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Expression of TIGAR and its correlation with clinicopathology, prognosis, and 18F-FDG PET/CT parameters in patients with resectable pancreatic ductal adenocarcinoma

Song, Yanga,,b; Wang, Peng-Yuana; Zheng, Yanga; Liu, Changc; Wang, Xiao-Minga

Author Information
Nuclear Medicine Communications: May 2021 - Volume 42 - Issue 5 - p 528-534
doi: 10.1097/MNM.0000000000001366
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The incidence of pancreatic cancer is close to its mortality rate; thus, it is currently the fourth leading cause of cancer-related death in the world [1]. Tumor cell growth is regulated by the dynamic balance between production of reactive oxygen species (ROS) and antioxidants, needed to maintain redox homeostasis, which plays a role in cellular proliferation and differentiation, and cell signal transduction [2]. Genetic changes in pancreatic cancer cells, chemoradiotherapy, and other treatments can lead to the accumulation of ROS causing oxidative stress, and impaired cellular function and even death of tumor cells [3]. In these cases, glucose metabolism is reprogrammed to produce NADP (NADPH), a reducing substance, to maintain redox homeostasis and cell survival.

Tumor cell glucose metabolism is characterized by the fact that tumor cells prefer glycolysis for energy creation even when oxygen is sufficient, a phenomenon known as the Warburg effect [4]. TP53-inducible glycolysis and apoptosis regulator (TIGAR) is a target gene of the tumor suppressor protein p53, which can hamper glycolysis and shift the metabolic flow from glycolysis to the pentose phosphate pathway (PPP), producing NADPH, which neutralizes ROS creating balance in intracellular reduction-oxidation (redox) homeostasis [5] and promoting tumor progression. It has been found that TIGAR expression is increased significantly in some cancers [6–9]; however, the effect of TIGAR expression on the prognosis of pancreatic ductal adenocarcinoma (PDAC) is unknown.

PET/computed tomography (CT) is a noninvasive image inspection technique in vivo. The radiotracer participates in the biochemical reactions of the body, inspected by PET/CT to show molecular biology mechanisms. At present, the methods of PET/CT imaging to detect redox reactions of tumors directly using different tracers are still limited to preclinical studies. One such study demonstrated that the amount of intracellular retention of (S)-4-(3-[18F]fluoropropyl)-L-glutamic acid [(18F)FSPG] is related to the cellular content of cystine, an important precursor in the process of glutathione biosynthesis [10]. In another study, 5-[18F]fluoroaminosuberic acid [(18F)FASu] was used to reflect redox homeostasis through the cystine transporter, system xc− [11]. 18F-labeled fluoro-2-deoxyglucose (18F-FDG) PET/CT, which is based on the principle that tumor cells need to take up more glucose to meet energy demands, is widely used in clinical practice in the diagnosis, staging, treatment, and prognosis of pancreatic cancer [12]. Can we predict the redox homeostasis of PDAC based on the 18F-FDG PET/CT and TIGAR expression since the glucose metabolism of tumor cells is closely related to redox homeostasis?

This study collected the clinical data on the overall survival (OS) and preoperative 18F-FDG PET/CT-related parameters and detected the expression of TIGAR using an immunohistochemical method in patients with PDAC. We then retrospectively analyzed the correlation between TIGAR expression and clinical data and the value of TIGAR expression in prognosis prediction. The relationship between TIGAR expression and 18F-FDG PET/CT parameters in patients with PDAC was explored to evaluate the value of 18F-FDG PET/CT in predicting redox homeostasis in PDAC tissues.

Materials and methods


This study analyzed data from patients diagnosed with PDAC in our hospital from January 2009 to December 2016, with a December 2018 follow-up time. The interval between the date of surgery and the date of death or last follow-up was defined as OS, which was obtained through telephone follow-up or medical records. The inclusion criteria of patients were as follows: (1) the patients did not accept chemotherapy, radiotherapy, or other anti-tumor treatment before surgery; (2) the patients did the preoperative baseline 18F-FDG PET/CT examination; (3) postoperative histopathology confirmed PDAC; (4) no other primary malignant tumors were present; (5) complete clinical data were obtained. The patients’ age, sex, tumor location, the maximum tumor diameter, tumor differentiation, serum CA19-9 level, lymph node classification, and tumor specimen were collected. The ethics committee of Shengjing Hospital of China Medical University (Shenyang, China) approved the study. The baseline characteristics of the patients are shown in Table 1.

