Genes of glycolysis are ubiquitously overexpressed in 24 cancer classes

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1 Genomics 84 (2004) Genes of glycolysis are ubiquitously overexpressed in 24 cancer classes B. Altenberg a, *, K.. Greulich b a Bioinformatics Group, European Molecular Biology Laboratory, Meyerhofstrasse 1, D Heidelberg, Germany b Department of Single Cell and Single Molecule Techniques, Institute for Molecular Biotechnology, Beutenbergstrasse 11, D Jena, Germany Received 27 July 2004; accepted 8 August 2004 Available online 25 September 2004 Abstract Using NIH s public database dbest for expression of genes and ESTs, genes of the glycolysis pathway have been found to be overexpressed in a set of 24 cancers representing more than 70% of human cancer cases worldwide. Genes can be classified as those that are almost ubiquitously overexpressed, particularly glyceraldehyde-3-phosphate dehydrogenase, enolase 1, and also pyruvate kinase, and those that are overexpressed in less than 50% of the investigated cancers. Cancers can be classified as those with overexpression of the majority of the glycolysis genes, particularly lymph node, prostate, and brain cancer, in which essentially all glycolysis genes are overexpressed, and those with only sporadic overexpression, particularly cancers of the cartilage or bone marrow. This classification may be useful when cancer therapies aimed at the Warburg effect are designed. D 2004 Elsevier Inc. All rights reserved. Keywords: dbest database; Gene overexpression; Glycolysis; Cancers; Warburg effect * Corresponding author. Fax: address: altenber@embl.de (B. Altenberg). Despite the widely accepted view of the functional importance of glycolysis in cancer, surprisingly little is known about the influence of gene expression levels on increased rates of this metabolism. More seriously, the assumption that cancer cells are inherently glycolytic (Warburg effect) has been recently questioned [1]. Here we try to resolve this apparent contradiction by a suitable classification of cancers with respect to glycolysis enhancement. Glycolysis is often studied in the context of one or, at most, a few cancers in a single investigation. For example, increased uptake of fluorodeoxyglucose in animal models, often used for PET imaging, is not caused by increased proliferation but by differences in the transcription of glycolysis-associated genes [2]. Inducibility of 6-phosphofructo-2-kinase has been studied in cell lines of several human cancers [3]. Glycolysis enhancement is possible via different mechanisms such as gene amplification, increased gene expression, increased translation, posttranslational modification, and regulation by protein-protein interactions in the cytoplasm or even by small RNA networks or RNA interference. Given this large number of different mechanisms, and the assumption that carcinogenesis is the result of a large number of stochastic events, one might expect that different cancers exploit different mechanisms to achieve increased glucose consumption. To show the importance of one of these mechanisms, gene expression, in glycolysis enhancement, an overview comprising the majority of human cancers is required. Gene expression data derived, among others, from oligonucleotide or cdna chips now offer the chance to investigate this aspect essentially for all cancers in parallel. Such studies often provide expression data for thousands of different genes or ESTs at once (i.e., more than typically evaluated in one experiment). Thus, glycolysis data are often buried in data generated for other purposes. Fortunately, most of these data are available in gene expression libraries and evaluated by a program package made available by the NIH. This makes it possible to use the data to investigate questions not addressed in the original studies. In the present work, we have used this /$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi: /j.ygeno

