journal article·2021-08

Graphene promotes lung cancer metastasis through Wnt signaling activation induced by DAMPs

Zhenfei Bi, Lu Li, Jingyun Yang, Xue Li, Fei Mo, Tianqi Lu, Yanlin Song, Zesheng Cheng, Qianmei Fu, Hong Lei, Xuemei He, Yuquan Wei, Xiawei Wei

Nano Today· Volume 39
Publisher page

Abstract

Abstract Graphene has important application prospects in many fields, and is considered a revolutionary material in the future. The biocompatibility of graphene and its safety issues have drawn much attention. Possible toxicities of a nanomaterial might be concerned with cell deaths, releasing of damage-associated molecular patters (DAMPs), inflammation, risks of tumorigenesis etc. In the current study, we investigated the long-term pulmonary exposure model of graphene and carbon black and discovered that long-term pulmonary exposure of the materials led to lung cancer metastasis and progression. Notably, pulmonary exposure of graphene or carbon black induced cell necrosis and the release of the DAMP, namely, mitochondrial DNA (mtDNA), which is a potent stimulator for elevating the secretion of Wnt ligands in alveolar macrophages (AMs). The Wnt-activating macrophages contributed to the formation of premetastatic niches in lung tissues by enhancing the stemness potential of cancer cells and promoting their generation and proliferation. The growth of metastatic cancer induced by exposure of carbon-based nanomaterials could be effectively inhibited by treating mice with a Wnt inhibitor. Taken together, our findings elucidate the how the pulmonary cell deaths induced by graphene reshaped the tumor microenvironment by releasing DAMPs and promoting tumor metastasis, which throws the light on the relationship between nanomaterial toxicity and its tumorigenesis risk.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Graphene and Nanomaterials Applications · Nanoparticles: synthesis and applications · Carbon and Quantum Dots Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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