Transketolase like 1 (TKTL1) expression alterations in prostate cancer tumorigenesis

Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering

Cancer patients show heterogeneous phenotypes and very different outcomes and responses even to common treatments, such as standard chemotherapy. This state-of-affairs has motivated the need for the comprehensive characterization of cancer phenotypes and fueled the generation of large omics datasets, comprising multiple omics data reported for the same patients, which might now allow us to start deciphering cancer heterogeneity and implement personalized therapeutic strategies. In this work, we performed the analysis of four cancer types obtained from the latest efforts by The Cancer Genome Atlas, for which seven distinct omics data were available for each patient, in addition to curated clinical outcomes. We performed a uniform pipeline for raw data preprocessing and adopted the Cancer Integration via MultIkernel LeaRning (CIMLR) integrative clustering method to extract cancer subtypes. We then systematically review the discovered clusters for the considered cancer types, highlighting novel associations between the different omics and prognosis.

TGF-β signaling in the tumor metabolic microenvironment and targeted therapies

Transforming growth factor-β (TGF-β) signaling has a paradoxical role in cancer progression, and it acts as a tumor suppressor in the early stages but a tumor promoter in the late stages of cancer. Once cancer cells are generated, TGF-β signaling is responsible for the orchestration of the immunosuppressive tumor microenvironment (TME) and supports cancer growth, invasion, metastasis, recurrence, and therapy resistance. These progressive behaviors are driven by an “engine” of the metabolic reprogramming in cancer. Recent studies have revealed that TGF-β signaling regulates cancer metabolic reprogramming and is a metabolic driver in the tumor metabolic microenvironment (TMME). Intriguingly, TGF-β ligands act as an “endocrine” cytokine and influence host metabolism. Therefore, having insight into the role of TGF-β signaling in the TMME is instrumental for acknowledging its wide range of effects and designing new cancer treatment strategies. Herein, we try to illustrate the concise definition of TMME based on the published literature. Then, we review the metabolic reprogramming in the TMME and elaborate on the contribution of TGF-β to metabolic rewiring at the cellular (intracellular), tissular (intercellular), and organismal (cancer-host) levels. Furthermore, we propose three potential applications of targeting TGF-β-dependent mechanism reprogramming, paving the way for TGF-β-related antitumor therapy from the perspective of metabolism.

The role of transketolase in human cancer progression and therapy

Transketolase (TKT) is an enzyme that is ubiquitously expressed in all living organisms and has been identified as an important regulator of cancer. Recent studies have shown that the TKT family includes the TKT gene and two TKT-like (TKTL) genes; TKTL1 and TKTL2. TKT and TKTL1 have been reported to be involved in the regulation of multiple cancer-related events, such as cancer cell proliferation, metastasis, invasion, epithelial-mesenchymal transition, chemoradiotherapy resistance, and patient survival and prognosis. Therefore, TKT may be an ideal target for cancer treatment. More importantly, the levels of TKTL1 were detected using EDIM technology for the early detection of some malignancies, and TKTL1 was more sensitive and specific than traditional tumor markers. Detecting TKTL1 levels before and after surgery could be used to evaluate the surgery's effect. While targeted TKT suppresses cancer in multiple ways, in some cases, it has detrimental effects on the organism. In this review, we discuss the role of TKT in different tumors and the detailed mechanisms while evaluating its value and limitations in clinical applications. Therefore, this review provides a basis for the clinical application of targeted therapy for TKT in the future, and a strategy for subsequent cancer-related research.

TKTL1 Knockdown Impairs Hypoxia-Induced Glucose-6-phosphate Dehydrogenase and Glyceraldehyde-3-phosphate Dehydrogenase Overexpression

