogenase E1 alpha 1 subunit 5160 Xp22.12
PDHA2 pyruvate dehydrogenase E1 alpha 2 subunit 5161 4q22.3
PDHB pyruvate dehydrogenase E1 beta subunit 5162 3p14.3
PDHX pyruvate dehydrogenase complex component X 8050 11p13
DLAT dihydrolipoamide S-acetyltransferase 1737 11q23.1
DLD dihydrolipoamide dehydrogenase 1738 7q31.1
CS citrate synthase 1431 12q13.3
ACO1 aconitase 1 48 9p21.1
ACO2 aconitase 2 50 22q13.2
ACO3 (IREB2) iron responsive element binding protein 2 3658 15q25.1
IDH1 isocitrate dehydrogenase (NADP(+)) 1, cytosolic 3417 2q34
IDH2 isocitrate dehydrogenase (NADP(+)) 2, mitochondrial 3418 15q26.1
IDH3A isocitrate dehydrogenase 3 (NAD(+)) alpha 3419 15q25.1
IDH3B isocitrate dehydrogenase 3 (NAD(+)) beta 3420 20p13
IDH3G isocitrate dehydrogenase 3 (NAD(+)) gamma 3421 Xq28
OGDH oxoglutarate dehydrogenase 4967 7p13
DLD dihydrolipoamide dehydrogenase 1738 7q31.1
PDHX pyruvate dehydrogenase complex component X 8050 11p13
SUCLG2 succinate-CoA ligase GDP-forming beta subunit 8801 3p14.1
SUCLG1 succinate-CoA ligase alpha subunit 8802 2p11.2
SUCLA2 succinate-CoA ligase ADP-forming beta subunit 8803 13q14.2
SDHA succinate dehydrogenase complex flavoprotein subunit A 6389 5p15.33
SDHB succinate dehydrogenase complex iron sulfur subunit B 6390 1p36.13
SDHC succinate dehydrogenase complex subunit C 6391 1q23.3
SDHD succinate dehydrogenase complex subunit D 6392 11q23.1
FH fumarate hydratase 2271 1q43
MDH2 malate dehydrogenase 2 4191 7q11.23
GLS glutaminase 2744 2q32.2
GLS2 glutaminase 2 27165 12q13.3
GLUD1 glutamate dehydrogenase 1 2746 10q23.2
GLUD2 glutamate dehydrogenase 2 2747 Xq24
ME2 malic enzyme 2 4200 18q21.2
ME3 malic enzyme 3 10873 11q14.2
ACLY ATP citrate lyase 47 17q21.2
ACACA acetyl-CoA carboxylase alpha 31 17q12
FASN fatty acid synthase 2194 17q25.3
SLC1A5 solute carrier family 1 member 5 6510 19q13.32
SLC7A5 solute carrier family 7 member 5 8140 16q24.2
GSS glutathione synthetase 2937 20q11.22
PPAT phosphoribosyl pyrophosphate amidotransferase 5471 4q12
GART hosphoribosylglycinamide formyltransferase, phosphoribosylglycinamide synthetase, phosphoribosylaminoimidazole synthetase 2618 21q22.11
PFAS phosphoribosylformylglycinamidine synthase 5198 17p13.1
ATIC 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase 471 2q35
CAD carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase 790 2p23.3
DHODH dihydroorotate dehydrogenase 1723 16q22.2
UMPS uridine monophosphate synthetase 7372 3q21.2

In order to display the functional positions of SDHC, PKLR, and FH, the glycolytic pathway is shown in Figure 1. The lowest SDHC copy number was 0.28 and the highest was 0.65. The mean value for the copy number for SDHC was 0.421. Calculated standard deviation was 0.119. PKLR had lowest (0.28) and highest (0.65) copy numbers. The mean copy number 0.4 and standard deviation of PKLR copy number was 0.117. Lastly, FH lowest and highest copy numbers were 0.27 and 0.65, respectively. The mean copy number for FH was 0.415 and the standard deviation was 0.126. Taken together, the most recurrent copy number changes in glycolytic pathway genes in endometrial cancer patients were SDHC, PKLR, and FH. The significance of the increased copy number of these three genes in endometrial carcinoma remains to be explored.

Figure 1.

— A) Glycolytic pathway scheme. B) SDHC, PKLR, and FH copy number arrangement levels in endometrial cancer patients are shown.