Table 1 - Baseline characteristics of patients with pancreatic ductal adenocarcinoma (N = 23)
Characteristics No. of cases (%)
 Men 13 (56.5%)
 Women 10 (43.7%)
Age (years)
 ≥60 12 (52.2%)
 <60 11 (47.8%)
 Head 14 (60.9%)
 Body/tail 9 (39.1%)
Maximum tumor diameter (cm)
 <4 16 (69.6%)
 ≥4 7 (30.4%)
CA19-9 (U/ml)
 <37 7 (30.4%)
 ≥37 16 (69.6%)
 Well 9 (39.1%)
 Moderate/poor 14 (60.9%)
LN metastasis
 pN0 15 (65.2%)
 pN1 8 (34.8%)
LN, lymph node.

Computed tomography and PET acquisition and image reconstruction

Patients fasted for at least 6 h before the CT and PET scans. The patient blood glucose levels were controlled below 110 mg/ml. 18F-FDG (MiniTrace II and TracerLab FX-FDG, purity > 99%; GE Healthcare, Waukesha, Wisconsin, USA) was administered at a dose of 3.7 MBq/kg. The patients underwent the PET/CT (Discovery PET/CT 690; GE Healthcare, USA) scan after 60 min of quiet rest. A low-dose attenuation-corrected CT scan (120 kV, 80 mA) was performed (thickness 3.75 mm), ranging from the base of the skull to the middle of the thigh. A three-dimensional PET scan was then performed on a total of seven beds at a speed of 1.5 min/bed. Using 24 subsets and two iterations, the PET images were reconstructed by the ordered-subsets expectation maximization (OSEM) algorithm with attenuation correction. Time-of-flight and point-spread function techniques were used in the reconstruction.

PET/computed tomography parameter measurement

All PET/CT images were analyzed using a GE Advantage Workstation 4.5 (GE Healthcare) and post-processed using PET volume computer-assisted reading (PET-VCAR) software. Two physicians with more than 5 years of experience in the department of nuclear medicine, who did not have access to the patients’ clinical information, evaluated the PET/CT images and measured the semiquantitative parameters independently. The volume of interest (VOI) around the pancreatic tumor was automatically delineated by the software according to the isometric method (Fig. 1). The standard uptake value (SUV) is the product of the radiotracer activity concentration (Bq/ml) times the patient’s body weight in kilograms (kg) divided by the injection dose (Bq). The SUVmax refers to the maximum value of the SUV within the area of the VOI. The SUVpeak is the average value of the SUV within the 1.2-cm diameter spherical area around the SUVmax. The SUVmean is defined as the average value of the SUV within the VOI. Metabolic tumor volume (MTV) was the high metabolic volume of SUV greater than the defined threshold. The threshold was defined by a fixed percentage of SUVmax and an iterative adaptive algorithm. For the fixed percentage SUVmax method, the SUVmax threshold was used in increments of 10% from 20 to 60% (MTV 20%–MTV 60%). PET-VCAR was used to segment and measure MTV automatically using the iterative adaptive algorithm. The tumor segmentation threshold for primary PDAC was 40%. Other organ metastases were not included in the MTV calculation. Total lesion glycolysis (TLG) is the product of the MTV and the SUVmean.

Fig. 1:
18F-FDG PET/CT images and immunohistochemistry for a 53-year-old female patient with ductal adenocarcinoma of the head of the pancreas. The volume of interest (VOI) around the tumor was created using the isocontour method. The PET, CT, and infused PET/CT images are shown in the coronal (a–c), sagittal (d–f), and axial (g–i) planes. Immunohistochemistry indicated high TIGAR expression (brown-yellow particles) in the nucleus and cytoplasm of tumor cells in PDAC tissue (j). Original magnification: 100×. CT, computed tomography; 18F-FDG, 18F-labeled fluoro-2-deoxyglucose; TIGAR, TP53-inducible glycolysis and apoptosis regulator.