Increased expression of transketolase (TKT) and its isoform transketolase-like-1 (TKTL1) has been related to the malignant leukemia phenotype through promoting an increase in the non-oxidative branch of the pentose phosphate pathway (PPP). Recently, it has also been described that TKTL1 can have a role in survival under hypoxic conditions and in the acquisition of radio resistance. However, TKTL1’s role in triggering metabolic reprogramming under hypoxia in leukemia cells has never been characterized. Using THP-1 AML cells, and by combining metabolomics and transcriptomics techniques, we characterized the impact of TKTL1 knockdown on the metabolic reprogramming triggered by hypoxia. Results demonstrated that TKTL1 knockdown results in a decrease in TKT, glucose-6-phosphate dehydrogenase (G6PD) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) activities and impairs the hypoxia-induced overexpression of G6PD and GAPDH, all having significant impacts on the redox capacity of NADPH- and NADH-related cells. Moreover, TKTL1 knockdown impedes hypoxia-induced transcription of genes encoding key enzymes and transporters involved in glucose, PPP and amino acid metabolism, rendering cells unable to switch to enhanced glycolysis under hypoxia. Altogether, our results show that TKTL1 plays a key role in the metabolic adaptation to hypoxia in THP-1 AML cells through modulation of G6PD and GAPDH activities, both regulating glucose/glutamine consumption and the transcriptomic overexpression of key players of PPP, glucose and amino acids metabolism.

Transketolase promotes colorectal cancer metastasis through regulating AKT phosphorylation

Transketolase (TKT) which is an important metabolic enzyme in the pentose phosphate pathway (PPP) participates in maintaining ribose 5-phosphate levels. TKT is necessary for maintaining cell growth. However, we found that in addition to this, TKT can also affect tumor progression through other ways. Our previous study indicate that TKT could promote the development of liver cancer by affecting bile acid metabolism. And in this study, we discovered that TKT expression was remarkably upregulated in colorectal cancer, abnormal high expression of TKT is associated with poor prognosis of colorectal cancer. Additionally, TKT promoted colorectal cancer cell growth and metastasis. Further study demonstrated that TKT interacted with GRP78 and promoted colorectal cancer cell glycolysis through increasing AKT phosphorylation, thereby enhancing colorectal cancer cell metastasis. Thus, TKT is expected to become an indicator for judging the prognosis of colorectal cancer, and provide a theoretical basis for drug development of new treatment targets for colorectal cancer.

TKTL1 participated in malignant progression of cervical cancer cells via regulating AKT signal mediated PFKFB3 and thus regulating glycolysis

BACKGROUND

Cervical cancer (CC) is the second most common cancer among women with high morbidity and mortality. TKTL1 is a key protein in glucose metabolism in cancer cells and controls the pentose phosphate pathway (PPP). In this paper, we aim to explore whether TKTL1 can participate in the malignant process of CC cells through glucose metabolism.

METHODS

The expression and activity of TKTL1 in CC cell lines were detected by RT-qPCR and Western blot. Cell transfection was conducted to interfere the expression of TKTL1 in SiHa cells, with efficiency detected by RT-qPCR and Western blot. Cell proliferation was then measured by CCK-8 kits. Wound Healing and Transwell experiments were performed to respectively detect the levels of cell migration and invasion, and western blot was used to detect the expressions of migration-related proteins. Tunel and Western blot were used to detect the apoptosis and apoptosis-related proteins. Glucose uptake, lactate production, and ATP production were measured by corresponding commercial kits. Next, the expression of p-Akt, AKT, p-MTOR, mTOR, HK2 and PFKFB3 was detected by Western blot. The mechanism was further investigated by interfering the expression of HK2 and PFKFB3 and adding AKT agonist SC79. At the animal level, the tumor bearing mouse model of CC was constructed, and the weight, volume and pathological morphology of the tumor tissue were detected to verify the cell experiment.

RESULTS

TKTL1 expression was increased in CC cells. Interference of TKTL1 expression can inhibit TKTL1 enzyme activity, proliferation, invasion and migration of CC cells, and simultaneously suppress the generation of glycolysis. In addition, the results showed that TKTL1 activated PFKFB3 through AKT rather than HK2 signaling and is involved in glycolysis, cell invasion, migration, and apoptosis of CC cells. In animal level, inhibition of TKTL1 also contributed to decreased tumor volume of CC tumor bearing mice and improved histopathological status.

CONCLUSION

TKTL1 participated in malignant progression of CC cells via regulating AKT signal-mediated HK2 and PFKFB3 and thus regulating glucose metabolism.