The succinate dehydrogenase enzyme (also known as succinate-ubiquinone oxydoreductase) is a highly conserved heterotetrameric protein complex, with SDHA and SDHB as catalytic subunits, which extend into the mitochondrial matrix and are anchored to the inner membrane by SDHC and SDHD [6,7]. These latter subunits provide also the binding site for the ubiquinone (coenzyme Q10 or Q as shown in Figure 2) then reducing it to ubiquinol (QH2). The SDH complex comprises mitochondrial complex II, which is involved in the Krebs cycle and in the electron transport chain (ETC) [8]. Complex II couples the oxidation of succinate to fumarate in the Krebs cycle with the electron transfer to the terminal acceptor ubiquinone in the ETC. Partial ubiquinone binding and stabilizing site of it is in SDHC along with SDHB and SDHD. As part of the mitochondrial electron transport chain, coenzyme Q10 cepts electrons from reducing equivalents generated during fatty acid and glucose metabolism and then transfers them to electron acceptors. At the same time, coenzyme Q10 transfers protons outside the inner mitochondrial membrane, creating a proton gradient across that membrane. The energy released when the protons flow back into the mitochondrial interior is used to form ATP [9, 10]. Mutations affecting the activity of SDH subunits in B, C, and D result in increased ROS production and enhanced tumorigenesis. Elevated rates of reactive oxygen species (ROS) have been detected in almost all cancers, where they promote many aspects of tumor development and progression [11]. Furthermore, loss of SDH leads to succinate build up. Hypoxia and succinate accumulation synergistically lead to hypermethylation of histones and DNA indirectly [12].

Figure 2.

— Succinate dehydrogenase and its subunits are shown in an electron transport chain

Overexpression of wild-type SDHC had no influence on the lifespan in C. elegans and overexpressed SDHC increased the amount of protein carbonyl compared to control, suggesting that deregulation of SDHC results in oxidative stress [13]. On the other hand in tumor cells, SDHC is thought to a be tumor suppressor; however, the tumor suppressor molecular mechanism of SDHC is yet to be defined. Hu et al. [14] analyzed more than 2,500 microarray using 22 different cancer-normal pairs and the meta analysis revealed that SDHC mRNA level has the highest expression fold change in female specific cancers compared to the other cancer types. Cervix squamous cell carcinoma samples displayed a 2.90 log2 fold higher differential expression for SDHC (p value= 3.4E-04). Ovary serous carcinoma showed a 1.72 (p value = 1.2E-02) (log2 scale) fold change for expression for SDHC.

Pyruvate kinase catalyzes the transfer of a phosphate group from phosphoenolpyruvate to ADP and converts to pyruvate and ATP in glycolysis. PK has different mammalian isoforms: PKM1, PKM2, and PKLR. Most adult tissues express PKM2; however, the other isoforms display tissue specificity. PKM1 is mostly expressed in tissues which have higher catabolism rates such as muscle, heart, and brain while PKLR is exclusively expressed in liver red blood cells [15]. PK activity is strictly regulated and an irreversible step in glycolysis after hexokinase and phosphofructokinase. The PK step is regulated by allosteric factors, covalent modifiers (phosphorylation), and hormones. Allosteric regulators of PK are alanine (a biosynthetic product of pyruvate) and ATP (negatively); and fructose-1,6-biphosphate (positively). Regulation through covalent modification of PK is via phosphorylation of the enzyme. High glucagon (low blood sugar) levels lead to PK phosphorylation causing restricted enzyme activity [16].

Inherited pyruvate kinase deficiency causes hemolytic anemia and leads red blood cells to break down easily. In this inherited disorder, pyruvate and lactate levels are lower than normal, while intermediates such as 1,3-bisphosphoglycerate (1,3 BPG) and phosphoenolpyruvate (PEP) accumulates. On the other hand, high levels of PKLR protein using transgenic expression in mice does not affect metabolic variables. Mice expressing high levels of PK have normal PK activity and ATP levels indicating that the transgenic expression of PK in these cells did not affect the biochemical balance of the energy pathway, probably due to delicate regulation of glycolytic pathway by other key enzymes, intermediary metabolites, and redox coenzymes. Serum levels of PK are measured as an indication of stability of the internal environment. Normal levels of serum PK were observed, indicating normal homeostatic balance and no side effects in leukocytes are produced by the increased PK expression through transgenic expression in mice [17].

