Clinical impact of the Warburg effect in gastrointestinal cancer (Review)

  • Authors:
    • Hiroshi Sawayama
    • Takatsugu Ishimoto
    • Hidetaka Sugihara
    • Nobutomo Miyanari
    • Yuji Miyamoto
    • Yoshifumi Baba
    • Naoya Yoshida
    • Hideo Baba
  • View Affiliations

  • Published online on: July 25, 2014     https://doi.org/10.3892/ijo.2014.2563
  • Pages: 1345-1354
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Abstract

Cancer cells exhibit altered glucose metabolism, termed the Warburg effect, which is described by the increased uptake of glucose and the conversion of glucose to lactate in cancer cells under adequate oxygen tension. Recent genetic and metabolic analyses have provided insights into the molecular mechanisms of genes that are involved in the Warburg effect and tumorigenesis. The aim of this review was to discuss significant molecular insights into clinical impacts of the Warburg effect such as oncogenic alterations and overexpression of transcriptional factors (c-Myc and hypoxia-inducible factor), metabolite transporters (glucose transporters) and glycolytic enzymes (hexokinases 2, pyruvate kinase M2, pyruvate dehydrogenase kinase, isozyme 1, lactate dehydrogenase A). Overexpression of transcriptional factors, metabolite transporters and glycolytic enzymes was associated with poor prognosis and may be associated with chemoradiotherapy resistance in multiple gastrointestinal cancer cell types. Novel small molecules targeting these enzymes or transporters exert anti-proliferative effects. Glycolytic enzymes and metabolite transporters may be significant biomarkers for predicting cancer prognosis and may be therapeutic targets in gastrointestinal cancer.

1. Introduction

Cancers exhibit altered glucose metabolism, defined as the Warburg effect (1), which is characterized by an increased uptake of glucose (2) and the conversion of glucose to lactate in cancer cells, rather than catabolizing glucose via the TCA cycle under adequate oxygen tension (3). While the electron transfer system generates 36 ATP molecules per glucose molecule across the mitochondrial inner membrane, glycolysis metabolizes glucose to pyruvate in the cytoplasm to produce a net 2 ATP molecules from each glucose. The Warburg effect represents high levels of glycolysis and thus enables the clinical application of metabolic imaging, such as 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), which is a non-invasive imaging technique that allows quantification of tumor activity on the basis of altered tissue glucose metabolism (4). Small molecule inhibitors targeting the enzymes that function in the Warburg effect have been identified and pursued in preclinical studies.

The direct mechanistic link between an activated oncogene and altered glucose metabolism is regulated by phosphoinositide 3-kinase (PI3K) (5), Akt (6), p53 (7,8), AMP-activated protein kinase (AMPK) (9,10), c-Myc and hypoxia-inducible factor (HIF). c-Myc and HIF1A transcription factors target many of the same glycolytic enzyme genes, including hexokinase 2 (HK2), pyruvate kinase type M2 (PKM2), lactate dehydrogenase A (LDHA), and pyruvate dehydrogenase kinase, isozyme 1 (PDK1). Recent investigations using genetic and metabolic analyses have provided insights into the molecular mechanisms of these genes that contribute to the Warburg effect and tumorigenesis (Fig. 1).

In this review, significant molecular insights into clinical impacts of the Warburg effect, such as oncogenic alterations and overexpression of glycolytic enzymes and metabolite transporters, will be discussed.