A feedback loop between the androgen receptor and 6-phosphogluoconate dehydrogenase (6PGD) drives prostate cancer growth

Alterations to the androgen receptor (AR) signalling axis and cellular metabolism are hallmarks of prostate cancer. This study provides insight into both hallmarks by uncovering a novel link between AR and the pentose phosphate pathway (PPP). Specifically, we identify 6-phosphogluoconate dehydrogenase (6PGD) as an androgen-regulated gene that is upregulated in prostate cancer. AR increased the expression of 6PGD indirectly via activation of sterol regulatory element binding protein 1 (SREBP1). Accordingly, loss of 6PGD, AR or SREBP1 resulted in suppression of PPP activity as revealed by 1,2-¹³C₂ glucose metabolic flux analysis. Knockdown of 6PGD also impaired growth and elicited death of prostate cancer cells, at least in part due to increased oxidative stress. We investigated the therapeutic potential of targeting 6PGD using two specific inhibitors, physcion and S3, and observed substantial anti-cancer activity in multiple models of prostate cancer, including aggressive, therapy-resistant models of castration-resistant disease as well as prospectively collected patient-derived tumour explants. Targeting of 6PGD was associated with two important tumour-suppressive mechanisms: first, increased activity of the AMP-activated protein kinase (AMPK), which repressed anabolic growth-promoting pathways regulated by acetyl-CoA carboxylase 1 (ACC1) and mammalian target of rapamycin complex 1 (mTORC1); and second, enhanced AR ubiquitylation, associated with a reduction in AR protein levels and activity. Supporting the biological relevance of positive feedback between AR and 6PGD, pharmacological co-targeting of both factors was more effective in suppressing the growth of prostate cancer cells than single-agent therapies. Collectively, this work provides new insight into the dysregulated metabolism of prostate cancer and provides impetus for further investigation of co-targeting AR and the PPP as a novel therapeutic strategy.

Mechanisms of Metabolic Reprogramming in Cancer Cells Supporting Enhanced Growth and Proliferation

Cancer cells alter metabolic processes to sustain their characteristic uncontrolled growth and proliferation. These metabolic alterations include (1) a shift from oxidative phosphorylation to aerobic glycolysis to support the increased need for ATP, (2) increased glutaminolysis for NADPH regeneration, (3) altered flux through the pentose phosphate pathway and the tricarboxylic acid cycle for macromolecule generation, (4) increased lipid uptake, lipogenesis, and cholesterol synthesis, (5) upregulation of one-carbon metabolism for the production of ATP, NADH/NADPH, nucleotides, and glutathione, (6) altered amino acid metabolism, (7) metabolism-based regulation of apoptosis, and (8) the utilization of alternative substrates, such as lactate and acetate. Altered metabolic flux in cancer is controlled by tumor-host cell interactions, key oncogenes, tumor suppressors, and other regulatory molecules, including non-coding RNAs. Changes to metabolic pathways in cancer are dynamic, exhibit plasticity, and are often dependent on the type of tumor and the tumor microenvironment, leading in a shift of thought from the Warburg Effect and the “reverse Warburg Effect” to metabolic plasticity. Understanding the complex nature of altered flux through these multiple pathways in cancer cells can support the development of new therapies.

CircDUSP16 Contributes to Cell Development in Esophageal Squamous Cell Carcinoma by Regulating miR-497-5p/TKTL1 Axis

BACKGROUND

The vital roles of circular RNAs in human cancers have been demonstrated. In this study, we aimed to investigate the functions of circDUSP16 in esophageal squamous cell carcinoma (ESCC) development.

METHODS

Quantitative real-time polymerase chain reaction was executed for the expression levels of circDUSP16, DUSP16, miR-497-5p, and transketolase-like-1 (TKTL1) messenger RNA. Actinomycin D assay and RNase R digestion assay were used to determine the characteristics of circDUSP16. Cell Counting Kit-8 assay and colony formation assay were applied for cell proliferation. Transwell assay was performed to assess cell migration and invasion. The glycolysis level was evaluated using specific kits. Protein levels were measured by Western blot assay. RNA pull-down assay and dual-luciferase reporter assay were adopted to explore the relationships among circDUSP16, miR-497-5p, and TKTL1. Murine xenograft model was used to determine the role of circDUSP16 in ESCC in vivo.