Human protein atlases for normal and cancer tissues based on antibody staining and proteomics studies ( indicates that pathological protein expression of PKLR is linked with different cancer types including endometrial cancer [18-20]. Although very low, the liver and red blood cell specific forms of PKLR were found to be expressed in endometrial cancer tissue. Since PKLR is specific to liver and red blood cells, it remains to be explored why endometrial cancer tissue prefers to express a silenced gene.

Fumarase (fumarate hydratase) is an enzyme that catalyzes the reversible hydration/dehydration of fumarate to malate in citric acid cycle. Fumarase was identified as a mitochondrial tumour suppressor gene in families with the hereditary uterine leiomyomatosis and renal cell carcinoma (HLRCC) syndrome [21]. HLRCC syndrome is a genetic disorder and germline loss of function mutations of fumarase gene lead to an increased risk of cutaneous and uterine leiomyomas and renal cancer. Somatic loss of function of FH mutations are not common but are mostly seen in uterus leiomyomas [22].

FH which ubiquitously expressed throughout the body is also known as a tumor suppressor [23]. The increased genomic copy number of FH in the present study may be correlated with the mRNA level and might be consistent with the result of a Hu et al.’s retrospective study [24]. Their meta analysis showed that FH also has the highest expression fold change -like SDHC -in female specific cancers compared to the other cancer types. Cervix squamous cell carcinoma indicated 0.52 log2 fold higher differential expression for FH (p = 1.6E-01, respectively). Ovary serous carcinoma 1.08 log2 fold (p value = 4.5E-02) change for expression for FH. FH’s role in female specific cancers is yet to be explored.


Multiple genes encoding glycolytic pathway enzymes are related to tumor metabolism including SDHB, SDHC, and succinate dehydrogenase subunits B,C,D (SDHD), FH, PK [6,15,22,23,25]. Copy number alteration of SDHC, FH, and PKLR glycolytic pathway genes seem to be recurrent in endometrial cancer patients. Many recurrent CNA cannot be fully explained by the presence of known cancer genes although oncogenes and tumor suppressors force and lead to some recurrent CNA in the tumor cells [2]. Analysis of different tumors can describe a defined CNA signature. This might be a predictive tool in the future for cancer patients [2]. However, the preference of cancer cells for predictive metabolic phenotypes and CNA still remains to be explored.

Since the discovery of Warburg effect in 1920s, the significance of metabolic reprogramming in carcinogenesis continues to grow and directs pharmacological drug design. Further research on tumor metabolism will shed light on how to most effectively and selectively destroy cancer cells.

