Targeting the transcription factor receptor LXR to treat clear cell renal cell carcinoma: agonist or inverse agonist?

Wu, Guangzhen; Wang, Qinglian; Xu, Yingkun; Li, Jianyi; Zhang, Hongge; Qi, Guanghui; Xia, Qinghua

doi:10.1038/s41419-019-1654-6

Cited by 62 publications

(61 citation statements)

References 64 publications

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“…In this context, LXR inhibition effectively impairs tumour growth by suppressing lipogenesis and glycolysis. Furthermore, the effects of both the LXR agonist LXR623 and the inverse agonist SR9243 on clear cell renal cell carcinoma have been examined 121 . Both drugs effectively decrease cancer cell proliferation and induce apoptosis.…”

Section: Enhancement Of Cd8 T Cell Function and Inhibition Of The Regmentioning

confidence: 99%

Cholesterol metabolism in cancer: mechanisms and therapeutic opportunities

2020

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C holesterol is vital for the survival and growth of mammalian cells. More than a membrane constituent, cholesterol is a precursor to bile acids and steroid hormones, which can initiate or promote colon, breast and prostate cancers 1-3 . Cholesterol can also modulate signalling pathways involved in tumourigenesis and cancer progression by covalently modifying proteins including hedgehog and smoothened 4,5 , and by facilitating the formation of specialized membrane microdomains 6,7 . Brief overview of cholesterol metabolismEvery mammalian cell can synthesize cholesterol through the mevalonate pathway (Fig. 1a). Two acetyl-CoA molecules in the cytosol condense, thus forming acetoacetyl-CoA, which reacts with the third acetyl-CoA and yields 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA). HMG-CoA is reduced to mevalonate by HMG-CoA reductase (HMGCR), the primary rate-limiting enzyme in cholesterol biosynthesis. A series of enzymatic reactions convert mevalonate to farnesyl pyrophosphate (FPP), a precursor of sterols and all non-sterol isoprenoids. The condensation of two FPP molecules to squalene commits the process to sterol production. FPP also gives rise to geranylgeranyl pyrophosphate (GGPP), and both FPP and GGPP can prenylate and activate several oncogenic proteins such as Ras 8 . Squalene is then oxidized by squalene epoxidase (SQLE) to 2,3-epoxysqualene, which is cyclized to lanosterol. In the next steps, lanosterol follows the Bloch pathway, the Kandutsch-Russell pathway or a hybrid pathway before it is finally converted to cholesterol.Beyond de novo cholesterol biosynthesis, most cells acquire cholesterol from low-density lipoprotein (LDL) taken up from the circulation via LDL receptor (LDLR)-mediated endocytosis 9 . Enterocytes absorb dietary cholesterol from the intestinal lumen in a process involving the cholesterol transporter NPC1L1, the clathrin adaptor NUMB and the adaptor protein LIMA1 (refs. 10-12 ). Cholesterol within the cell is dynamically transported, reaching the destined membranes for structural and functional needs 13 . Cholesterol in excess of the current cellular demand is either exported from the cell by ATP-binding cassette (ABC) transporters, Reprogrammed cholesterol metabolism in cancer cellsHallmark features of cancer cholesterol metabolism. As fastproliferating cells, cancer cells require high levels of cholesterol for membrane biogenesis and other functional needs. For example, the cholesterol-derived oncometabolite 6-oxo-cholestan-3β,5α-diol, which is enriched in patients with breast cancer, binds glucocorticoid receptors and subsequently promotes tumour growth 19 . In general, cholesterol metabolism substantially contributes to cancer progression, including cell proliferation, migration and invasion 20-23 .Cholesterol metabolism produces essential membrane components as well as metabolites with a variety of biological functions. In the tumour microenvironment, cell-intrinsic and cell-extrinsic cues reprogram cholesterol metabolism and consequently promote tumourigenesis. Cholesterol-de...

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Section: Enhancement Of Cd8 T Cell Function and Inhibition Of The Regmentioning

confidence: 99%

Cholesterol metabolism in cancer: mechanisms and therapeutic opportunities

2020

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“…The presence of apparent abnormalities in fatty acid metabolism in kidney cancer is recognized. In our previous studies, it was found that breaking the lipid homeostasis in this type of kidney cancer could significantly limit tumor progression [ 30 ]. Of course, many molecules clearly express correlations, and because of the limited space, they are not listed here.…”

Section: Resultsmentioning

confidence: 99%

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Han

et al. 2020

PPAR Research

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Objective. This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC). Methods. For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis. Results. We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients’ prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat. Conclusions. To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

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“…Subsequently, we found that most of these genes have statistical significance in the prognosis of patients with KIRC. Due to our previous research involving KIRC 17 , we focused on the potential role of these genes, particularly their mRNA expression, in KIRC and displayed this information in the form of heat maps and violin diagrams. We used the online tool of the GSCALite website to map the TRIM family genes and cancer pathway regulatory network.…”

Section: Introductionmentioning

confidence: 99%

Tripartite-motif family genes associated with cancer stem cells affect tumor progression and can assist in the clinical prognosis of kidney renal clear cell carcinoma

Wu¹,

Xu²,

Lin³

et al. 2020

Int. J. Med. Sci.

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Ubiquitination is presently a hot topic in the field of oncology. The tripartite-motif (TRIM) family of proteins represents one of the largest classes of putative single protein RING-finger E3 ubiquitin ligases, which play an essential role in the ubiquitination of proteins in the body. At the same time, research related to cancer stem cells (CSCs) is increasing in popularity in the field of oncology. CSCs are potentially chemically resistant and can be selectively enriched in patients receiving chemotherapy, ultimately leading to adverse outcomes, such as treatment failure and cancer recurrence. There is a close relationship between multiple TRIM family genes and CSCs. Accumulating evidence suggests that TRIM family proteins are expressed in diverse human cancers and act as regulators of oncoproteins or tumor suppressor proteins. In this study, we used biological information to explore the potential function of TRIM family genes related to CSCs in the development of pan-cancer. Kidney renal clear cell carcinoma (KIRC) is one of the deadliest malignant tumors in the world. Owing to its complex molecular and cellular heterogeneity, the effectiveness of existing KIRC-related risk prediction models is not satisfactory at present. Therefore, we focused on the potential role of these TRIM family genes in KIRC and used seven TRIM family genes to establish a prognostic risk model. This model includes TRIM16, TRIM32, TRIM24, TRIM8, TRIM27, PML, and TRIM11. In conclusion, this study provides further insight into the prognosis of KIRC, which may guide treatment.

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Targeting the transcription factor receptor LXR to treat clear cell renal cell carcinoma: agonist or inverse agonist?

Cited by 62 publications

References 64 publications

Cholesterol metabolism in cancer: mechanisms and therapeutic opportunities

Cholesterol metabolism in cancer: mechanisms and therapeutic opportunities

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Tripartite-motif family genes associated with cancer stem cells affect tumor progression and can assist in the clinical prognosis of kidney renal clear cell carcinoma

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