Abstract
Background
The clinical significance of glucose transporter 1 (GLUT1) expression in cancers remains controversial due to inconsistent findings. This meta-analysis systematically evaluates the association between GLUT1 overexpression and its prevalence in cancer tissues, highlighting its potential role as a diagnostic biomarker.
Methods
A comprehensive search of MEDLINE and the Cochrane Library was performed up to May 30, 2024, to identify observational studies and RCTs examining the association of GLUT1 expression with its prevalence in cancer tissues. Data were analyzed using random-effects models, and results were presented as Risk Ratios (RRs) with 95% confidence intervals (CIs). Subgroup analyses were performed based on cancer types and geographical locations. This meta-analysis focused on the diagnostic significance of GLUT1 expression in distinguishing cancer tissues from normal tissues. Publication bias and sensitivity analyses were assessed.
Results
Seventeen studies with 1,795 patients were included. GLUT1 overexpression was significantly associated with its prevalence in cancer tissues compared to normal tissues (RR = 5.57, 95% CI = 3.42–9.09, P < 0.001). Subgroup analysis showed the highest prevalence in urological cancers (RR = 20.56, 95% CI = 10.85–38.93), followed by head and neck cancers (RR = 18.00, 95% CI = 2.66–121.63), and gynecological cancers (RR = 10.55, 95% CI = 2.06–54.00). Geographical analysis indicated a stronger association in Europe (RR = 24.18, 95% CI = 13.37–43.73) than in East Asia (RR = 6.16, 95% CI = 2.69–14.15) and North America (RR = 1.90, 95% CI = 1.32–2.73). This analysis evaluates the diagnostic significance of GLUT1 expression in cancer tissues. Evidence of publication bias was detected based on Egger’s test (z = 4.667, p < 0.001), suggesting funnel plot asymmetry.
Conclusion
GLUT1 overexpression is associated with its high prevalence in cancer tissues, particularly in urological and head and neck cancers. These findings suggest that GLUT1 may serve as a potential diagnostic biomarker in oncology. However, this study does not evaluate survival outcomes (such as overall survival or progression-free survival), and further research is needed to determine its diagnostic significance and therapeutic potential.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12672-025-03473-4.
Keywords: GLUT1, Cancer diagnosis, Meta-analysis, Glucose metabolism, Biomarker
Introduction
Cancer has emerged as one of the most prevalent and deadly diseases worldwide, impacting individuals in both technologically advanced and developing countries [1]. Cancer cells modify their metabolic flow through various pathways to satisfy a higher demand for bioenergetic and biosynthetic resources, which is necessary for the growth and viability of cancer cells [2]. The glucose metabolism is dysregulated in cancer cells. The enhanced production of glycolysis-related enzymes as well as increased translocation of GLUT1 and other glucose transporters to the plasma membrane are all components of the glucose metabolic reprogramming that takes place in cancer cells [3].German scientist Otto Heinrich Warburg first discovered that liver cancer cells consume more glucose than normal cells. Moreover, there was a metabolic shift from oxidative phosphorylation to glycolysis in cancer cells, as seen by the enhanced production of lactate cells generated even with sufficient oxygen. The events in question inspired the term “Warburg effect” [4]. Therefore, malignant cells experience cancerous transformation, which increases the uptake of glucose and aerobic glycolysis, leading to the Warburg effect [5].Cancer cells grow more quickly, thus they need more energy to proliferate. The expression of glucose transporters increased in response to an increase in glucose demand in cancer cells, and the metabolic pathway transitioned from mitochondrial oxidative phosphorylation to glycolytic phosphorylation [6]. Additionally, the competitive nutrient milieu in tumor microenvironment (TME) largely favors cancer cells leading to increased malignant plasticity and immunosuppression.
GLUTs, or facilitative sugar transporters, are crucial transmembrane proteins involved in sugar uptake in mammalian cells [7]. GLUT1 upregulation in malignant cells within the tumor microenvironment (TME) has significant implications for cancer progression and treatment. Studies have shown that increased GLUT1 expression is associated with poor prognosis in various tumors, including non-small cell lung cancer (NSCLC) [8]. This upregulation of GLUT1 promotes a malignant phenotype by enhancing cancer cell proliferation, migration, and invasion, and inhibiting apoptosis through integrin β1/Src/FAK signaling pathways [9]. Furthermore, the dysregulated glucose metabolism in the TME, characterized by differential expression of glucose transporters like GLUT1 and GLUT3, plays a crucial role in immune escape mechanisms and cancer cell survival [10]. The competitive uptake of glucose by cancer and immune cells mediated by GLUT expression highlights the intricate interplay between metabolism and immune functionality in the TME [11]. Targeting GLUT1 activity in malignant cells emerges as a promising strategy for developing anti-cancer therapies aimed at disrupting neoplastic growth [12].