Immunohistochemical assays of TIGAR were performed using the self-potential (S-P) method. A rabbit TIGAR mAb and S-P immunohistochemical kits were provided by Abcam Biotech Company (Cambridge, UK) and Fuzhou Maixin Biological Technology Ltd., respectively. PDAC tissues were antigen repaired with 10 mmol/l of citrate buffer after being embedded into paraffin sections, dewaxed, and hydrated. After being inactivated the endogenous peroxidase and enclosed nonspecific antigen, the tissue was applied by TIGAR (1:200 dilution) primary antibody for wet incubation at 4°C. The secondary antibody was applied to the tumor tissue after washing with phosphate-buffered saline. The tissue then went through the processes of color development, dehydration, and fixation. Each tissue specimen was observed under an optical microscope at low power (10×) and at high powers (20× and 40×). The results were analyzed independently by two experienced pathologists. The immunohistochemical staining results take the positive staining intensity and the proportion tumor cells into consideration. Positive staining was defined as the brown granules in the cell cytoplasm. Staining cell intensity was graded as follows: 0 = negative; 1 = weak positive; 2 = moderately positive; 3 = positive. The proportion of tumor cells was defined as follows: less than 5%, 0; 5–25%, 1; 26–50%, 2; 51–75%, 3; and greater than 75%, 4. The score was determined by the product of staining cell intensity and the proportion of tumor cells. Dyeing intensity 0–7 was defined as low expression, while dyeing intensity 8–12 was defined as high expression.

Gene Expression Profiling Interactive Analysis data analysis

The Gene Expression Profiling Interactive Analysis (GEPIA) ( database contains data from 9736 tumor samples and 8587 normal samples retrieved from the UCSC Xena server. The database provides key interactive and custom functions including differential expression analysis, correlation analysis, contour mapping, patient survival analysis, dimensional reduction analysis, and genetic testing [13]. The GEPIA database was used to explore the expression of the TIGAR gene in PDAC. When the expression of TIGAR in tumor tissues was significantly higher than that in normal tissues (P < 0.05), it was considered a differential expression.

Statistical analysis

SPSS 19.0 (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses. Measurement data were expressed as mean ± SD or percentage. The relationship between TIGAR expression and clinical pathology data were analyzed by an independent sample t-test. Spearman’s rank correlation coefficient was used to evaluate the correlation between 18F-FDG PET/CT-related parameters and TIGAR expression. The log-rank test was used to evaluate the correlation between TIGAR expression and OS. A Kaplan–Meier method was used to draw the survival curve and a Cox risk ratio model was used for multivariate analysis. A P-value less than 0.05 was considered statistically significant.


  • 1. TIGAR is highly expressed in 23 patients with PDAC.

A significant increase in TIGAR expression was found in patients with PDAC from the GEPIA database (P < 0.05) (Fig. 2). Immunohistochemical results indicated that TIGAR expression was low in eight patients (34.8%) and high in 15 patients (65.2%). Brown granules were distributed within the nucleus and cytoplasm of the tumor cells, indicating positive TIGAR expression.

Fig. 2:
The expression of the TIGAR gene in pancreatic ductal adenocarcinoma samples and paired normal tissues from the GEPIA database. The expression of TIGAR was significantly higher in pancreatic ductal adenocarcinoma tissues than in the paired normal tissues (P < 0.05) [num (T) = 179; num (N) = 171]. GEPIA, Gene Expression Profiling Interactive Analysis; TIGAR, TP53-inducible glycolysis and apoptosis regulator.
Fig. 3:
Kaplan–Meier curves of overall survival (OS) in patients with pancreatic ductal adenocarcinoma. TIGAR expression can predict the prognosis of pancreatic ductal adenocarcinoma patients. The median OS of patients with high and low expression of TIGAR is 11.2 months and 35.4 months, respectively (P < 0.01). High expression of TIGAR predicts a poor prognosis in pancreatic ductal adenocarcinoma patients. TIGAR, TP53-inducible glycolysis and apoptosis regulator.
  • 2. Correlations between TIGAR expression and clinicopathological parameters, TIGAR expression, and survival in patients with PDAC.

In this study, there was no significant correlation between TIGAR expression and age, gender, tumor location, maximum tumor diameter, serum CA19-9 level, lymph node metastasis, or tumor differentiation (Table 2). However, the conclusion is not strong enough for the small sample size of only 23 PDAC patients who are enrolled in this research.

