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三阴乳腺癌对紫杉醇敏感性中TTK、TPX2、UBE2C和LRP8的分子亚型分析及功能验证

Molecular subtyping and functional validation of TTK, TPX2, UBE2C, and LRP8 in sensitivity of TNBC to paclitaxel.

作者信息

Elango Ramesh, Vishnubalaji Radhakrishnan, Shaath Hibah, Alajez Nehad M

机构信息

Cancer Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), PO Box 34110, Doha 00000, Qatar.

College of Health & Life Sciences, Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar.

出版信息

Mol Ther Methods Clin Dev. 2021 Jan 26;20:601-614. doi: 10.1016/j.omtm.2021.01.013. eCollection 2021 Mar 12.

DOI:10.1016/j.omtm.2021.01.013
PMID:33665229
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7899947/
Abstract

Triple-negative breast cancer (TNBC) patients exhibit variable responses to chemotherapy, suggesting an underlying molecular heterogeneity. In the current study, we analyzed publicly available transcriptome data from 360 TNBC and 88 normal breast tissues, which revealed activation of nucleosome and cell cycle as the hallmarks of TNBC. Mechanistic network analysis identified activation of FOXM1 and ERBB2, and suppression of TP53 and NURP1 networks in TNBC. Employing Iterative Clustering and Guide-gene Selection (ICGS), Uniform Manifold Approximation and Projection (UMAP), and dimensionality reduction analyses, we classified TNBC into seven molecular subtypes, each exhibiting a unique molecular signature, including immune infiltration (CD19, CD8, and macrophages) and mesenchymal signature, which correlated with variable disease outcomes in a larger cohort (1,070) of BC. Mechanistically, depletion of , , , , , , , and led to substantial inhibition of colony formation of TNBC models, which was further enhanced in the presence of paclitaxel. Our data provide novel insights into the molecular heterogeneity of TNBC and identified , , , and as main drivers of TNBC tumorigenesis.

摘要

三阴性乳腺癌(TNBC)患者对化疗表现出不同的反应,这表明存在潜在的分子异质性。在本研究中,我们分析了来自360例TNBC和88例正常乳腺组织的公开转录组数据,结果显示核小体和细胞周期的激活是TNBC的特征。机制网络分析确定了TNBC中FOXM1和ERBB2的激活,以及TP53和NURP1网络的抑制。通过迭代聚类和引导基因选择(ICGS)、均匀流形逼近和投影(UMAP)以及降维分析,我们将TNBC分为七种分子亚型,每种亚型都表现出独特的分子特征,包括免疫浸润(CD19、CD8和巨噬细胞)和间充质特征,这与更大队列(1070例)乳腺癌的不同疾病结局相关。从机制上讲,对……的消耗导致TNBC模型的集落形成受到显著抑制,在紫杉醇存在的情况下这种抑制作用进一步增强。我们的数据为TNBC的分子异质性提供了新的见解,并确定……为TNBC肿瘤发生的主要驱动因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f3/7899947/499751896308/gr8.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f3/7899947/a9fdcd8a4126/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f3/7899947/e2e30268defa/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f3/7899947/0d9b88d9bc40/gr2.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f3/7899947/127874f89baf/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f3/7899947/45d8eeb0a959/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f3/7899947/988e3df6b49a/gr6.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37f3/7899947/499751896308/gr8.jpg

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