Despite growing evidence linking glucose transporters to cancer metabolism, there remains a need for a comprehensive analysis of the existing literature to clarify the association between GLUT1 expression and its prevalence in cancer tissues. This meta-analysis aims to provide insights into the potential significance of GLUT1 as a diagnostic biomarker in oncology.
Materials and methods
Design
This systematic review and meta-analysis were conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions [13]. The findings have been presented following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [14]. The completed PRISMA 2020 checklist corresponding to this review is provided in the Supplementary Material. Additionally, the review protocol has been registered in PROSPERO under the registration number CRD42024553459.
Search strategy
A comprehensive search was conducted in MEDLINE and the Cochrane Library up to May 30, 2024, utilizing a predefined PICO framework (Table 1). To enhance the search coverage, the reference lists of included studies were manually reviewed. The selection process targeted randomized controlled trials and observational studies, employing the following search terms: (“GLUT receptors” OR “glucose transporter proteins” OR “GLUT1”) AND “Neoplasms“[Mesh] AND (“correlation” OR “association” OR “relationship” OR “incidence” OR “occurrence” OR “cancer risk”). The search strategy for the Cochrane Library is given in supplementary data (Supplementary Data S1).References to eligible literature were also manually screened to find potentially relevant studies.
Table 1.
PICO criteria for study inclusion in systematic review and Meta-Analysis on GLUT1 expression in cancer
| Criteria | Description |
|---|---|
| Participant(s) | Individuals with cancer (any type) and corresponding non-cancer controls (when available). |
| Intervention(s) | GLUT1 expression levels in cancer tissues |
| Comparison(s) | GLUT1 expression in non-cancerous tissues or low/no GLUT1 expression within cancer populations. |
| Outcome(s) | Prevalence of GLUT1 overexpression in cancer tissues (diagnostic association). No survival outcomes (OS/PFS) analyzed. |
| Study design(s) | Randomized controlled trials (RCTs), retrospective observational studies and prospective observational studies. |
Inclusion and exclusion criteria
During the initial screening, duplicate records, review articles, case reports, non-English publications, and studies lacking correlation coefficients between GLUT1 expression and cancer were excluded. This meta-analysis includes both observational studies (cohort and case-control) and randomized controlled trials (RCTs) to comprehensively assess the association between GLUT1 expression and its potential clinical significance in cancer. A total of 1795 patients from 17 studies were included in the quantitative analysis (Fig. 1). The primary objective of this systematic review was to assess the prevalence of GLUT1 overexpression in cancer tissues compared to normal or GLUT1-negative tissues, in order to evaluate its potential as a diagnostic biomarker. Studies were considered eligible if tumor diagnosis was confirmed through histopathology or other techniques and GLUT1 expression was assessed. The exclusion criteria included: (1) systematic reviews; (2) case reports; (3) studies predicting treatment outcomes or assessing histopathology post-treatment; (4) non-English language publications; and (5) studies conducted on xenograft or animal models (e.g., mouse or rabbit).
Fig. 1.
PRISMA Flow Diagram Illustrating Study Selection Process. This figure outlines the identification, screening, eligibility, and inclusion stages for studies in the systematic review and meta-analysis, detailing reasons for exclusion at each stage
Data extraction
To minimize bias in data abstraction, two investigators independently reviewed the full-text articles and extracted relevant information. The extracted data included the author’s name, year of publication, study population origin, cancer type, gender distribution, total number of patients, age, cancer stage, follow-up duration, mortality rates during the study, tissue sources, the methods used to assess GLUT1 expression, and cut-off definition (Table S1, see supplementary material). If trials did not explicitly report the number of patients with GLUT1 expression, statistical methods were applied to estimate the data. Any discrepancies between the investigators were resolved through discussion.
Risk of bias assessment
To assess the methodological quality of the included studies, two different tools were employed based on study design. For the three randomized controlled trials (RCTs), the revised Cochrane Risk of Bias tool (RoB 2.0) was used via RevMan Web. This tool evaluates five domains: bias arising from the randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Each domain was rated as “low risk,” “some concerns,” or “high risk” of bias. Discrepancies were resolved through discussion and consensus [13].
