The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Cancer-cell-secreted exosomal miR-105 promotes tumour growth through the MYC-dependent metabolic reprogramming of stromal cells
Open accessAbstract
Cancer and other cells residing in the same niche engage various modes of interactions to synchronize and buffer the negative effects of environmental changes. Extracellular microRNAs (miRNAs) have recently been implicated in the intercellular crosstalk. Here we show a mechanistic model involving breast-cancer-secreted, extracellular-vesicle-encapsulated miR-105, which is induced by the oncoprotein MYC in cancer cells and, in turn, activates MYC signalling in cancer-associated fibroblasts (CAFs) to induce a metabolic program. This results in the capacity of CAFs to display different metabolic features in response to changes in the metabolic environment. When nutrients are sufficient, miR-105-reprogrammed CAFs enhance glucose and glutamine metabolism to fuel adjacent cancer cells. When nutrient levels are low and metabolic by-products accumulate, these CAFs detoxify metabolic wastes, including lactic acid and ammonium, by converting them into energy-rich metabolites. Thus, the miR-105-mediated metabolic reprogramming of stromal cells contributes to sustained tumour growth by conditioning the shared metabolic environment. Consistent crosstalk between cancer cells and stromal cells exists in the tumour microenvironment. Yan et al. show that exosomal miR-105 derived from cancer cells confers metabolic plasticity in recipient cancer-associated fibroblasts to adapt to nutrient-replete and -deplete conditions, thereby sustaining tumour growth.
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Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Valadi, H., Ekström, K., Bossios, A., Sjöstrand, M., Lee, J. J., & Lötvall, J. O. (2007) · Nature Cell Biology · doi:10.1038/ncb1596 · Cited by 12,834
Genome engineering using the CRISPR-Cas9 system
Ran, F. A., Hsu, P. D., Wright, J., Agarwala, V., Scott, D. A., & Zhang, F. (2013) · Nature Protocols · doi:10.1038/nprot.2013.143 · Cited by 11,133
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814
Causal analysis approaches in Ingenuity Pathway Analysis
Krämer, A., Green, J., Pollard, J., & Tugendreich, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt703 · Cited by 5,724
Skog, J., Würdinger, T., van Rijn, S., Meijer, D. H., Gainche, L., Curry, W. T., Carter, B. S., Krichevsky, A. M., & Breakefield, X. O. (2008) · Nature Cell Biology · doi:10.1038/ncb1800 · Cited by 5,001
The biology and function of fibroblasts in cancer
Kalluri, R. (2016) · Nature Reviews Cancer · doi:10.1038/nrc.2016.73 · Cited by 4,366
The Biology of Cancer: Metabolic Reprogramming Fuels Cell Growth and Proliferation
DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G., & Thompson, C. B. (2008) · Cell Metabolism · doi:10.1016/j.cmet.2007.10.002 · Cited by 4,088
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Metadata
- Authors
- Yan, W., Wu, X., Zhou, W., Fong, M. Y., Cao, M., Liu, J., Liu, X., Chen, C.-H., Fadare, O., Pizzo, D. P., Wu, J., Liu, L., Liu, X., Chin, A. R., Ren, X., Chen, Y., Locasale, J. W., & Wang, S. E.
- Venue
- Nature Cell Biology
- Published
- 2018-04-16
- Volume
- 20
- Issue
- 5
- Pages
- 597-609
- ISSN
- 1465-7392 (ISSN-L)
- 1476-4679
- OpenAlex
- W2797145001
- Cited by
- 469
- References
- 64
- FWCI
- 16.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Yan, W., Wu, X., Zhou, W., Fong, M. Y., Cao, M., Liu, J., Liu, X., Chen, C.-H., Fadare, O., Pizzo, D. P., Wu, J., Liu, L., Liu, X., Chin, A. R., Ren, X., Chen, Y., Locasale, J. W., & Wang, S. E. (2018). Cancer-cell-secreted exosomal miR-105 promotes tumour growth through the MYC-dependent metabolic reprogramming of stromal cells. Nature Cell Biology. https://doi.org/10.1038/s41556-018-0083-6
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