2 B. Altenberg, K.. Greulich / Genomics 84 (2004) package to reveal that genes of the glycolysis pathway are overexpressed in the majority of clinically relevant cancers. This indicates which mechanism cancers prefer to achieve increased glycolysis and supports, for a majority of cancers, the classical view that the Warburg effect indeed is relevant. Results Table 1 lists 49 cancer classes as provided by bvirtual Northern.Q The left column lists those 24 cancers in which overexpression has been found, i.e., in which at least for 1 of the 10 glycolysis genes one p value (see Materials and methods) is b0.05. In the right column are 25, primarily rare, cancers in which none of the 10 genes meets this requirement. A cancer may be listed in the right column simply due to the fact that it has not yet been investigated with sufficient detail to give information on overexpression. Cancers in the right column will not be included in the following discussion of overexpression data. To give an idea of how frequent the cancers in the left column are, i.e., how relevant these data are for general statements on cancer, the second column gives the number of incidences in 2001, according to Ref. [4]. Cancers of the brain and nervous system are listed in the Brain row, Table 1 Cancers in which overexpression of glycolysis genes has been found, the number of cases worldwide in 2001 (male and female added), and cancers in which this overexpression has not been found Cancers with overexpression Bone Bone marrow Cases worldwide in 2001 Cancers without overexpression Adipose Adrenal cortex Brain 75,610 Adrenal medulla Cartilage Cerebellum Cervix 470,606 Cerebrum Colon 944,717 Ear Eye Embryonic tissue Head and neck Endocrine Kidney 189,077 Esophagus Liver 564,336 Gastrointestinal tract Lung 1,238,861 Germ cell Lymph node 423,529 Genitourinary Lymphoreticular Heart Mammary gland 1,050,346 Pancreatic island Muscle Peripheral nervous system Nervous system Pineal gland vary 192,379 Pituitary gland Pancreas 216,367 Retina Placenta Salivary gland Prostate 542,990 Spleen Skin 132,602 Synovium Stomach 876,341 Thymus Testis 49,302 Thyroid Uterus 188,952 Vascular White blood cells Hodgkin and non-hodgkin lymphoma and multiple myeloma are listed under Lymph node cancers, for skin cancers only data for melanoma are available. All numbers in the second column of Table 1 add up to 7,156,015 cases worldwide. Compared with the 10,055,551 cancer cases diagnosed in total worldwide in 2001, the ratio (0.71 or 71%) indicates how frequent the cancers with glycolysis gene overexpression are. This is only a lower limit, since for some of the cancers on the left no incidence data are available. Probably, our study comprises more than 80% of worldwide cancer incidences. Table 2A lists the 10 glycolysis genes (rows) that are overexpressed in the selected 24 cancers (columns), i.e., 240 combinations. For a graphical representation of the glycolysis pathway see for example edu/glycolysis/pathway.html. If the corresponding gene is overexpressed in a given cancer, this is marked by an bq.if a given position in Table 2 is empty, this may be for one of two reasons: either it has not yet been investigated or it is indeed not overexpressed. Thus, with increasing data in the literature, empty positions may still be filled with an bq, particularly for rare cancers. Correspondingly, cancers of Table 1 now listed in the right column may then move to the left column. The three columns on the left of Table 2 give the reaction step number in glycolysis and the gene name in two versions. The column on the right indicates in how many of the 24 investigated cancers the corresponding gene is overexpressed. The row at the bottom of Table 2A shows how many of the 10 glycolysis genes have so far been found to be overexpressed in a given cancer (column). Cancers of the brain have all glycolysis genes overexpressed, those of the prostate and lymph node lack overexpression in only one enzyme. ther cancers, such as those of the bone, bone marrow, cervix, or cartilage, either are not yet investigated in sufficient detail or use other strategies to enhance their glycolysis (see next paragraph). An example of a cancer using such an alternative strategy is gastric tumors, which are only partially included in Table 2. For this class of tumors it was shown by RT- PCR and immunohistochemistry that the glucose transporter GLUT-1, which is not expressed in normal gastric tissue, is found expressed in 19/20 gastric carcinoma samples [5]. bviously cancers of the gastrointestinal tract enhance their glucose metabolism by increased glucose transport and not so much by enhanced glycolysis. Table 2C shows that GLUT-1 to -4 genes reveal a moderate expression pattern (6/24 cancers), i.e., glucose transport appears not to be ubiquitously enhanced by gene overexpression. Also included in Table 2C are the lactate dehydrogenases, which are involved in the anaerobic part of glycolysis. These genes are overexpressed in 13 of 17 cancers, i.e., they can be seen already as almost ubiquitously overexpressed in cancers. To get an idea of the general importance of overexpression we have investigated 10 other biochemical