For the observational studies, the Newcastle-Ottawa Scale (NOS) was applied, which assesses studies based on selection of participants, comparability of study groups, and ascertainment of exposure or outcome. A maximum of nine stars could be awarded, with studies scoring seven or more stars considered high quality. Two independent reviewers conducted the assessment, and any disagreements were resolved through discussion until consensus was achieved.
Publication bias and sensitivity analysis
Publication bias and sensitivity analysis were assessed using the RevMan Web tool. P < 0.05 was regarded as evidence of small-study effects. Publication bias was quantitatively tested using Egger’s test using the JASP software (version 0.18.3)and visually examined using funnel plots. Sensitivity tests were carried out, whereby each trial was eliminated from the meta-analysis, and the effect size was recalculated using the remaining trials, in order to ascertain whether any one of the trials had a particularly significant impact on the overall results. Additionally, sensitivity assessments were conducted using the leave-one-out technique [15] in Open Metanalyst software.
Statistical analysis
Heterogeneity among the included studies was evaluated using the I² statistic, with values greater than 50% indicating substantial heterogeneity. Risk Ratios (RRs) with 95% confidence intervals (CIs) were calculated to assess the diagnostic value of GLUT1 overexpression. A random-effects model was applied to account for heterogeneity across studies. Sensitivity analysis was conducted using the leave-one-out method to evaluate the robustness of the results. Publication bias was assessed using Begg’s and Egger’s tests, with a significance threshold set at P < 0.05. All statistical analyses were performed using RevMan Web [16].
Results
Literature search and study characteristics
The study selection process is illustrated in Fig. 1. An initial search yielded 2,786 records. After eliminating duplicate entries and excluding studies that did not meet the inclusion criteria—such as those that were not randomized controlled trials (RCTs), clinical trials, or observational studies—a total of 50 records were screened. Forty-five full-text articles were further evaluated. Subsequently, 28 studies were excluded for the following reasons: 15studies lacked the related study design, and 13 of them were excluded for not having related data. Finally, 17studies were included in this meta-analysis (Fig. 1). The characteristics of the included studies are detailed in Table S1 [17–33]. As shown in Table S1, the definition of GLUT1 positivity varied across studies, with cut-off values based on staining intensity, percentage of positive tumor cells, or composite scoring systems. The specific cut-off criteria used in each study are detailed within the table. All eligible studies were published in English. In summary, the included studies comprised 11 retrospective observational studies, 3 prospective observational studies, and 3 randomized controlled trials (RCTs). Sample sizes ranged from 10 to 1168 participants, and the studies were published between 2001 and 2015. Fourteen of the included studies (82%) measured GLUT1 expression employing immuno-histochemistry (IHC) staining, while one study each used tissue microarrays (TMA) [27] in human tissues, quantitative reverse transcription polymerase chain reaction (RT-PCR) [23] and antibodies to stain GLUT1 [31]. The cut-off values varied across studies detailed in Supplementary Table S1. A total of 1795 patients’ data with an age ranging from 10 to 90 years consisting of 22% males and 78% females was used in quantitative analysis. Three studies did not mention or specify the gender distribution. Among the studies, one study each evaluated oral squamous cell carcinoma, and bladder cancer, while two studies each evaluated bone or soft tissue sarcoma, osteosarcoma, resectable colorectal cancer metastatic to the liver, adenocarcinoma, breast cancer, endometrial cancer, cervical cancer, colorectal cancer and non-small-cell lung cancer. One study dealt with different types of cancer. Subgroup analyses were performed based on cancer types and geographical locations to further explore the heterogeneity and diagnostic significance of GLUT1 expression across different cancer categories and regions.
Association of GLUT1 with various cancers
It was found that GLUT1 was overexpressed in all of the cancer types included in this meta-analysis. The pooled data from 17 studies showed a Risk Ratio (RR) of 5.57 [3.42–9.09], indicating a significant diagnostic value of GLUT1 expression in cancer patients (Fig. 2). Subgroup analysis based on cancer type revealed notable variations across different malignancies. Urological cancers exhibited the highest risk associated with GLUT1 overexpression (RR = 20.56 [10.85–38.93]), followed by head and neck cancers (RR = 18.00 [2.66–121.63]), lung cancer (RR = 12.94 [0.24–683.74]), gastrointestinal cancers (RR = 11.31 [0.92–138.43]), gynecological cancers (RR = 10.55 [2.06–54.00]), adenocarcinomas (RR = 7.71 [0.45–132.83]), bone and soft tissue sarcomas (RR = 4.51 [0.67–30.17]), and breast cancer (RR = 1.53 [1.40–1.68]). The subgroup analysis based on cancer type is presented in Fig. 3.