2. HIF-1A and c-Myc transcription factors and the Warburg effect

HIF-1A and c-Myc cooperatively induce a transcriptional program for glycolysis. HIF plays a crucial role in cellular adaptation to hypoxia and regulates the expression of genes responsible for glucose metabolism, angiogenesis, and cell survival (11). Cellular HIF levels are regulated by both an oxygen-dependent pathway and an oxygen-independent pathway. HIF contains two key regulatory subunits, HIF1A and endothelial PAS domain protein 1 (EPAS1; HIF-2), and the genes encoding these proteins are overexpressed in human cancers (12,13). Many studies have assessed the significance of HIF-1A positive expression in the prediction of clinical outcome of gastrointestinal cancer. HIF-1A expression is associated with poor prognosis in esophageal squamous cell carcinoma (ESCC) (14,15), gastric cancer (16,17), colorectal cancer (CRC) (18) and hepatocellular carcinoma (HCC) (19). Low expression of HIF1A may be associated with a favorable effect of 5-FU-based adjuvant chemotherapy in gastric cancer patients (20,21). HIF-2A is associated with poor survival in gastric cancer patients (22) but not CRC patients (18,23).

The c-Myc oncogene, a member of the MYC family, encodes the transcription factor c-Myc and is upregulated in many human cancers, linking altered cellular metabolism to tumorigenesis (24). MYC gene expressions are often elevated or deregulated in human neoplasms, and c-Myc seems to be at the crossroads of several important pathways and processes involved in carcinogenesis. MYC deregulation due to gene amplification (25), chromosomal translocation or insertion (26), mutations (27), and epigenetic modifications (28) has been reported in different types of cancers. The number of studies of MYC expression as detected by immunohistochemistry (IHC) is less than that of HIF1A. c-Myc overexpression and promoter hypomethylation may have a role in the gastric carcinogenesis process and c-Myc deregulation was associated mainly with poor prognosis (29). c-Myc expression detected by IHC was associated with poor prognosis in pancreatic cancer (30), but its expression was not associated with poor prognosis in CRC patients (18,23) (Table I).

Table I

Impact of HIF and MYC on cancer prognosis and correlation with clinicopathological features.

Table I

Impact of HIF and MYC on cancer prognosis and correlation with clinicopathological features.

TotalPrognosis


OrganN%Cut-offsExpression correlated with: (condition)UnivariateMultivariate(Ref.)
HIF-1A
 ESCC1,261Depth of invasion, N+, stage, VEGF (meta-analysis-2011)NANA(16)
 ESCC21568Scores 3–4VEGFDFS: poor
OS: NS
DFS: NS
OS: NS
(14)
 ESCC9668Score 4–6N+ (T1b patients)DSS: poor
DFS: poor
Poor(15)
 GCNA (meta-analysis 2003–2012)DFS: NS
OS: poor
NA(16)
 GC1103Differentiation, T-stage
N+, ly+, v+, stage (meta-analysis 2003–2013)
OS: poorNA(17)
 GC21639>10% HIF1A+-p53+ cases undifferentiated, ly+, N+OS: poorOS: poor(84)
 GC19352N+DFS: NS
OS: NS
DFS: poor
OS: poor
(45)
 GC12866>5%Histology, depth of invasion
VEGF expression, MVD
DFS: poor
OS: poor
DFS: poor
OS: poor
(85)
 GC6458No correlation [adjuvant CT S-1 (77%)]DFS: poor
DSS: poor
DFS: poor
DSS: NS
(21)
 GC4457>10%No correlation (adjuvant CT 5-FU based)DSS: poor
DFS: poor
NA(20)
 CRC73119>50%COX-2, CIMP-high
LINE1 hypomethylation
CSS: poor
OS: poor
CSS: poor
OS: poor
(18)
 RC9054N+, v+, stageDFS: poor
CSS: poor
OS: poor(23)
 RC9255Scaling systempT4, N+, v+ (T3,4/N+/−)DFS: poor
OS: poor
DFS: poor
OS: poor
(86)
 HCC953Tumor grade, N+, v+ (meta-analysis-2013)DFS: poor
OS: poor
(19)
 HCC110Male, LC, COX-2, PDGFRA
MMP7, MMP9, MYC
DFS: poor
OS: poor
DFS: poor
OS: poor
(87)
 HCC20063Intrahepatic metastasisDFS: poor
OS: poor
DFS: poor
OS: poor
(88)
 PC5066>5%VEGFDFS: NS
OS: NS
NA(89)
HIF-2A
 GC8038>Score 0Diffuse typeDFS: poor
OS: poor
CSS: NS
OS: NS
(22)
 CRC73119>50%Low tumor grade, male, BMI<30CSS: NS
OS: NS
CSS: NS
OS: NS
(18)
 RC9064No correlationDFS: NS
CSS: NS
(23)
MYC
 GC12577>10%Intestinal-type, late-onset deeper tumor extension, M+NANA(29)
 PC7052Score 5–9Perineural invasion, stageOS: poorOS: poor(30)
 CRC73119>50%Low tumor grade male, BMI<30CSS: NS
OS: NS
CSS: NS
OS: NS
(18)
 RC9064No correlationDFS: NS
CSS: NS
(23)