3 1016 B. Altenberg, K.. Greulich / Genomics 84 (2004) Table 2 Genes and their expression status in 24 cancers Gene name Gene description Brain Lymph Prostate Skin Kidney Stomach Testis node (A) Glycolysis pathway (p b 0.05) Step 1 ATP Y ADP HK1 Hexokinase 1* Step 2 GPI Glucose phosphate isomerase Step 3 ATP Y ADP PFKL Phosphofructokinase, liver Step 4 ALDA Aldolase A, fructose bisphosphate Step 5 TPI TPI1, triosephosphate isomerase 1 Step 6 2NAD Y 2NADH GAPD Glyceraldehyde-3-phosphate dehydrogenase Step 7 ADP Y ATP PGK1 Phosphoglycerate kinase 1 Step 8 PGAM1 Phosphoglycerate mutase 1 (brain) Step 9 ADP Y ATP EN EN1, enolase 1 (a) Step 10 PKM PKM2, pyruvate kinase, muscle Sum up per pathway (B) Glycolysis pathway (p b 0.01) HK1 Hexokinase 1* GPI Glucose phosphate isomerase PFKL Phosphofructokinase, liver ALDA Aldolase A, fructose bisphosphate TPI TPI1, triosephosphate isomerase 1 GAPD Glyceraldehyde-3-phosphate dehydrogenase PGK1 Phosphoglycerate kinase 1 PGAM1 Phosphoglycerate mutase 1 (brain) EN EN1, enolase 1 (a) PKM PKM2, pyruvate kinase, muscle Sum up per pathway (C) Anaerobic glycolysis and transport enzymes Lactate LDHA Lactate dehydrogenase A Lactate LDHB Lactate dehydrogenase B Transport GLUT1 SLC2A1, solute carrier family 2 (facilitated glucose transporter), member 1 Sum up per pathway (D) Citric acid cycle Step 1 H 2 X CoA SH CS Citrate synthase Step 2 Y H 2 Step 3 Y H 2 AC2 Aconitase 2, mitochondrial Step 4 Y C 2 IDH2 Isocitrate dehydrogenase 2 (NADP), mitochondrial Step 5 CoA SH Y C 2 dehydrogenase (lipoamide) Step 6 GDP(ADP) Y GTP(ATP) SUCLA2 Succinate-CoA ligase, ADP-forming, h subunit Step 7 SDHA Succinate dehydrogenase complex, subunit A, flavoprotein (Fp) Step 8 FH Fumarate hydratase Step 9 MDH1 Malate dehydrogenase 1, NAD (soluble) Sum up per pathway Genes are listed in rows, cancers in columns. When a gene is found to be overexpressed in a given cancer the corresponding position is marked by an bq. (A) Genes of glycolysis, calculated with a p b (B) Same analysis, however with p b (C) Genes of the anaerobic continuation of glycolysis. (D) Genes of the citric acid pathway for comparison.

4 B. Altenberg, K.. Greulich / Genomics 84 (2004) Lung Liver Placenta Pancreas Uterus vary Eye Head and neck Mammary gland Nervous Lympho reticular Colon Muscle Cartilage Cervix Bone marrow Bone Sum up

5 1018 B. Altenberg, K.. Greulich / Genomics 84 (2004) pathways in a way similar to what was done for glycolysis. None of them showed a similarly clear overexpression pattern. As a representative of these pathways, the citric acid cycle is listed in Table 2D. It can immediately be seen that none of the genes is overexpressed in more than 50% of the cancers and that in none of the cancers are more than 50% of the genes overexpressed. Discussion The data show that in glycolysis the overexpression of genes is the rule rather than the exception (149 of 240 possible b sq or 62% in Table 2A). The result is dependent on the statistical assumptions made. With p = 0.05 (see Materials and methods) 12 type 1 errors (false positives) are statistically expected. Then 137/240 = 57% of the genes are found overexpressed. With the much more stringent p = 0.01 (Table 2B) 119/240 genes are found overexpressed. This value is, however, probably too low due to type 2 errors, i.e., false negatives. Independent of these statistical aspects the finding that overexpression of genes of glycolysis is a general phenomenon in cancers is correct. Cancers of the brain, lymph node, and prostate show almost exclusively overexpression. In contrast, cancers of the colon and mammary gland appear to use additional strategies, though even here glycolysis is affected. For cancers of the cervix, cartilage, bone marrow, and bone, classification as cancers with at least a few overexpressed genes is uncertain