Fig. 2.
Forest plot of the pooled Risk Ratio (RR) for the association between GLUT1 overexpression and its prevalence in cancer tissues. The meta-analysis of 17 studies (n = 1,795 patients) showed a significant association, with an overall RR of 5.57 [95% CI 3.42–9.09, P < 0.001], indicating that GLUT1 overexpression is more frequent in cancer tissues compared to normal tissues. This analysis reflects the diagnostic association of GLUT1 expression
Fig. 3.
Subgroup analysis of GLUT1 overexpression by cancer type. The forest plot illustrates the variation in diagnostic significance across different malignancies. Urological cancers exhibited the highest risk (RR = 20.56 [10.85–38.93]), followed by head and neck cancers (RR = 18.00 [2.66–121.63]), gynecological cancers (RR = 10.55 [2.06–54.00]), and gastrointestinal cancers (RR = 11.31 [0.92–138.43]). Lower RRs were observed in breast cancer (RR = 1.53 [1.40–1.68]) and bone and soft tissue sarcomas (RR = 4.51 [0.67–30.17])
Further subgroup analysis based on geographical locations indicated substantial differences in diagnostic value. Studies conducted in Europe reported the highest RR of 24.18 [13.37–43.73], followed by East Asia with a RR of 6.16 [2.69–14.15], and North America with a RR of 1.90 [1.32–2.73]. This geographical variability highlights potential population-specific influences on the diagnostic role of GLUT1. The results of the geographical subgroup analysis are presented in Fig. 4.
Fig. 4.
Subgroup analysis of GLUT1 overexpression by geographical location. The forest plot shows substantial geographical variations in the diagnostic significance of GLUT1. The highest RR was observed in European populations (RR = 24.18 [13.37–43.73]), followed by East Asia (RR = 6.16 [2.69–14.15]), while North America exhibited the lowest RR of 1.90 [1.32–2.73]. This variability suggests potential population-specific influences on the diagnostic role of GLUT1
Publication bias
A visual examination of the funnel plot (Supplementary Fig. 1) suggested notable asymmetry, with a clear clustering of studies on the right side, indicating a possible small-study effect or publication bias. This was further supported by Egger’s test, conducted using JASP (v0.18.3), which yielded a statistically significant result (z = 4.667, p < 0.001), confirming funnel plot asymmetry. These findings suggest the presence of small-study effects that may reflect publication bias.
Sensitivity analyses
The systematic exclusion of individual trials did not impact the overall results. Sensitivity analyses using leave-one-out forest plots confirmed that no single study disproportionately influenced the findings. The overall association between GLUT1 expression and various cancers remained consistent (Supplementary Fig. 3).
Risk of bias assessment
The three included randomized controlled trials were evaluated using the RoB 2.0 tool. Results showed low to moderate risk of bias across most domains, with no serious methodological limitations (Supplementary Fig. 2A and B). For the observational studies, the Newcastle-Ottawa Scale (NOS) was used. Ten out of thirteen studies scored between 7 and 9 stars, indicating high methodological quality, while two studies scored 6 stars and one study scored 4 stars due to insufficient information on follow-up and comparability (Supplementary Table 2). Overall, the majority of studies were considered to have low risk of bias.