[i] ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; CRC, colorectal cancer; RC, rectal cancer; HCC, hepatocellular carcinoma; PC, pancreatic cancer; NA, no assessment; NS, not significant.

3. Glucose transporters (Gluts)

Glut1 is composed of 492 amino acid residues and possesses a single site of N-linked glycosylation at N45 (31). Multiple glucose transporter-like proteins have been identified and characterized (32) with sequence similarity to Glut1, and these genes appear to belong to the family of solute carriers 2A (SLC2A, protein symbol Glut). The 14 Gluts are categorized into three classes based on sequence similarity: Class 1 (Gluts 1–4 and 14), Class 2 (Gluts 5, 7, 9 and 11), and Class 3 (Gluts 6, 8, 10, 12, and HMIT) (32). Glut families were evaluated with the GEO data set in silico (http://www.ncbi.nlm.nih.gov/gds/). Glut1 mRNA levels were remarkably upregulated in tumor lesions compared with normal lesions in CRC (GDS 4382), ESCC (GDS 3838) and pancreatic cancer (GDS 4336) (Table II). Several studies have been published on Glut family members, especially Glut3 (3335), but Glut1 has been the main focus of investigation. A previous study evaluating Glut1 by IHC in tissue microarray slides comprising 1,955 samples detected Glut1 positivity in 47% prostate adenocarcinomas, 29% thyroid cancer, 10% gastric cancer, 5% breast adenocarcinomas, 36% head and neck SCC, 42% uterine cervix SCC, 18.6% glioblastomas and 9.4% retinoblastomas (36).

Table II

Overexpression of metabolite transporters and glycolytic enzymes in the Warburg effect.

Table II

Overexpression of metabolite transporters and glycolytic enzymes in the Warburg effect.

Colorectal cancer (N=17)
GDS4382
ESCC (N=17)
GDS3838
Panctreatic cancer (N=45)
GDS4336



T/N ratio95% CIP-valueT/N ratio95% CIP-valueT/N ratio95% CIP-value
Glut11.90(1.16–3.09)0.012.44(1.78–3.34)<0.0013.58(2.74–4.67)<0.001
Glut20.92(0.87–0.98)0.010.97(0.93–1.01)NS0.60(0.46–0.80)<0.001
Glut31.55(0.72–3.31)NS1.96(1.23–3.13)0.011.54(1.18–2.01)<0.001
Glut41.18(0.99–1.40)NS0.97(0.83–1.13)NS0.89(0.83–0.97)0.01
Glut50.52(0.37–0.72)<0.0011.08(0.80–1.44)NS1.10(0.91–1.31)NS
Glut60.84(0.71–1.00)0.051.31(1.12–1.54)<0.0010.95(0.88–1.02)NS
Glut80.59(0.10–3.60)NS1.17(1.00–1.36)0.040.93(0.88–0.98)0.01
Glut91.13(1.01–1.26)0.031.21(1.01–1.45)0.041.07(0.98–1.17)NS
Glut100.65(0.35–1.19)NS0.89(0.66–1.18)NS1.13(1.02–1.25)0.02
Glut111.33(0.96–1.85)NS0.96(0.88–1.05)NS0.85(0.78–0.93)<0.001
Glut141.72(1.08–2.72)0.031.45(1.04–2.02)0.031.10(0.98–1.23)NS
HK20.46(0.26–0.79)0.0091.53(1.16–2.03)0.0052.55(1.97–3.30)<0.001
LDHA1.05(0.92–1.19)NS0.92(0.78–1.10)NS1.89(1.57–2.28)<0.001
PMK20.80(0.65–0.98)0.0331.41(1.02–1.95)0.042.03(1.72–2.40)<0.001
PDK11.18(0.85–1.63)NS1.37(1.08–1.74)0.0121.38(1.18–1.61)<0.001

