Metabolic reprogramming and cancer progression

Faubert, Brandon; Solmonson, Ashley; DeBerardinis, Ralph J.

doi:10.1126/science.aaw5473

Cited by 1,434 publications

(1,128 citation statements)

References 123 publications

(136 reference statements)

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“…More and more cancer researchers are focusing their attention on the mechanism of tumor metabolic changes. A recent study pointed out that the heterogeneity of metabolic reprogramming in the tumor is clearly related to the outcome of the tumor [26]. New cancer treatments targeting metabolic pathways are being studied in many tumors.…”

Section: Discussionmentioning

confidence: 99%

Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

Wang

Yao

et al. 2020

Preprint

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Background Renal cell carcinoma (RCC) is one of the major malignant tumors of the urinary system, with a high mortality rate and a poor prognosis. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC. Although the diagnosis and treatment methods have been significantly improved, the incidence and mortality of ccRCC are high and still increasing. The occurrence and development of ccRCC are closely related to the changes of classic metabolic pathways. This article aims to explore the relationship between metabolic genes and the prognosis of patients with ccRCC. Patients and methods: Gene expression profiles of 63 normal kidney tissues and 446 ccRCC tissues from TCGA database and gene expression profiles of 39 ccRCC tissues from GEO database were used to obtain differentially expressed genes (DEGs) in ccRCC. Through the the KEGG gene sets of GSEA database, we obtained metabolic genes (MGs). Univariate Cox regression analysis was used to identify prognostic MGs. Lasso regression analysis was used to eliminate false positives because of over-fitting. Multivariate Cox regression analysis was used to established a prognostic model. Gene expression data and related survival data of 101 ccRCC patients from ArrayExpress database were used for external validation. Survival analysis, ROC curve analysis, independent prognostic analysis and clinical correlation analysis were performed to evaluate this model. Results We found that there were 479 abnormally expressed MGs in ccRCC tissues. Through univariate Cox regression analysis, Lasso regression analysis and multivariate Cox regression analysis, we identified 4 prognostic MGs (P4HA3, ETNK2, PAFAH2 and ALAD) and established a prognostic model (riskScore). Whether in the training cohort, the testing cohort or the entire cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 4 MGs was also confirmed in the ArrayExpress database. Results of GSEA analysis show that DEGs in patients with better prognosis were enriched in metabolic pathways. Then, a new Nomogram with higher prognostic value was constructed to better predict the 1-year OS, 3-year OS and 5-year OS of ccRCC patients. In addition, we successfully established a ceRNA network to further explain the differences in the expression of these MGs between high-risk patients and low-risk patients Conclusion We have successfully established a risk model (riskScore) based on 4 MGs, which could accurately predict the prognosis of patients with ccRCC. Our research may shed new light on ccRCC patients' prognosis and treatment management.

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Section: Discussionmentioning

confidence: 99%

Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

Wang

Yao

et al. 2020

Preprint

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“…All the steps of the metastatic cascade, from escape of tumor cells from the primary tumor up to emergence of lethal macrometastases, are mirrored by tumor metabolism and metabolic rewiring. It is also clear that survival in the metastatic environment imposes metabolic requirements distinct from those supporting cell growth; inhibition of these adaptive activities results in decreased metastatic spread [ 145 ]. Moreover, it is becoming increasingly evident that this metabolic plasticity contributes significantly to the primary (and acquired) therapeutic resistance of metastatic cancer [ 146 ].…”

Section: Metabolic Rewiring and Metastasismentioning

confidence: 99%

“…As noted above, the metastatic cascade is a highly inefficient process characterized by the presence of multiple bottlenecks, but metabolic reprogramming enables successful navigation through some of these barriers [ 145 ]. Initial steps of local invasion and intravasation require release of CO2, lactate, and other organic acids from metabolically active tumor cells promoting acidification of tumor microenvironment, decrease in the number of adherens junctions between tumor cells, and degradation of ECM [ 147 ].…”

Section: Metabolic Rewiring and Metastasismentioning

confidence: 99%

A Perspective on Therapeutic Pan-Resistance in Metastatic Cancer

Korentzelos

Clark

Wells

2020

IJMS

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Metastatic spread represents the leading cause of disease-related mortality among cancer patients. Many cancer patients suffer from metastatic relapse years or even decades after radical surgery for the primary tumor. This clinical phenomenon is explained by the early dissemination of cancer cells followed by a long period of dormancy. Although dormancy could be viewed as a window of opportunity for therapeutic interventions, dormant disseminated cancer cells and micrometastases, as well as emergent outgrowing macrometastases, exhibit a generalized, innate resistance to chemotherapy and even immunotherapy. This therapeutic pan-resistance, on top of other adaptive responses to targeted agents such as acquired mutations and lineage plasticity, underpins the current difficulties in eradicating cancer. In the present review, we attempt to provide a framework to understand the underlying biology of this major issue.

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“…Cancer cells undergo extensive metabolic reprogramming including the typical aerobic glycolysis or the Warburg effect, which involves the switch of a multitude of metabolic enzymes to support the diversion of metabolites that facilitate glycolytic fermentation for energy metabolism even under ambient oxygen conditions [1][2][3][4] . Compelling literatures have recently revealed that many glycolytic enzymes have moonlighting activities in regulating gene expression [5][6][7] .…”

Section: Taken Together This Study Establishes a Novel Role For Thementioning

confidence: 99%

ENO1 suppresses cancer cell ferroptosis by degrading the mRNA of iron regulatory protein 1

Zhang¹,

Zhang²,

Sun³

et al. 2020

Preprint

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α-Enolase 1 (ENO1) is a critical glycolytic enzyme whose aberrant expression drives the pathogenesis of various cancers. ENO1 has been indicated to have additional roles beyond its conventional metabolic activity, but the underlying mechanisms and biological consequences remain elusive. Here, we show that ENO1 suppresses iron regulatory protein 1 (IRP1) expression to regulate iron homeostasis and survival of hepatocellular carcinoma (HCC) cells. Mechanistically, we unprecedentedly uncover that ENO1, as an RNA-binding protein, recruits CNOT6 to accelerate the mRNA decay of IRP1 in cancer cells, leading to inhibition of mtioferin-1 (Mfrn1) expression and subsequent repression of mitochondrial iron-induced ferroptosis. Moreover, through in vitro and in vivo experiments and clinical sample analysis, we identified IRP1 and Mfrn1 as tumor suppressors by inducing ferroptosis in HCC cells. Taken together, this study establishes a novel role for the ENO1/IRP1/Mfrn1 pathway in the pathogenesis of HCC and reveals a previously unknown connection between the ENO1/IRP1/Mfrn1 pathway and ferroptosis, suggesting a potential innovative cancer therapy.

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Metabolic reprogramming and cancer progression

Cited by 1,434 publications

References 123 publications

Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

A Perspective on Therapeutic Pan-Resistance in Metastatic Cancer

ENO1 suppresses cancer cell ferroptosis by degrading the mRNA of iron regulatory protein 1

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