Discussion
GLUT1 is a major metabolic checkpoint determining glucose intake in cells. Highly charged TME is expected to upregulate GLUT1 in cancer cells as compared to nearby normal cells. Cancer cells edge highly competitive nutrient scenarios and upregulate metabolic checkpoint genes such as GLUT1 [34, 35]. GLUT1 upregulation in cancer cells plays a crucial role in promoting tumor progression and chemoresistance across various types of cancer. Studies have shown that increased expression of GLUT1 is associated with poor prognosis in endometrial cancer (EC) patients, contributing to enhanced cell proliferation, invasion, glycolysis, and resistance to paclitaxel [36]. Furthermore, in lung adenocarcinoma, tumor-associated neutrophils (TANs) exhibit elevated levels of GLUT1 and increased glucose metabolism, supporting tumor growth and potentially influencing their pro-versus antitumor behaviors [37]. In prostate cancer, SOD2 overexpression has been linked to upregulation of GLUT1 and increased glucose uptake, indicating a significant role of SOD2 in regulating redox and glycolytic metabolism in prostate cancer cells [38]. These findings underscore the importance of targeting GLUT1 and associated pathways to potentially overcome drug resistance and inhibit tumor progression in various cancer types. This meta-analysis aims to consolidate existing knowledge on GLUT1 overexpression across different cancer types. This systematic review and meta-analysis consolidate current evidence on GLUT1 overexpression across different cancer types, using data from 1,795 patients across 17 studies. We found a strong association between GLUT1 expression and malignant tissues compared to normal controls. Although our analysis confirms a high prevalence of GLUT1 overexpression in malignant tissues, we did not evaluate clinical endpoints such as tumor stage, grade, or patient survival. Therefore, any associations between GLUT1 expression and tumor aggressiveness or adverse outcomes require further investigation. However, since our meta-analysis did not include pooled data specifically assessing these clinicopathologic parameters, these associations should be interpreted cautiously. To explore potential patterns, subgroup analyses were conducted using risk ratios (RR) based on cancer types and geographical locations, but not specifically on tumor grade, size, or nodal status due to insufficient uniform reporting across studies.
In the meta-analysis of 17 studies, the overall risk ratio for the diagnostic value of GLUT1 expression in cancer patients was 5.57 [3.42–9.09]. Subgroup analysis based on cancer types revealed that urological cancers exhibited the highest risk ratio (RR = 20.56 [10.85–38.93]), followed by head and neck cancers (RR = 18.00 [2.66–121.63]), lung cancer (RR = 12.94 [0.24–683.74]), gastrointestinal cancers (RR = 11.31 [0.92–138.43]), gynecological cancers (RR = 10.55 [2.06–54.00]), adenocarcinomas (RR = 7.71 [0.45–132.83]), bone and soft tissue sarcomas (RR = 4.51 [0.67–30.17]), and breast cancer (RR = 1.53 [1.40–1.68]). Although several subgroups demonstrated relatively high-risk ratios, including gastrointestinal cancers, lung cancer, and adenocarcinomas, their associations with GLUT1 overexpression were not statistically significant, as indicated by wide confidence intervals that included the null value (RR = 1.0). This lack of significance may be attributed to small sample sizes, limited number of studies, and substantial variability in methodology or population characteristics. For example, gastrointestinal cancers were represented by only three studies with a total of 150 participants, which may reduce statistical power. Similarly, lung cancer and adenocarcinoma subgroups had fewer than 100 and 32 participants, respectively, across multiple studies. These limitations introduce heterogeneity and imprecision, emphasizing the need for cautious interpretation of subgroup findings based solely on RR magnitude.
Subgroup analysis based on geographical locations indicated that studies conducted in Europe showed the highest risk ratio of 24.18 [13.37–43.73], followed by East Asia with an RR of 6.16 [2.69–14.15], and North America with an RR of 1.90 [1.32–2.73]. These findings highlight the frequent overexpression of GLUT1 in various cancer types and across different populations, supporting its role as a potential diagnostic biomarker.
Metabolic alterations are a hallmark of cancer [1] and play a significant role in invasion and metastasis [39]. GLUT1, also known as glucose transporter member 1 (SLC2A1), is one of the primary glucose transporters in cancer cells, facilitating increased glucose uptake in tumors [40]. Furthermore, GLUT1 has been reported to drive tumor cell proliferation, migration, and invasion through the activation of the EGFR/MAPK and integrin/Src/FAK signaling pathways. Its expression is also linked to 18 F-FDG uptake, further reinforcing its association with tumor progression [41]. Previous research has shown that GLUT1 mRNA levels are significantly elevated in cancerous tissues and cell lines compared to normal tissues. Notably, inhibiting GLUT1 expression has been found to substantially reduce cancer cell survival and migration potential [42]. Given its role in tumor metabolism, GLUT1 expression can be conveniently assessed through routine histopathological biopsy before surgery [43].
However, it is important to clarify that this study evaluated the association of GLUT1 expression with cancerous versus normal tissues, and did not assess diagnostic test accuracy metrics (such as sensitivity, specificity, or area under the ROC curve). Thus, while GLUT1 overexpression is consistently observed in cancer tissues, its utility as a diagnostic biomarker cannot be confirmed based on our findings. Future studies should explore GLUT1’s diagnostic accuracy using robust statistical approaches such as ROC analysis, predictive values, and prospective validation in clinical settings.