due to type 1 errors. A particularly interesting case is enhanced glycolysis in colon cancer, which is generally an overexpression cancer, i.e., many genes not involved in glycolysis are overexpressed (our own unpublished data). If glycolysis enhancement were just a consequence of general gene derangement, one would expect particularly for this cancer that glycolysis would be enhanced by overexpression. However, our results indicate that rather the opposite is true (Table 2). Ubiquitous gene overexpression appears to be restricted to glycolysis. For other biochemical pathways such a consistent overexpression pattern has so far not been found, not even for the citric acid cycle, which might also supply energy to the cancer tissue (45 of 192 possible positions with overexpression or 23% in Table 2D). ther pathways investigated so far show even less consistent overexpression. In conclusion, glycolysis is indeed special. The findings of this study may also be of some interest for therapy. Gene expression patterns in general can be modified by external factors such as drugs or components of nutrition. Thus one may envision substances that modify expression of glycolysis genes as complementary to conventional cancer therapies. Materials and methods bvirtual Northern,Q one function of the public database dbest [6 8] on the Gene Info Page from NCI s Cancer Genome Anatomy Project serves as a basis for the investigations of the present work. Virtual Northern searches in databases that contain cdna or EST data [9,10]. Unfortunately, the direct Web address changes with time. Therefore one has to use GeneFinder at cgap.nci.nih.gov/genes/genefinder to get access to the table Monochromatic SAGE/cDNA Virtual Northern. Virtual Northern is an in silico analysis. To get an idea of the meanings of numbers and quantities, one can imagine a hypothetical gene chip carrying all 10,000 genes that are typically expressed in a given tissue. ne average singlecopy gene would show one hybridization spot on such a chip, i.e., a hybridization frequency of 1:10,000 or 0.01%. In reality there are genes that for their regular function are expressed more frequently than average, i.e., values higher than 0.01% do not necessarily indicate overexpression. verexpression is given only when in a real gene or EST expression study the same gene is expressed more frequently in cancer than in the normal tissue. In principle any expression ratio above 1 may be acceptable as overexpression, provided the statistics allows a safe statement (see below). In practice it turns out that ratios above 1.5 are acceptable. This makes biological sense, since for other diseases it is known that 50% overexpression of genes may cause serious problems. dbest data are the results of many experiments under different experimental conditions and thus have gene and EST redundancy as well as experimental errors. Thus, there is some statistical uncertainty, particularly in tissues in which only a small number of genes or ESTs have so far been investigated. To avoid false positive overexpression statements (type 1 errors), the confidence value p is introduced. If it is zero, one can confidently assert that the gene is overexpressed. If the p value is high, the underlying data should not be included in the evaluation. For the present problem the actual calculation of the p value is not trivial because the underlying statistics is not Gaussian but essentially unknown and because the datasets to be compared (normal tissue vs cancer tissue) may differ largely in size. Lal et al. [11] have, by reasonable assumptions on a possible underlying statistics, calculated the probability density for a quantity x. The latter is related (by a factor of 100) to the percentage by which an mrna is represented in the larger dataset (of size A) compared to its representation in both datasets. Lal et al. [11] provide a formula that calculates the p value. Essentially, p is related to the library sizes A and B and the number of hits of a given gene or EST a and b in these libraries. It is included in the table provided by dbest (see Table 3). Experience shows that p values below 0.05 give an acceptable statistical safety for stating overexpression. Statistically a p value of 0.05 means that 1 in 20 statements on overexpression may be incorrect, i.e.,

