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癌症免疫衰老景观的多组学和单细胞特征分析。

Multi-omics and single cell characterization of cancer immunosenescence landscape.

机构信息

Department of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, 100191, China.

Gannan Medical University, Ganzhou, 341000, China.

出版信息

Sci Data. 2024 Jul 7;11(1):739. doi: 10.1038/s41597-024-03562-z.

DOI:10.1038/s41597-024-03562-z
PMID:38972884
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11228048/
Abstract

Cellular senescence (CS) is closely related to tumor progression. However, the studies about CS genes across human cancers have not explored the relationship between cancer senescence signature and telomere length. Additionally, single-cell analyses have not revealed the evolutionary trends of malignant cells and immune cells at the CS level. We defined a CS-associated signature, called "senescence signature", and found that patients with higher senescence signature had worse prognosis. Higher senescence signature was related to older age, higher genomic instability, longer telomeres, increased lymphocytic infiltration, higher pro-tumor immune infiltrates (Treg cells and MDSCs), and could predict responses to immune checkpoint inhibitor therapy. Single-cell analysis further reveals malignant cells and immune cells share a consistent evolutionary trend at the CS level. MAPK signaling pathway and apoptotic processes may play a key role in CS, and senescence signature may effectively predict sensitivity of MEK1/2 inhibitors, ERK1/2 inhibitors and BCL-2 family inhibitors. We also developed a new CS prediction model of cancer survival and established a portal website to apply this model ( https://bio-pub.shinyapps.io/cs_nomo/ ).

摘要

细胞衰老(CS)与肿瘤进展密切相关。然而,关于人类癌症中 CS 基因的研究尚未探讨癌症衰老特征与端粒长度之间的关系。此外,单细胞分析尚未揭示 CS 水平下恶性细胞和免疫细胞的进化趋势。我们定义了一个与 CS 相关的特征,称为“衰老特征”,并发现具有更高衰老特征的患者预后更差。更高的衰老特征与年龄较大、基因组不稳定性更高、端粒更长、淋巴细胞浸润增加、促肿瘤免疫浸润(Treg 细胞和 MDSC)更高有关,并且可以预测对免疫检查点抑制剂治疗的反应。单细胞分析进一步揭示了恶性细胞和免疫细胞在 CS 水平上具有一致的进化趋势。MAPK 信号通路和凋亡过程可能在 CS 中发挥关键作用,衰老特征可有效预测 MEK1/2 抑制剂、ERK1/2 抑制剂和 BCL-2 家族抑制剂的敏感性。我们还开发了一种新的癌症生存 CS 预测模型,并建立了一个门户网站来应用该模型(https://bio-pub.shinyapps.io/cs_nomo/)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/ce04047e0df5/41597_2024_3562_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/33bd0d7486eb/41597_2024_3562_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/da71c6234d8e/41597_2024_3562_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/1213413be9b9/41597_2024_3562_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/f1f3c0399b7b/41597_2024_3562_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/1e4c21b6b479/41597_2024_3562_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/ce04047e0df5/41597_2024_3562_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/33bd0d7486eb/41597_2024_3562_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/da71c6234d8e/41597_2024_3562_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/1213413be9b9/41597_2024_3562_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/f1f3c0399b7b/41597_2024_3562_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/1e4c21b6b479/41597_2024_3562_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1345/11228048/ce04047e0df5/41597_2024_3562_Fig6_HTML.jpg

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