Lipid Synthesis Is a Metabolic Liability of Non–Small Cell Lung Cancer

Svensson, Robert; Shaw, Reuben J.

doi:10.1101/sqb.2016.81.030874

Cited by 34 publications

(30 citation statements)

References 71 publications

(83 reference statements)

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“…Lipid metabolism is also involved in NSCLC development. Studies showed that lipid synthesis is the metabolic liability of lipid metabolism in NSCLC (41). Different from normal tissue, NSCLC shows an enhanced lipid synthesis ability to favor its lipid demand due to rapid cell proliferation.…”

Section: Lipid Metabolismmentioning

confidence: 99%

Nrf2 Mediates Metabolic Reprogramming in Non-Small Cell Lung Cancer

Zhao

Meng

et al. 2020

Front. Oncol.

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Nuclear factor erythroid-2–related factor-2 (NFE2L2/Nrf2) is a transcription factor that regulates the expression of antioxidant genes. Both Kelch-like ECH-associated protein 1 (Keap1) mutations and Nrf2 mutations contribute to the activation of Nrf2 in non-small cell lung cancer (NSCLC). Nrf2 activity is associated with poor prognosis in NSCLC. Metabolic reprogramming represents a cancer hallmark. Increasing studies reveal that Nrf2 activation promotes metabolic reprogramming in cancer. In this review, we discuss the underlying mechanisms of Nrf2-mediated metabolic reprogramming and elucidate its role in NSCLC. Inhibition of Nrf2 can alter metabolic processes, thus suppress tumor growth, prevent metastasis, and increase sensitivity to chemotherapy in NSCLC. In conclusion, Nrf2 may serve as a therapeutic target for the treatment of NSCLC.

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Section: Lipid Metabolismmentioning

confidence: 99%

Nrf2 Mediates Metabolic Reprogramming in Non-Small Cell Lung Cancer

Zhao

Meng

et al. 2020

Front. Oncol.

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“…ACC1 is a kind of lipid metabolism enzyme and converts from acetyl CoA to malonyl CoA which is the rate limiting step in de novo fatty acid synthesis (FASyn) [20]. Lots of cancers demonstrate an increase in FASyn and decrease of FASyn inhibits the tumor growth [21,22]. It is observed an overexpression of ACC1 in several kinks of cancers, including breast cancer, lung cancer and prostate cancer [23,24].…”

Section: Discussionmentioning

confidence: 99%

Construction of a protein-based model for prognosis prediction of kidney renal clear cell carcinoma: an investigation based on functional proteomics data

Jiang

Chen

Wan

et al. 2020

Preprint

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Background Several studies have shown prognostic value of gene-based models at mRNA level in kidney renal clear cell carcinoma (KIRC). However, protein-based models for prognosis prediction of KIRC are rarely reported, then we conduct this study. Methods Proteomics and clinical data of KIRC were acquired from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA), respectively. Prognosis-associated proteins were screened by univariate Cox regression analysis and log-rank test. Patients were grouped into training set and testing set. The protein-based prognostic model was constructed by lasso Cox regression analysis in training set and validated in test set and whole set. Results A five-protein model (ACC1, IGFBP2, MIG6, PEA15 and RAD51) was constructed for KIRC prognosis prediction. It could well classify KIRC patients into high-risk and low-risk group with significantly different survival. The results was validated in the testing set and whole set. Age, AJCC stage and risk score based on the five-protein model were identified as independent prognostic parameters and they were used to construct a nomogram. The calibration plot showed the nomogram had good agreement between predicted and actual outcomes. Time-dependent ROC curves revealed the nomogram performed best in predicting the 1-year, 3-year and 5-year overall survival (OS) compared with other independent prognostic parameters. DCA demonstrated the nomogram had obviously clinical net benefit. Furthermore, several cancer-related biological signaling pathways were enriched in the functional enrichment analysis. Conclusion Our study developed an effective protein-based model to predict the OS of KIRC, which may help clinicians to offer individual treatment.

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“…Considering that one of the common colonization sites for metastatic lung cancer is liver (33)(34)(35)(36), pointing to a favorable environment for cancer cells to thrive, we wondered whether the four urinary metabolite biomarkers may be able to distinguish liver cancer cases from high-risk patients (e.g., harboring HBV and HCV infections)-individuals who would be subject to liver cancer screening. While the two cancer types share some of the mutual metabolic characteristics, such as deregulated lipid metabolism (23,37,38), not enough is known about the similarities between lung and liver cancer metabolism or about overlapping products of metabolism that may serve as robust biomarkers in both cancer types. We therefore hypothesized that the four metabolites previously described as robust markers of lung cancer (27), even 2 or more years prior to diagnosis (26), may demonstrate a significant diagnostic and prognostic potential in liver cancer.…”

Section: Discussionmentioning

confidence: 99%

Urinary Metabolites Diagnostic and Prognostic of Intrahepatic Cholangiocarcinoma

Haznadar

Diehl

Parker

et al. 2019

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background: Liver cancer is the second leading cause of cancer-related deaths worldwide. With a predicted 2.4-fold rise in liver cancer incidence by 2020, there is an urgent need for early, inexpensive diagnostic biomarkers to deploy in the clinic. Methods: We employed ultraperformance liquid chromatography tandem mass-spectrometry (UPLC/MS-MS) for the quantitation of four metabolites, creatine riboside (CR), N-acetylneuraminic acid (NANA), cortisol sulfate, and a lipid molecule designated as 561þ, in urine samples from the NCI-MD cohort comprising 98 hepatocellular carcinoma (HCC) cases, 101 high-risk subjects, and 95 controls. Validation was carried out in the TIGER-LC cohort [n ¼ 370 HCC and intrahepatic cholangiocarcinoma (ICC) cases, 471 high-risk subjects, 251 controls], where ICC, the second most common primary hepatic malignancy, is highly prevalent. Metabolite quantitation was also conducted in TIGER-LC tissue samples (n ¼ 48 ICC; n ¼ 51 HCC). Results: All profiled metabolites were significantly increased in liver cancer when compared with high-risk subjects and controls in the NCI-MD study. In the TIGER-LC cohort, the four-metabolite profile was superior at classifying ICC than a clinically utilized marker, CA19-9, and their combination led to a significantly improved model (AUC ¼ 0.88, P ¼ 4E-8). Metabolites CR and NANA were significantly elevated in ICC when compared with HCC cases in both urine and tissue samples. High levels of CR were associated with poorer prognosis in ICC. Conclusions: Four metabolites are significantly increased in HCC and ICC and are robust at classifying ICC in combination with the clinically utilized marker CA19-9. Impact: Noninvasive urinary metabolite biomarkers hold promise for diagnostic and prognostic evaluation of ICC.

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Lipid Synthesis Is a Metabolic Liability of Non–Small Cell Lung Cancer

Cited by 34 publications

References 71 publications

Nrf2 Mediates Metabolic Reprogramming in Non-Small Cell Lung Cancer

Nrf2 Mediates Metabolic Reprogramming in Non-Small Cell Lung Cancer

Construction of a protein-based model for prognosis prediction of kidney renal clear cell carcinoma: an investigation based on functional proteomics data

Urinary Metabolites Diagnostic and Prognostic of Intrahepatic Cholangiocarcinoma

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