Table 2 - Associations between the expression of TIGAR and clinicopathological variables in patients with pancreatic ductal adenocarcinoma
Patient characteristic Low expression
(intensity 0 or 1)
High expression
(intensity 2 or 3)
Gender 0.645
 Men 4 9
 Women 4 6
Age (years) 0.469
 ≥60 5 7
 <60 3 8
Location 0.906
 Head 5 9
 Body/tail 3 6
Maximum tumor diameter (cm) 0.172
 <4 7 9
 ≥4 1 6
CA19-9 (U/ml) 0.591
 <37 3 4
 ≥37 5 11
Differentiation 0.435
 Well 4 5
 Moderate/poor 4 10
LN metastasis 0.842
 No 5 10
 Yes 3 5
TIGAR, TP53-inducible glycolysis and apoptosis regulator.

The age of male patients (range, 52–72 years and average, 61 years) was not significantly different from female patients (range, 52–77 years and average, 64 years). Twenty-one patients (91.3%) died during the follow-up period; no patients withdrew from the study. Univariate analysis showed that a high TIGAR expression was associated with a poor prognosis in patients with PDAC (11.2 vs. 35.4 months OS, P < 0.05), and all univariate predictors of OS were included in the Cox regression analysis (Table 3). The results of the Kaplan–Meier survival curve showed that patients with high TIGAR expression had a poor prognosis (P < 0.05) (Fig. 3).

Table 3 - Univariate and multivariate analysis of overall survival and clinicopathological characteristics in 23 patients with pancreatic ductal adenocarcinoma
Characteristics Median OS (months) (95% CI) Log-rank χ2 P-value Multivariate analysis; P-value
Gender 0.341 0.559
 Men 17.88 (11.65–24.12)
 Women 23.53 (10.46–36.60)
Age (years) 1.285 0.257
 ≥60 23.44 (13.42–33.47)
 <60 16.41 (9.73–23.09)
Location 1.416 0.234
 Head 22.79 (14.04–31.54)
 Body/tail 15.94 (8.28–23.60)
Maximum tumor diameter (cm) 3.082 0.079
 <4 23.23 (14.98–31.48)
 ≥4 13.38 (5.87–20.90)
CA19-9 (U/ml) 0.468 0.494
 <37 15.83 (9.33–22.33)
 ≥37 21.58 (13.42–29.76)
Differentiation 3.617 0.057
 Well 28.96 (16.04–41.88)
 Moderate/poor 15.27 (9.49–21.05)
LN metastasis 0.307 0.580
 No 21.93 (13.01–30.85)
 Yes 18.18 (8.92–27.45)
TIGAR expression 14.39 <0.01 0.033
 Low expression 35.42 (26.38–44.46)
 High expression 11.19 (8.49–13.88)
The P-value of the TIGAR expression is <0.05, which means it was significantly associated with OS.
CI, confidence interval; OS, overall survival; TIGAR, TP53-inducible glycolysis and apoptosis regulator.

  • 3. The Relationship between TIGAR expression and 18F-FDG PET/CT parameters

Although there was no statistical significance between TIGAR expression and SUVmax, SUVmean, SUVpeak, or TLG in patients, the values of these parameters were higher in patients with high TIGAR expression. The value of MTV and TIGAR expression was significantly positively correlated (P < 0.05) (Table 4).

Table 4 - Relationship between expression of TIGAR and 18F-FDG PET/CT parameters in patients with pancreatic ductal adenocarcinoma
TIGAR expression n SUVmax
(mean ± SD)
P-value SUVmean
(mean ± SD)
P-value SUVpeak
(mean ± SD)
P-value MTV
(mean ± SD)
P-value TLG
(mean ± SD)
 High 15 8.17 ± 4.78 0.214 4.52 ± 2.44 0.26 6.62 ± 3.67 0.296 29.20 ± 20.91 0.049 98.19 ± 49.41 0.104
 Low 8 6.34 ± 2.01 3.68 ± 1.03 5.14 ± 1.73 12.66 ± 10.10 61.89 ± 47.31
Expression of TIGAR has significant correlation with MTV (P < 0.05).
CT, computed tomography; 18F-FDG, 18F-labeled fluoro-2-deoxyglucose; OS, overall survival; MTV, metabolic tumor volume; SUV, standard uptake value; TIGAR, TP53-inducible glycolysis and apoptosis regulator; TLG, total lesion glycolysis.