Glut1 is transcriptionally regulated by c-Myc (24) and HIF1A (37). A recent study demonstrated that Glut1 was one of three genes consistently upregulated in cells with KRAS or BRAF mutations (38). Glut1 expression in CRC cells was positively correlated with FDG accumulation and KRAS/BRAF mutation (39). EGFR and ERK1/2 correlate with levels of PKM2 Ser 37 phosphorylation, and nuclear PKM2 induces c-Myc expression, resulting in the upregulation of Glut1 (40). In a recent study using xenografts, overexpression of Glut1 in a mammary tumor cell lines with low levels of endogenous Glut1 results from both a decrease in apoptosis and an increase in proliferation (41).

Glut1 expression is generally absent in normal tissue, but in multiple gastrointestinal cancer cell types, Glut1 expression is remarkably enhanced. Glut1 positivity is associated with poor prognosis in diverse gastrointestinal cancers, ESCC (15,42,43), gastric cancer (44,45), CRC (46,47), pancreatic cancer, HCC (48), and gallbladder cancer (49,50) (Table III).

Table III

Impact of Glut1 and glycolytic enzymes on prognosis and correlation with clinicopathological features.

Table III

Impact of Glut1 and glycolytic enzymes on prognosis and correlation with clinicopathological features.

TotalPrognosis


OrganN%Cut-offsExpression correlated with: (condition)UnivariateMultivariate(Ref.)
Glut1
 ESCC14543>50%pT3, v+ MVD (no preoperative treatment)DFS: poor
CSS: poor
DFS: NS
CCS: NS
(42)
 ESCC6348>30%No correlation (curative operation)OS: poorOS: NS(43)
 ESCC9671Score 4–6N+ (T1b patients)DFS: poor
CSS: poor
NS(15)
 GC61730>1%pap>por or tub, T-stage
N+, ly+, v+, H+, stage
OS: poorOS: poor(44)
 GC15224>30%T2-T4, N+, diffuse typeDFS: NS
OS: NS
DFS: NS
OS: NS
(70)
 GC19343Age >65, T2-T4, N+, stage, intestinal typeOS: poorOS: NS(45)
 CRC163Poorly differentied higher in stage III + IVOS: poorOS: poor(90)
 CRC11218>50%N+,CSS: poorCSS: poor(46)
 RC4648>10%No correlationDFS: p=0.066NA(47)
 PC9475>50%Historogical grade, MIB1 (ductal AC)OS: poorOS: poor(91)
 HCC6337Scoring
≥Score 1
SUV, TNR, Ki67LIDFS: poor
OS: poor
NA(48)
 GB5634>50%Perinecrotic areasOS: poorNA(49)
 GB7152Histologic tumor type tumor stageOS: poor(50)
HK2
 GC25717>30%No correlationDFS: NS
OS: NS
DFS: NS
OS: NS
(92)
 GC1525>30%No correlationDFS: NS
OS: NS
DFS: NS
OS: NS
(70)
 GC18821Size, lower differentiation, stage, HIF1AOS: poorOS: poor(93)
 HCC15715High mod.Moderately and poorly, advanced stageOS: poorOS: poor(58)
 HCC9756No correlationOS: poorNA(94)
 HCC3181Moderately and poorly differentiatedOS: NSOS: NS(59)
PKM2
 ESCC18080IRS strong mod.Differntiation poorly tumor size, stageOS: poorOS: poor(95)
 GC36839>25%Age, t-stage, well differentiatiedOS: NSOS: NS(96)
 GC7918>25%Subgroup analysis above study (signet cell)OS: poorOS: poor(96)
 GB8056>25%Differntiation poorly, tumor size, stage, N+OS: poorOS: poor(97)
PDK-1
 GC15212>30%T3-T4, N+, tumor size
HIF-1A
DFS: poor
OS: poor
DFS: poor
OS: poor
(70)
 CC74-Blot densityExpression PDK1 deceased in cancer tissueNANA(70)
PDK-3
 CC20686 Stain+Stage, HIF-1ADFS: poor
OS: poor
NA(71)
LDH-5
 GC9462Advanced tumor, v+
HIF-1A, VEGF, COC-2
DFS: poor
OS: poor
NA(98)
 CC12877Poor differentiation
HIF1A, pKDR
DFS: poor
OS: poor
NA(78)