Hanahan D., Weinberg R.A.: “Hallmarks of cancer: The next generation”. Cell, 2011,144, 646. 10.1016/j.cell.2011.02.01321376230
Graham N.A, Minasyan A., Lomova A., Cass A., Balanis N.G., Friedman M., et al.: “Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures”. Mol. Syst. Biol., 2017,13, 914. 10.15252/msb.2016715928202506
Warburg O.: “On the Origin of Cancer Cells”. Science, 1956,123, 309. 10.1126/science.123.3191.30913298683
Wu W., Zhao S.: “Metabolic Changes in Cancer: Beyond the Warburg Effect”. Acta Biochim. Biophys. Sin. (Shanghai), 2013,45, 18. 10.1093/abbs/gms104
Stamati K., Mudera V., Cheema U.: “Evolution of oxygen utilization in multicellular organisms and implications for cell signalling in tissue engineering”. J. Tissue Eng., 2011,2, 2041731411432365. 10.1177/204173141143236522292107
Bardella C., Pollard P.J., Tomlinson I.: “SDH mutations in cancer”. Biochim. Biophys. Acta, 2011,1807, 1432. 10.1016/j.bbabio.2011.07.00321771581
Rutter J., Winge D.R., Schiffman J.D.: “Succinate dehydrogenase - Assembly, regulation and role in human disease”. Mitochondrion, 2010,10, 393. 10.1016/j.mito.2010.03.0013e6ad33e-58f4-4a46-8f34-8e125152978a
Gaude E., Frezza C.: “Defects in mitochondrial metabolism and cancer”. Cancer Metab., 2014,2, 10. 10.1186/2049-3002-2-1025057353
Cooper G.M.: “The Mechanism of Oxidative Phosphorylation”. In: The Cell: A Molecular Approach. 2nd ed. Sunderland (MA): Sinauer Associates, 2000.
Alberts B., Johnson A., Lewis J., Raff M., Roberts K., Walter P.: “The Mitochondrion”. In: Molecular Biology of the Cell. 4th ed. New York: Garland Science, 2002.
Sullivan L.B., Gui D.Y., Heiden M.G.V.: “Altered metabolite levels in cancer: implications for tumour biology and cancer therapy”. Nat.Rev. Cancer, 2016,16, 680. 10.1186/s12885-016-2700-827558259
Xiao M., Yang H., Xu W., Ma S., Lin H., Zhu H., et al.: “Inhibition of α-KG-dependent histone and DNA demethylases by fumarate and succinate that are accumulated in mutations of FH and SDH tumor suppressors”. Genes Dev., 2012,26, 1326. 10.1101/gad.191056.11222677546
Tsuda M., Sugiura T., Ishii T., Ishii N., Aigaki T.: “A mev-1-like dominant-negative SdhC increases oxidative stress and reduces lifespan in Drosophila”. Biochem. Biophys. Res. Commun., 2007,363, 342. 10.1016/j.bbrc.2007.08.16817854771
Hu J., Locasale J.W., Bielas J.H., O’Sullivan J., Sheahan K., Cantley L.C. et al.: “Heterogeneity of tumor-induced gene expression changes in the human metabolic network”. Nat. Biotechnol., 2013,31, 522. 10.1038/nbt.253023604282
Israelsen W.J., Heiden M.G.V.: “Pyruvate kinase: function, regulation and role in cancer”. Semin. Cell Dev. Biol., 2015,43, 43. 10.1016/j.semcdb.2015.08.00426277545
Berg J.M., Tymoczko J.L., Lubert S.: ”The Glycolytic Pathway Is Tightly Controlled”. In: Biochemistry. 5th ed. New York: W H Freeman, 2002.
Meza N.W., Alonso-Ferrero M.E., Navarro S., Quintana-Bustamante O., Valeri A., Garcia-Gomez M., et al.: “Rescue of Pyruvate Kinase Deficiency in Mice by Gene Therapy Using the Human Isoenzyme”. Mol. Ther., 2009,17, 2000. 19755962
Uhlen M., Zhang C., Lee S., Sjöstedt E., Fagerberg L., Bidkhori G., et al.: “A pathology atlas of the human cancer transcriptome”. Science, 2017, 357. pii: eaan2507.
Thul P.J., Åkesson L., Wiking M., Mahdessian D., Geladaki A., Blal H.A., et al.: “A subcellular map of the human proteome”. Sience, 2017, 356. pii: eaal3321.
Uhlén M., Björling E., Agaton C., Szigyarto C.A., Amini B., Andersen E., et al.: “A Human Protein Atlas for Normal and Cancer Tissues Based on Antibody Proteomics”. Mol. Cell Proteomics, 2005,4(12), 1920. 10.1074/mcp.M500279-MCP20016127175
Chen Y.B., Brannon A.R., Toubaji A., Dudas M.E., Won H.H., Al-Ahmadie H.A., et al.: “Hereditary leiomyomatosis and renal cell carcinoma syndrome-associated renal cancer: recognition of the syndrome by pathologic features and the utility of detecting aberrant succination by immunohistochemistry”. Am. J. Surg. Pathol., 2014,38, 627. 10.1097/PAS.000000000000016324441663
King A., Selak M.A., Gottlieb E.: “Succinate dehydrogenase and fumarate hydratase: linking mitochondrial dysfunction and cancer”. Oncogene, 2006,25, 4675. 10.1038/sj.onc.120959416892081
Bardella C., Olivero M., Lorenzato A., Geuna M., Adam J., O’Flaherty L., et al.: “Cells lacking the fumarase tumor suppressor are protected from apoptosis through a HIF-independent, AMPK dependent mechanism”. Mol. Cell Biol., 2012,32, 3081. 10.1128/MCB.06160-11cd3bbc0d-0f79-44f1-adf8-47cca9248bcf
Hu J., Locasale J.W., Bielas J.H., O’Sullivan J., Sheahan K., Cantley L.C., et al.: “Heterogeneity of tumor-induced gene expression changes in the human metabolic network”. Nat. Biotechnol., 2013,31, 522. 23604282
Nguyen A., Loo J.M., Mital R., Weinberg E.M., Man F.Y., Zeng Z., et al.: “PKLR promotes colorectal cancer liver colonization through induction of glutathione synjournal”. J. Clin. Invest., 2016,126, 681. 26784545
Back to top