Conclusion
GLUT1 overexpression is strongly associated with cancer tissues compared to normal tissues, supporting its role as a potential diagnostic biomarker. However, no survival data were analyzed, and thus its diagnostic significance remains unvalidated. Future studies focusing on clinical outcomes are needed to establish its diagnostic relevance. While this meta-analysis highlights the high prevalence of GLUT1 overexpression in cancer tissues, its utility as a standalone diagnostic marker is limited by non-specific expression in benign conditions [44, 45]. Future research integrating GLUT1 expression with clinicopathological variables and multi-marker panels is warranted to better define its diagnostic and prognostic roles.
Limitations
Although we have attempted to directly assess the role of GLUT1 overexpression in cancer tissues, our pooled data predominantly represent female patients, with approximately 78% of the included population being women. This gender imbalance likely reflects the overrepresentation of female-predominant cancers, such as breast and gynecological malignancies, rather than a selection bias. However, this limits the generalizability of our findings to male-dominant cancers such as prostate or liver cancer, which were underrepresented in the included studies.
We did not conduct a sensitivity analysis excluding female-predominant cancers due to limitations in the available data and the scope of this meta-analysis. Nevertheless, it is important to consider that sex-specific biological differences and cancer epidemiology may influence GLUT1 expression patterns. Future studies should incorporate sex-stratified analyses to explore potential differences in GLUT1 prevalence and prognostic significance between male- and female-predominant cancers.
Additionally, several subgroup analyses were based on a limited number of studies or participants, resulting in wide confidence intervals that included the null value, indicating non-significant associations despite seemingly elevated risk ratios. This highlights the risk of over interpretation and restricts our ability to draw definitive conclusions for certain cancer types, including gastrointestinal, lung, or adenocarcinomas, and underscores the need for larger, adequately powered studies to validate these associations.
Moreover, most included studies were retrospective in nature, and while we attempted to incorporate data from both observational studies and randomized controlled trials, this may have introduced heterogeneity due to differences in study design, population characteristics, and outcome measurements. Despite this, we believe our comprehensive approach offers a broader perspective on the relationship between GLUT1 overexpression and cancer prevalence.
One limitation of this study is the variation in GLUT1 positivity thresholds across studies—such as differences in staining intensity cut-offs or scoring systems—which may have introduced heterogeneity and affected the comparability of prevalence estimates.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Author contributions
QY and HL contributed equally to the conceptualization and design of the study and should be regarded as co-first authors. CS and YT performed the data acquisition and statistical analyses. WH and RS were responsible for literature search and data extraction. CW supervised the study methodology, ensured the integrity of the meta-analysis, and provided critical revisions to the manuscript. LH, as the corresponding author, managed overall project coordination, wrote the manuscript, and finalized the submission process. All authors reviewed and approved the final manuscript.
Funding
Not applicable.
Data availability
Data will be available on request to the corresponding author.
Declarations
Ethics approval and consent to participate
Not applicable. This study is a systematic review and meta-analysis based on previously published studies and does not involve any new data collection involving human participants or animals.
Consent for publication
Not applicable. This study did not involve individual participant data, images, or other personal information requiring consent for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Quan Yuan and Hongzhi Liu contributed equally to this work.
Chen Wenjuan and Le Han have equal contribution as correspond author.