[i] ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; CRC, colorectal cancer; RC, rectal cancer; HCC, hepatocellular carcinoma; PC, pancreatic cancer; GB, gallbladder cancer; MVD, microvessel density; ductal AC, ductal adenocarcinoma; NA, no assessment; NS, not significant.

Glut1 expression has the potential to serve as a biomarker for cancer. Anticancer therapies, such as radiation and several chemotherapeutic drugs, induce oxidative stress in targeted cells. Reactive oxygen species (ROS) are required for the fixation of radiation-induced DNA damage (51). Therefore, an accumulation of antioxidants (e.g., lactate) may induce or enhance resistance to radiation and may cause chemoresistance (52). Glut1 positivity was associated with tumor regression grade (TRG) and may be a useful predictive marker of response to chemoradiotherapy in rectal cancer (47,53).

Phloretin, a natural product found in apples and pears with Glut inhibitory activity, exerts antitumor effects in HCC and color cancer cell lines (54,55). The WZB117 small molecule inhibitor of Glut1 was effective in inhibiting cancer cell growth both in vitro and in vivo (56) (Table IV).

Table IV

Anti-proliferative effect of inhibitors of metabolite transporters and glycolytic enzymes.

Table IV

Anti-proliferative effect of inhibitors of metabolite transporters and glycolytic enzymes.

TargetInhibitorCancer type (cell lines)Dose in vitroDose in vivoCombination or drug resistance(Ref.)
Glut1WZB117LC (A549)10 μM10 mg/kg (i.p.) dailyNA(56)
PhloretinCRC (SW620)50 μMNADNR(54)
Glut2PhloretinHCC (HepG2)200 μM10 mg/kg (i.p.)
3 times per week
DNR(55)
HK-23-BrPAHCC (Huh-7)100 μM1 mg/kg (i.p.)PDI(99)
3-BrPACRC (HCT116, HT29)30 μMNAOx-resistant cells(100)
PKM-2Compound 3LC (H1299), hematopoetic (FL5.12)30 μMNAGefitinib(101)
PDKDCAHCC (Huh-7)30 mM100 mg per kg bw per daySorafinib-resistant cells(74)
DCAGC (MKN45, AGS)20 mMNA5-FU(70)
DCACRC (SW620, LoVo, LS174t, HT29)10 mMNA5-FU(73)
BX-320CRC (HCT116)
PC (MiaPaCa)
0.28 μM
0.33 μM
NANA(102)
LDHAFX11Lymphoma (P493)9 μM42 μg (i.p.) dailyFK866(81)
OxmateCRC (CT26)9 μMNAPhenformin(82)

[i] DCA, dichloroacetate; 3-BrPA, 3-bromopyruvate; DNR, daunorubicin; PDI, protein disulfide isomerase; Ox, oxaliplatin; LC, lung cancer; CRC, colorectal cancer; HCC, hepatocellular carcinoma; GC, gastric cancer; NA, no assessment; NS, not significant.