RESULTS

CircDUSP16 level was elevated in ESCC tissues, cells, and hypoxia-stimulated ESCC cells. Knockdown of circDUSP16 suppressed hypoxia-induced ESCC cell viability, colony formation, migration, invasion, and glycolysis. For mechanism analysis, circDUSP16 could positively regulate TKTL1 expression by sponging miR-497-5p in ESCC cells. Moreover, miR-497-5p inhibition restored the effects of circDUSP16 knockdown on the malignant behaviors of ESCC cells under hypoxia condition. MiR-497-5p overexpression suppressed hypoxia-induced ESCC cell progression by targeting TKTL1. In addition, circDUSP16 knockdown repressed the tumorigenesis of ESCC in vivo.

CONCLUSIONS

CircDUSP16 knockdown suppressed hypoxia-induced ESCC cell growth, invasion, and glycolysis by regulating TKTL1 expression through sponging miR-497-5p.

Identification Six Metabolic Genes as Potential Biomarkers for Lung Adenocarcinoma

Metabolic genes have been reported to act as crucial roles in tumor progression. Lung adenocarcinoma (LUAD) is one of the most common cancers worldwide. This study aimed to predict the potential mechanism and novel markers of metabolic signature in LUAD. The gene expression profiles and the clinical parameters were obtained from The Cancer Genome Atlas-Lung adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus data set (GSE72094). A total of 105 differentially expressed metabolic genes of intersect expression in TCGA-LUAD and GSE72094 were screened by R language. Univariate Cox regression model found 18 survival-related genes and then the least absolute shrinkage and selection operator model was successfully constructed. Six significant genes prognostic model was validated though independent prognosis analysis. The model revealed high values for prognostic biomarkers by time-dependent receiver operating characteristic (ROC) analysis, risk score, Heatmap, and nomogram. In addition, Gene Set Enrichment Analysis showed that multiplex metabolism pathways correlated with LUAD. Furthermore, we found the six genes aberrantly expressed in LUAD samples. Our study showed that metabolism pathways play important roles in LUAD progression. The six metabolic genes could predict potential prognostic and diagnostic biomarkers in LUAD.

Metabolomics Contributions to the Discovery of Prostate Cancer Biomarkers

Prostate cancer (PCa) is one of the most frequently diagnosed cancers and a leading cause of death among men worldwide. Despite extensive efforts in biomarker discovery during the last years, currently used clinical biomarkers are still lacking enough specificity and sensitivity for PCa early detection, patient prognosis, and monitoring. Therefore, more precise biomarkers are required to improve the clinical management of PCa patients. In this context, metabolomics has shown to be a promising and powerful tool to identify novel PCa biomarkers in biofluids. Thus, changes in polyamines, tricarboxylic acid (TCA) cycle, amino acids, and fatty acids metabolism have been reported in different studies analyzing PCa patients' biofluids. The review provides an up-to-date summary of the main metabolic alterations that have been described in biofluid-based studies of PCa patients, as well as a discussion regarding their potential to improve clinical PCa diagnosis and prognosis. Furthermore, a summary of the most significant findings reported in these studies and the connections and interactions between the different metabolic changes described has also been included, aiming to better describe the specific metabolic signature associated to PCa.

Prostate Cancer Energetics and Biosynthesis

Cancers must alter their metabolism to satisfy the increased demand for energy and to produce building blocks that are required to create a rapidly growing tumor. Further, for cancer cells to thrive, they must also adapt to an often changing tumor microenvironment, which can present new metabolic challenges (ex. hypoxia) that are unfavorable for most other cells. As such, altered metabolism is now considered an emerging hallmark of cancer. Like many other malignancies, the metabolism of prostate cancer is considerably different compared to matched benign tissue. However, prostate cancers exhibit distinct metabolic characteristics that set them apart from many other tumor types. In this chapter, we will describe the known alterations in prostate cancer metabolism that occur during initial tumorigenesis and throughout disease progression. In addition, we will highlight upstream regulators that control these metabolic changes. Finally, we will discuss how this new knowledge is being leveraged to improve patient care through the development of novel biomarkers and metabolically targeted therapies.