  • 4. Correlations between 18F-FDG PET/CT parameters and prognosis in PDAC patients

Using ROC curve analysis, the optimal cutoff values for SUVmax, SUVmean, SUVpeak, MTV, and TLG were obtained as follows: 11.11, 6.09, 8.47, 40.08 cm3, and 175.25 g, respectively. Univariate analysis showed that high MTV and TLG were associated with poor prognosis (Table 5).

Table 5 - Correlations between 18F-FDG PET/CT parameters and prognosis in pancreatic ductal adenocarcinoma patients
18F-FDG PET/CT parameters OS (median months) P-value
SUVmax 0.39
 >11.11 11.27
 ≤11.11 20.87
SUVmean 0.38
 >6.09 10.98
 ≤6.09 19.67
SUVpeak 0.52
 >8.47 13.45
 ≤8.47 20.91
MTV 0.05
 >40.08 cm3 10.40
 ≤40.08 cm3 22.92
TLG 0.01
 >175.25 g 7.50
 ≤175.25 g 21.41
CT, computed tomography; 18F-FDG, 18F-labeled fluoro-2-deoxyglucose; OS, overall survival; MTV, metabolic tumor volume; SUV, standard uptake value; TIGAR, TP53-inducible glycolysis and apoptosis regulator; TLG, total lesion glycolysis.


P53 is one of the main driving genes of pancreatic cancer [3]. As the downstream gene of p53, TIGAR is involved in the metabolic reprogramming of glucose metabolism in tumor cells. TIGAR has the same domain as 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase, which can hydrolyze fructose-2,6-bisphosphate (Fru-2,6-P2). The reduction of Fru-2,6-P2 weakens the activation of 6-phosphofructo-1-kinase and blocks glycolysis, shifting the metabolic flow from glycolysis to the PPP [14]. The oxidative phase of the PPP produces NADPH, which can act as an antioxidant by neutralizing intracellular ROS, protecting cells against oxidative stress-related death [15] and maintaining redox homeostasis, which is of great significance to tumor development. Perhaps that is why TIGAR expression increases in many tumors. TIGAR expression in patients with PDAC was increased significantly in the GEPIA database (Fig. 2); our results also confirmed this. At the same time, we found that the OS of patients with PDAC who had high TIGAR expression was shorter (Table 3, Fig. 3), and similar results were previously observed in patients with chronic lymphocytic leukemia [16]. It was also seen that TIGAR expression positively correlated with the increased invasion and metastasis of tumor cells in vitro in nasopharyngeal carcinoma [17] and non-small cell lung cancer [8]. It may be that these types of tumors have intense oxidative stress responses and high malignant degrees, so greater amounts of antioxidants are needed to maintain redox homeostasis. In our study, no correlation was found between TIGAR expression and age, sex, tumor location, maximum diameter, serum CA19-9 level, tumor differentiation, or lymph node metastasis in patients with PDAC (Table 2). In future studies, we will expand the sample size.