4. Glycolytic enzymes (HK2, PKM2, PDK1 and LDHA)

Hexokinases catalyze the phosphorylation of glucose to glucose-6-phosphate. This is the first and rate-limiting step in glucose metabolism. HK2 is one of four members of the hexokinase family. The four isoenzymes (HK1, HK2, HK3, and glucokinase) are structurally similar, but only HK1 and HK2 are functionally similar. HK2, but not HK1, is overexpressed in several cancer types compared with normal tissue. HK2 localizes to the outer membrane of the mitochondria and is the major hexokinase isoform expressed in cancer cells (57). High expression of HK2 confers a poor prognosis in HCC and gastric cancer (Table II), and HK2 positivity was associated with poor differentiation and advanced stage in HCC (58,59). Tumor differentiation in HCC correlated with FDG uptake (60), and the cellular retention of FDG may be mediated by HK2 (58).

The widely used 3-bromopyruvate (3-BrPA) (61) depletes cellular ATP. A previous study showed that 3-BrPA inhibits HK2 expression and exhibits anti-proliferative effects combined with daunorubicin in CRC cell lines (54) and combined with protein disulfide isomerase in HCC cell lines in vivo (55).

Pyruvate kinase (PK) is a glycolytic enzyme that catalyzes a reaction generating pyruvate and ATP from phosphoenolpyruvate (PEP) and ADP. Four isoforms of PK (L, R, M1, and M2) have been identified in mammals. Splicing of PKM is controlled by splicing repressors, and the expression of the repressors is upregulated by c-Myc oncoprotein (62,63). M2 is expressed in embryonic cells, adult stem cells, and cancer cells and is necessary for aerobic glycolysis. This metabolic phenotype provides a selective growth advantage for tumor cells in vivo (64,65). PKM2 expression is associated with poor prognosis in ESCC, gallbladder cancer and signet ring cell carcinoma of gastric cancer (Table III). Small molecule inhibitors that selectively target PKM2 have been identified, suggesting that inhibition of PKM2 could be synergistic with other targeted therapies, including gefitinib. However, small molecule activation of PKM2 that promotes PKM2 tetramer formation interferes with anabolic metabolism and suppresses tumorigenesis (66). Mutation of the ERK-phosphorylation site S37 in PKM2 blocked translocation of PKM2 to the nucleus (40), suggesting that PKM2 moves into the nucleus as a monomer. Tumor cells have multiple ways to regulate PKM2 that are favorable to cell growth and survival, including PKM2 expression, localization, post-translational modification, and allosteric regulation. PKM2 also regulates non-metabolic functions as a transcriptional coactivator and protein kinase. PKM2 is considered an attractive target for cancer treatment (67). Further studies are needed before inhibitors and activators of PKM2 can be used as therapeutic interventions (68).

PDK regulates the mitochondrial gatekeeper pyruvate dehydrogenase (PDH), which links glycolysis to the TCA cycle by reversible phosphorylation. Phosphorylation of PDH by PDK inhibits the action of PDH and halts pyruvate use in the TCA cycle (69). Four PDK isoforms have been verified in human tissue, and the expressions of the isoforms are organ specific. PDK-1 positivity was associated with poor prognosis in gastric cancer (70), but expression of PDK-1 was decreased in colon cancer compared with normal tissue. PDK-3 expression was detected in colon cancer, and PDK-3 positivity was associated with poor prognosis (71). Several studies reported the relationship between PDK positivity and prognosis in gastrointestinal cancer, but the clinical significance of PDK expression has remained unclear. Many small molecule PDK-1 inhibitors have been identified (72). DCA, a PDK-1 inhibitor, reduced lactate production and increased responsiveness to 5-FU in MKN45 cells (70) and CRC cell lines (73). DCA treatment exerts anti-proliferative effects and sorafenib resistance in HCC cell lines in vivo (74).