6 B. Altenberg, K.. Greulich / Genomics 84 (2004) Table 3 EST data given by bvirtual NorthernQ for the example of GAPD, the enzyme catalyzing step 6 of glycolysis Tissue ESTs normal ESTs cancer ESTs p All tissues 4459/2,154,250 11,993/1,973, Adipose 007/9950 0/ Adrenal cortex 55/6221 Adrenal medulla 0/297 Bone 35/ /40, Bone marrow 50/14,453 14/20, Brain 406/209, /151, Cartilage 20/10, /35, Cerebellum 36/4308 0/0 Cerebrum 277/67,385 Cervix 0/ /39, Colon 20/17, /144, Ear 1/11,925 Embryonic tissue Endocrine 009/ / Esophagus 0/84 14/ Eye 292/74, /43, Gastrointestinal tract 005/655 75/12, Genitourinary 002/ /28, Germ cell 48/46,906 Head and neck 28/42, /57, Heart 145/53,288 Kidney 14/58, /71, Limb Liver 39/56, /71, Lung 106/97, /163, Lymph node 313/78, /46, Lymphoreticular 113/30, /75, Mammary gland 101/39, /79, Muscle 358/67, /35, Nervous 13/11, /56, vary 007/ /80, Pancreas 008/ / Pancreatic islet 85/82,692 0/0 Peripheral nervous system 248/23,111 0/ Pineal gland 39/6136 Pituitary gland 23/12,974 0/ Placenta 240/182, /38, Pooled tissue 137/294,688 20/29, Prostate 67/59, /58, Retina 208/44,602 Salivary gland 0/ /16, Skin 54/41, /120, Soft tissue 005/275 21/ Spleen 15/15,731 Stem cell Stomach 21/17, /112, Synovium 0/ / Testis 56/90, /36, Thymus 002/ / Thyroid 001/ / Uncharacterized tissue 641/161, /37, Uterus 18/31, /124, Vascular 36/25,157 White blood cells Whole body 158/64,813 interpretation of our results, we repeat our evaluation, particularly for Table 2, with the more stringent p = 0.01 (only 1 in 100 statements may be incorrect). It turns out that our statements are affected only moderately, i.e., that our choice of p = 0.05 is indeed suitable for the statements made in the present work. Using Virtual Northern for genes whose expression has been studied in healthy tissue and cancer, one can obtain information as shown in Table 3 for the example of GAPD (glyceraldehyde-3-phosphate dehydrogenase). Table 3 is a part of the data given by Virtual Northern which in its original version gives EST and SAGE data. We exploit only the EST data, since, at the present time, SAGE data do not yet give sufficiently complete information on gene expression in all cancers. The table should be read as follows: The cancers are arranged alphabetically in rows. For example in normal kidney tissue 58,073 partially redundant genes or ESTs have so far been investigated and included in the EST database for expression in normal kidney tissues. In normal kidney tissue, 14 (0.024%) genes or ESTs represent GAPD. This is already more than the expected value for an average gene and indicates the importance of this gene in the normal kidney cell. Even more, 417/71,211 (0.59%) genes or ESTs show expression in corresponding kidney cancer tissue. Since the ratio of percentages 0.59:0.024 = is in favor of the cancer tissue, it is immediately clear that GAPD is overexpressed in kidney cancers. For the classification of cases in which the result is not as clear as for kidney cancer, particularly for rarely investigated cancers, the p value reflects the probability that the classification boverexpressedq may be incorrect. For kidney cancer, the p value is In contrast, for normal tissue of the gastrointestinal tract the result is 5/655 (0.76%), and for cancerous tissue it is 75/12,494 (0.60%). Looking at just these figures, one might conclude that in cancers of the gastrointestinal tract GAPD is underexpressed. However, the statistics is much poorer than in the kidney cancers discussed above. The p value of 0.37 indicates that the probability for an incorrect classification is high. In the evaluation of these data we have accepted overexpression as significant only when the p value is 0.05 or lower. Acknowledgments This work was supported by the German Research Ministry BMBF, Grant 13N8028 (SCREEN). B.A. thanks Dr. Toby Gibson for enabling her to perform this work at the European Molecular Biology Laboratory. that we have a false positive. Thus, if not mentioned otherwise, the following discussions are made on the basis of p = However, to give the reader an idea of how the choice of the actual value of p affects the biological References [1] X.L. Zu, M. Guppy, Cancer metabolism: facts, fantasy, and fiction, Biochem. Biophys. Res. Commun. 313 (2004)

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