18F-FDG PET/CT is widely used in the differential diagnosis, staging, postoperative evaluation, and prognosis prediction for pancreatic cancer [18]. We observed the association between PET/CT parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) and TIGAR expression in patients with PDAC to study the relationship between PET/CT and cellular redox homeostasis. Although SUV is a common clinical indicator, this study did not find a correlation between TIGAR expression and SUV. We found a significant positive correlation between TIGAR expression and MTV. We also found that the PET/CT parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) were higher in patients with high TIGAR expression than those with low TIGAR expression (Table 4). Since TIGAR is considered as a stress-induced gene [19], we hypothesized that patients with high TIGAR expression would experience severe oxidative stress caused by multiple stimulus. So the PET/CT parameters, in patients with PDAC, may be closely related to redox homeostasis. In our study, we found that patients with high TIGAR expression had poor prognoses. Our results suggested that patients with high MTV and TLG have shorter OS, indicating that these PET/CT parameters are associated with poor prognosis in PDAC patients. Previous studies have shown that when compared with SUV, MTV can be used as a prognostic indicator for PDAC [18,20,21]. SUV reflects the metabolic degree of local tumor tissues with poor sensitivity to the overall metabolic degree of heterogeneous lesions. MTV is defined as the tissue volume higher than the threshold value of a certain SUV [22], that is, the volume of FDG uptake in the tumor. Cheung et al. found that the ROS is dynamic regulated by the expression of TIGAR in development of pancreatic cancer. The TIGAR level is high in the premalignant stage and low in the metastatic progression [23]. TIGAR is activated to promote the shift from glycolysis to PPP in order to generate sufficient NADPH and other reducing substances to scavenge ROS to maintain redox homeostasis in tumor cells. The occurrence of pancreatic cancer is caused by multiple genetic abnormalities. The common driver genes in pancreatic cancer are oncogene – KRAS and tumor suppressor genes – TP53, CDKN2A/P16, and SMAD4, which were associated with carcinogenesis and treatment-resistance. Studies have shown that MTV and TLG are significantly increased in pancreatic cancer patients with mutations in TP53−, SMAD4/DPC4− and CDKN2A/P16−, suggesting that ROS caused by gene mutations can regulate redox homeostasis through glucose metabolism reprogramming, which is closely related to PET/CT parameters [24]. About 63.3% of patients with PDAC will have mutations of TP53, and the regulation of TIGAR to glucose metabolism is regulated by the upstream gene TP53. However, in some circumstances, the TIGAR can play a role independently of TP53 [25]. The complex network of TP53, TIGAR, glucose metabolism, and redox homeostasis deserves further studies. Given the consistency of TIGAR expression and MTV, the effectiveness of anti-ROS therapy may be monitored by PET/CT.

This study first evaluated the relationship between TIGAR expression and 18F-FDG PET/CT parameters, but there were some limitations. First, the study was a retrospective analysis of information from PET/CT scans and clinical pathology in patients with PDAC. Second, the study population was small. The conclusion is limited, and we need to further verify the relationship between TIGAR expressions and the clinical and pathological characteristics of patients with PDAC, and 18F-FDG PET/CT parameters.

In conclusion, high TIGAR expression is associated with poor prognoses for patients with PDAC, suggesting that TIGAR can be used as an independent prognostic marker for these patients. MTV, a parameter of 18F-FDG PET/CT, is positively correlated with TIGAR expression and has statistical significance. Therefore, we believe that MTV may serve as a monitoring indicator for TIGAR-related anti-ROS therapy.


This study was supported by the National Natural Science Foundation of China (No. 81871408, 81471720) and Outstanding Scientific Fund of Shengjing Hospital (No. 201402).

Conflicts of interest

There are no conflicts of interest.


1. Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF, Anderson JC, et al. Colorectal cancer statistics, 2020. CA Cancer J Clin. 2020; 70:145–164.
2. Yang J, Chen Z, Liu N, Chen Y. Ribosomal protein L10 in mitochondria serves as a regulator for ROS level in pancreatic cancer cells. Redox Biol. 2018; 19:158–165.
3. Kamisawa T, Wood LD, Itoi T, Takaori K. Pancreatic cancer. Lancet. 2016; 388:73–85.
4. Hsu PP, Sabatini DM. Cancer cell metabolism: Warburg and beyond. Cell. 2008; 134:703–707.
5. Green DR, Chipuk JE. p53 and metabolism: inside the TIGAR. Cell. 2006; 126:30–32.
6. Ko YH, Domingo-Vidal M, Roche M, Lin Z, Whitaker-Menezes D, Seifert E, et al. TP53-inducible glycolysis and apoptosis regulator (TIGAR) metabolically reprograms carcinoma and stromal cells in breast cancer. J Biol Chem. 2016; 291:26291–26303.
7. Ahmad R, Alam M, Hasegawa M, Uchida Y, Al-Obaid O, Kharbanda S, Kufe D. Targeting MUC1-C inhibits the AKT-S6K1-elF4A pathway regulating TIGAR translation in colorectal cancer. Mol Cancer. 2017; 16:33.
8. Shen M, Zhao X, Zhao L, Shi L, An S, Huang G, Liu J. Met is involved in TIGAR-regulated metastasis of non-small-cell lung cancer. Mol Cancer. 2018; 17:88.
9. Tang Z, He Z. TIGAR promotes growth, survival and metastasis through oxidation resistance and AKT activation in glioblastoma. Oncol Lett. 2019; 18:2509–2517.
10. McCormick PN, Greenwood HE, Glaser M, Maddocks ODK, Gendron T, Sander K, et al. Assessment of tumor redox status through (S)-4-(3-[18F]fluoropropyl)-L-glutamic Acid PET imaging of system xc- activity. Cancer Res. 2019; 79:853–863.
11. Čolović M, Yang H, Merkens H, Colpo N, Bénard F, Schaffer P, et al. The effect of chirality on the application of 5-[18F]Fluoro-Aminosuberic acid ([18F]FASu) for oxidative stress imaging. Molecular Imaging Biol. 2020; 22:873–882.
12. Pinho DF, Subramaniam RM. PET-computed tomography and precision medicine in pancreatic adenocarcinoma and pancreatic neuroendocrine tumors. PET Clin. 2017; 12:407–421.
13. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017; 45:W98–W102.
14. Bensaad K, Tsuruta A, Selak MA, Vidal MN, Nakano K, Bartrons R, et al. TIGAR, a p53-inducible regulator of glycolysis and apoptosis. Cell. 2006; 126:107–120.
15. Lee P, Vousden KH, Cheung EC. TIGAR, TIGAR, burning bright. Cancer Metab. 2014; 2:1.
16. Hong M, Xia Y, Zhu Y, Zhao HH, Zhu H, Xie Y, et al. TP53-induced glycolysis and apoptosis regulator protects from spontaneous apoptosis and predicts poor prognosis in chronic lymphocytic leukemia. Leuk Res. 2016; 50:72–77.
17. Wong EY, Wong SC, Chan CM, Lam EK, Ho LY, Lau CP, et al. TP53-induced glycolysis and apoptosis regulator promotes proliferation and invasiveness of nasopharyngeal carcinoma cells. Oncol Lett. 2015; 9:569–574.
18. Lee JW, Kang CM, Choi HJ, Lee WJ, Song SY, Lee JH, Lee JD. Prognostic value of metabolic tumor volume and total lesion glycolysis on preoperative 18F-FDG PET/CT in patients with pancreatic cancer. J Nucl Med. 2014; 55:898–904.
19. Peña-Rico MA, Calvo-Vidal MN, Villalonga-Planells R, Martínez-Soler F, Giménez-Bonafé P, Navarro-Sabaté À, et al. TP53-induced glycolysis and apoptosis regulator (TIGAR) knockdown results in radiosensitization of glioma cells. Radiother Oncol. 2011; 101:132–139.
20. Dholakia AS, Chaudhry M, Leal JP, Chang DT, Raman SP, Hacker-Prietz A, et al. Baseline metabolic tumor volume and total lesion glycolysis are associated with survival outcomes in patients with locally advanced pancreatic cancer receiving stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2014; 89:539–546.
21. Zhu D, Wang L, Zhang H, Chen J, Wang Y, Byanju S, et al. Prognostic value of 18F-FDG-PET/CT parameters in patients with pancreatic carcinoma. Med. 2017; 96:e7813.
22. Kim YI, Paeng JC, Cheon GJ, Suh KS, Lee DS, Chung JK, Kang KW. Prediction of posttransplantation recurrence of hepatocellular carcinoma using metabolic and volumetric indices of 18F-FDG PET/CT. J Nucl Med. 2016; 57:1045–1051.
23. Cheung EC, DeNicola GM, Nixon C, Blyth K, Labuschagne CF, Tuveson DA, et al. Dynamic ROS control by TIGAR regulates the initiation and progression of pancreatic cancer. Cancer Cell. 2020; 37:168–182.e4.
24. Shi S, Ji S, Qin Y, Xu J, Zhang B, Xu W, et al. Metabolic tumor burden is associated with major oncogenomic alterations and serum tumor markers in patients with resected pancreatic cancer. Cancer Lett. 2015; 360:227–233.
25. Rajeshkumar NV, Dutta P, Yabuuchi S, de Wilde RF, Martinez GV, Le A, et al. Therapeutic targeting of the Warburg effect in pancreatic cancer relies on an absence of p53 function. Cancer Res. 2015; 75:3355–3364.

18F-FDG PET/CT; pancreatic ductal adenocarcinoma; prognosis; reactive oxygen species; TIGAR

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