Lactate dehydrogenase is a tetrameric enzyme comprising two major subunits, A and/or B, resulting in five isozymes (A4, A3B1, A2B2, A1B3 and B4) that can catalyze the forward and backward conversion of pyruvate to lactate. LDHA (LDH-5, MLDH, or A4), which is the predominant form in skeletal muscle, kinetically favors the conversion of pyruvate to lactate, controlling the conversion of pyruvate to lactate in the cellular glycolytic process (75). Many human cancers have higher LDHA levels than normal tissues (76). LDHA is specifically phosphorylated at Y10 in various cancer cell lines, head and neck SCC, lung cancer, breast cancer, and prostate cancer cells and by diverse oncogenic tyrosine kinases, including FGFR1, ABL, JAK2, and FLT3 (77).

LDHA reduction can suppress the tumorigenicity of intestinal- type gastric cancer (ITGC) cells, colon cancer (78) and HCC (79). A previous study of 661 ITGC specimens showed that low LDHA expression exhibited better overall survival than high LDHA expression (80).

Similar to small interfering RNA (siRNA) reduction of LDHA expression, the FX11 small molecule inhibitor for LDHA could increase cellular oxygen consumption, increase ROS production, and induce cell death that could be partially rescued by the antioxidant NAC in a lymphoma cell line (81). Oxmate, a lactate dehydrogenase inhibitor, combined with phenformin exhibited cytotoxic effects in diverse cancer cell lines, including colon cancer (82).

5. Conclusions and future perspectives

This review describes the significance of protein expression of the transcriptional factors, glycolytic enzymes and metabolite transporters involved in the Warburg effect as potential biomarkers. The functional and therapeutic importance of the Warburg effect is increasingly recognized, and glycolysis has become a target of anticancer strategies. Novel small molecule inhibitors targeting enzymes that function in the Warburg effect have been developed and anti-proliferative effects on diverse cancer cells have been demonstrated. The gene expressions of molecular factors involved in the Warburg effect are associated with poor prognosis and may be associated with chemoradiotherapy resistance in gastrointestinal cancers. Novel small molecules exert anti-proliferative effects and may reduce chemoradiotherapy resistance in gastrointestinal cancer, breast cancer (83) and lung cancer (56) (Table IV).

Future studies should examine whether inhibitors of glycolytic enzymes and metabolite transporters are useful in gastrointestinal cancer and evaluate adverse effect and feasibility for clinical practice. Furthermore, validation of imaging techniques, which establish glucose metabolism and predict response to drugs, is required for optimal patient selection.

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October 2014
Volume 45 Issue 4

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APA
Sawayama, H., Ishimoto, T., Sugihara, H., Miyanari, N., Miyamoto, Y., Baba, Y. ... Baba, H. (2014). Clinical impact of the Warburg effect in gastrointestinal cancer (Review). International Journal of Oncology, 45, 1345-1354. https://doi.org/10.3892/ijo.2014.2563
MLA
Sawayama, H., Ishimoto, T., Sugihara, H., Miyanari, N., Miyamoto, Y., Baba, Y., Yoshida, N., Baba, H."Clinical impact of the Warburg effect in gastrointestinal cancer (Review)". International Journal of Oncology 45.4 (2014): 1345-1354.
Chicago
Sawayama, H., Ishimoto, T., Sugihara, H., Miyanari, N., Miyamoto, Y., Baba, Y., Yoshida, N., Baba, H."Clinical impact of the Warburg effect in gastrointestinal cancer (Review)". International Journal of Oncology 45, no. 4 (2014): 1345-1354. https://doi.org/10.3892/ijo.2014.2563