References
- 1.Sharma A, et al. Adv Lung Cancer Treat Using Nanomedicines. 2022;8(1):10–41. [Google Scholar]
- 2.Martínez-Reyes I, N.S.J.N.R C, Chandel. Cancer Metabolism: Look Forw. 2021;21(10):669–80. [DOI] [PubMed] [Google Scholar]
- 3.Gyamfi J, Kim J. J.I.j.o.m.s. Choi. Cancer as Metabolic Disorder. 2022;23(3):1155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Dey S, et al. Multifaceted entrancing role of glucose and its analogue, 2-deoxy-D-glucose in cancer cell proliferation, inflammation, and virus infection. Biomed Pharmacother. 2022;156:113801. [DOI] [PubMed] [Google Scholar]
- 5.Romero-Garcia S, et al. Lactate contribution to the tumor microenvironment: mechanisms, effects on immune cells and therapeutic relevance. Front Immunol. 2016;7:52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Liu Y, et al. Small compound inhibitors of basal glucose transport inhibit cell proliferation and induce apoptosis in cancer cells via glucose-deprivation-like mechanisms. Cancer Lett. 2010;298(2):176–85. [DOI] [PubMed] [Google Scholar]
- 7.Ismail A, Tanasova M. Importance of GLUT transporters in disease diagnosis and treatment. Int J Mol Sci. 2022;23(15):8698. [DOI] [PMC free article] [PubMed]
- 8.Cribioli E, et al. Enforcing GLUT3 expression in CD8(+) T cells improves fitness and tumor control by promoting glucose uptake and energy storage. Front Immunol. 2022;13:976628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Zhao H, et al. Glucose transporter 1 promotes the malignant phenotype of Non-Small cell lung cancer through integrin β1/Src/FAK signaling. J Cancer. 2019;10(20):4989–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Na KJ, et al. Reciprocal change in glucose metabolism of cancer and immune cells mediated by different glucose transporters predicts immunotherapy response. Theranostics. 2020;10(21):9579–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Pehlivanoglu S, et al. Epithelial mesenchymal transition regulator TWIST1 transcription factor stimulates glucose uptake through upregulation of GLUT1, GLUT3, and GLUT12 in vitro. Vitro Cell Dev Biol Anim. 2021;57(10):933–43. [DOI] [PubMed] [Google Scholar]
- 12.Zambrano A, et al. Glut 1 in cancer cells and the inhibitory action of resveratrol as a potential therapeutic strategy. Int J Mol Sci. 2019;20(13):3374. [DOI] [PMC free article] [PubMed]
- 13.Higgins J, Green S. Cochrane handbook for systematic reviews of interventions (5. lO ed.). Cochrane Book Series. 2011.
- 14.Moher D, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Willis BH, Riley RD. Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice. Stat Med. 2017;36(21):3283–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Fonseca BHS, de Andrade PHS, Luvizutto GJ. Does non-invasive brain stimulation improve Spatiotemporal gait parameters in people with multiple sclerosis? A systematic review and meta-analysis. J Bodyw Mov Ther. 2024;37:350–9. [DOI] [PubMed] [Google Scholar]
- 17.Tian M, et al. Expression of Glut-1 and Glut-3 in untreated oral squamous cell carcinoma compared with FDG accumulation in a PET study. Eur J Nucl Med Mol Imaging. 2004;31(1):5–12. [DOI] [PubMed] [Google Scholar]
- 18.Chung JH, et al. Overexpression of Glut1 in lymphoid follicles correlates with false-positive (18)F-FDG PET results in lung cancer staging. J Nucl Med. 2004;45(6):999–1003. [PubMed] [Google Scholar]
- 19.Yen TC, et al. 18F-FDG uptake in squamous cell carcinoma of the cervix is correlated with glucose transporter 1 expression. J Nucl Med. 2004;45(1):22–9. [PubMed] [Google Scholar]
- 20.Tateishi U, et al. Glut-1 expression and enhanced glucose metabolism are associated with tumour grade in bone and soft tissue sarcomas: a prospective evaluation by [18F]fluorodeoxyglucose positron emission tomography. Eur J Nucl Med Mol Imaging. 2006;33(6):683–91. [DOI] [PubMed] [Google Scholar]
- 21.Nakamura K, et al. The SUVmax of 18F-FDG PET correlates with histological grade in endometrial cancer. Int J Gynecol Cancer. 2010;20(1):110–5. [DOI] [PubMed] [Google Scholar]
- 22.Im HJ, et al. Prediction of tumour necrosis fractions using metabolic and volumetric 18F-FDG PET/CT indices, after one course and at the completion of neoadjuvant chemotherapy, in children and young adults with osteosarcoma. Eur J Nucl Med Mol Imaging. 2012;39(1):39–49. [DOI] [PubMed] [Google Scholar]
- 23.Wilson PM, et al. Intratumoral expression profiling of genes involved in angiogenesis in colorectal cancer patients treated with chemotherapy plus the VEGFR inhibitor PTK787/ZK 222584 (vatalanib). Pharmacogenomics J. 2013;13(5):410–6. [DOI] [PubMed] [Google Scholar]
