机械诱导乳腺癌细胞癌变的动态蛋白质组学和乙酰组学分析。

IF 3.9 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Proteomics Pub Date : 2025-02-21 DOI:10.1002/pmic.202400409
Rong Han, Yue Wu, Yehong Yang, Qiaochu Wang, Tao Ding, Xutong Zhang, Juntao Yang
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引用次数: 0

摘要

与常规肿瘤细胞相比,癌症干细胞表现出高增殖、高转移潜能和显著增加的体内致瘤性等危险特性。尽管一些研究强调了微环境对细胞干性的影响,但它们在很大程度上忽略了由周围细胞外基质的刚度产生的机械力。我们之前的研究表明,在三维(3D)培养中,90pa软纤维蛋白基质可以诱导细胞成为高干性的癌症再生细胞。乙酰化修饰显著影响肿瘤细胞的代谢、表观遗传学、增殖、迁移和免疫逃避。在这项研究中,我们在不同刚度的二维(2D)平板和三维基质条件下对乳腺癌细胞的蛋白质组和乙酰蛋白质组进行了全面分析。该数据集为理解响应机械刚度的蛋白质乙酰化的动态调节提供了宝贵的资源。基于质谱的蛋白质组学数据已上传到ProteomeXchange Consortium,数据集标识符为PXD057820。
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Dynamic Proteomic and Acetylomic Profiling of Mechanically Induced Cancer Stemness in Breast Cancer Cells

Compared to regular tumor cells, cancer stem cells exhibit dangerous characteristics, including high proliferation, high metastatic potential, and significantly increased in vivo tumorigenicity. Although some studies have emphasized the impact of the microenvironment on cell stemness, they have largely overlooked the mechanical forces derived from the stiffness of the surrounding extracellular matrix. Our previous research demonstrated that a 90 Pa soft fibrin matrix in three-dimensional (3D) culture can induce cells to become cancer repopulating cells with high stemness. Acetylation modification significantly influences the metabolism, epigenetics, proliferation, migration, and immune evasion of tumor cells. In this study, we performed a comprehensive analysis of the proteome and acetyl-proteome of breast cancer cells under two-dimensional (2D) plate and 3D matrix conditions with varying stiffness. This dataset provides a valuable resource for understanding the dynamic regulation of protein acetylation in response to mechanical stiffness. The mass spectrometry-based proteomics data have been uploaded to the ProteomeXchange Consortium with the dataset identifier PXD057820.

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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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