- 24.Verstraete M, et al. Investigation of possible endogenous hypoxia markers in colorectal cancer. Int J Radiat Biol. 2013;89(1):9–15. [DOI] [PubMed] [Google Scholar]
- 25.Eustace A, et al. Necrosis predicts benefit from hypoxia-modifying therapy in patients with high risk bladder cancer enrolled in a phase III randomised trial. Radiother Oncol. 2013;108(1):40–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Sadlecki P, et al. The role of Hypoxia-inducible factor-1 α, glucose transporter-1, (GLUT-1) and carbon anhydrase IX in endometrial cancer patients. Biomed Res Int. 2014;2014:p616850. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Bane AL, et al. Tumor factors predictive of response to hypofractionated radiotherapy in a randomized trial following breast conserving therapy. Ann Oncol. 2014;25(5):992–8. [DOI] [PubMed] [Google Scholar]
- 28.Riedl CC, et al. 18F-FDG PET scanning correlates with tissue markers of poor prognosis and predicts mortality for patients after liver resection for colorectal metastases. J Nucl Med. 2007;48(5):771–5. [DOI] [PubMed] [Google Scholar]
- 29.Hu M, et al. Hypoxia imaging with 18F-fluoroerythronitroimidazole integrated PET/CT and immunohistochemical studies in non-small cell lung cancer. Clin Nucl Med. 2013;38(8):591–6. [DOI] [PubMed] [Google Scholar]
- 30.Brown RS, et al. Expression of hexokinase II and Glut-1 in untreated human breast cancer. Nucl Med Biol. 2002;29(4):443–53. [DOI] [PubMed] [Google Scholar]
- 31.Mensch LS, et al. GLUT1 antibody staining in thin-layer specimens of benign and malignant body cavity effusions. Acta Cytol. 2002;46(5):813–8. [DOI] [PubMed] [Google Scholar]
- 32.Yun MS, et al. Additional prognostic value of SUVmax measured by F-18 FDG PET/CT over biological marker expressions in surgically resected cervical cancer patients. Oncol Res Treat. 2015;38(9):413–6. [DOI] [PubMed] [Google Scholar]
- 33.Heinrich S, et al. Neoadjuvant chemotherapy generates a significant tumor response in resectable pancreatic cancer without increasing morbidity: results of a prospective phase II trial. Ann Surg. 2008;248(6):1014–22. [DOI] [PubMed] [Google Scholar]
- 34.Zhou Z, et al. GLUT1 promotes cell proliferation via binds and stabilizes phosphorylated EGFR in lung adenocarcinoma. Cell Commun Signal. 2024;22(1):303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Deepika K, et al. Immunohistochemical evaluation of Glut1 in dentigerous cysts, odontogenic keratocysts, and ameloblastoma. J Oral Maxillofac Pathol. 2024;28(1):77–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Dong P, et al. Long Non-Coding RNA TMPO-AS1 promotes GLUT1-Mediated Glycolysis and Paclitaxel resistance in endometrial cancer cells by interacting with miR-140 and miR-143. Front Oncol. 2022;12:912935. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Ancey PB, et al. GLUT1 expression in Tumor-Associated neutrophils promotes lung cancer growth and resistance to radiotherapy. Cancer Res. 2021;81(9):2345–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Quiros-Gonzalez I, et al. Androgen-dependent prostate cancer cells reprogram their metabolic signature upon GLUT1 upregulation by manganese superoxide dismutase. Antioxid (Basel). 2022;11(2):263. [DOI] [PMC free article] [PubMed]
- 39.Libby CJ, et al. A role for GLUT3 in glioblastoma cell invasion that is not recapitulated by GLUT1. Cell Adh Migr. 2021;15(1):101–115. [DOI] [PMC free article] [PubMed]
- 40.Dai W et al. GLUT3 induced by AMPK/CREB1 axis is key for withstanding energy stress and augments the efficacy of current colorectal cancer therapies. 2020. 5(1): p. 177. [DOI] [PMC free article] [PubMed]
- 41.Deng Y, et al. Clinicopathological and prognostic significance of GLUT1 in breast cancer: A meta-analysis. Med (Baltim). 2018;97(48):e12961. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Yu M, et al. The prognostic value of GLUT1 in cancers: a systematic review and meta-analysis. Oncotarget. 2017;8(26):43356–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Yu M, et al. Metabolic phenotypes in pancreatic cancer. PLoS ONE. 2015;10(2):e0115153. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Airley R, et al. Glucose transporter Glut-1 is detectable in peri-necrotic regions in many human tumor types but not normal tissues: study using tissue microarrays. Ann Anat. 2010;192(3):133–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Temre MK, Kumar A, Singh SM. An appraisal of the current status of Inhibition of glucose transporters as an emerging antineoplastic approach: promising potential of new pan-GLUT inhibitors. Front Pharmacol. 2022;13:1035510. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data will be available on request to the corresponding author.