ISENICS: a model for identifying senescent immune cells and samples and characterization of their roles in tumor microenvironment.

IF 7.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Miaomiao Tian, Hao Cui, Xinyu Wang, Huading Hu, Longlong Dong, Song Xiao, Changfan Qu, Peng Wang, Hui Zhi, Shangwei Ning, Yue Gao
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引用次数: 0

Abstract

Senescent immune cells secrete varied inflammatory factors that weaken the systemic anti-tumor ability and promote the proliferation and metastasis of tumor cells. Tumor cells could also accelerate the immune cellular senescence through diverse mechanisms. However, there has been a lack of indicators to quantify the senescence levels of different immune cell types. A model for Identifying Senescent Immune Cells and Samples was developed to explore the role of senescent immune cells in the tumor immune microenvironment (TIME). By integrating bulk and single-cell RNA-seq data, we constructed immune cell gene expression profiles for 23 cancer types using a deconvolution algorithm. By calculating the cellular senescence scores, we found that tumor samples exhibited higher senescence levels than normal samples. Monocytes/macrophages were prone to co-senescence with other cell subtypes. Differentially expressed genes in the high- and low-immune cellular senescence scores groups were enriched in the senescence pathway. Patients with higher levels of immunosenescence were associated with better prognosis. At the single-cell level, the number and strength of cell-to-cell interactions increased following immune cellular senescence in most cancers. Samples with senescent immune cells exhibited poorer immunotherapy response. Our study advances our understanding of senescent immune cells in the TIME, provides insights into cancer-specific relationships between immune cellular senescence and immune characteristics, and offers a model for identifying these senescent immune cells.

ISENICS:一个识别衰老免疫细胞和样本并表征其在肿瘤微环境中的作用的模型。
衰老的免疫细胞分泌多种炎症因子,削弱全身抗肿瘤能力,促进肿瘤细胞的增殖和转移。肿瘤细胞也可通过多种机制加速免疫细胞衰老。然而,一直缺乏指标来量化不同免疫细胞类型的衰老水平。建立衰老免疫细胞及样本鉴定模型,探讨衰老免疫细胞在肿瘤免疫微环境(TIME)中的作用。通过整合整体和单细胞RNA-seq数据,我们使用反卷积算法构建了23种癌症类型的免疫细胞基因表达谱。通过计算细胞衰老评分,我们发现肿瘤样本比正常样本表现出更高的衰老水平。单核/巨噬细胞易与其他细胞亚型共衰老。免疫细胞衰老高评分组和低评分组的差异表达基因在衰老途径中富集。免疫衰老水平越高的患者预后越好。在单细胞水平上,在大多数癌症中,随着免疫细胞衰老,细胞间相互作用的数量和强度增加。具有衰老免疫细胞的样品表现出较差的免疫治疗反应。我们的研究促进了我们对衰老免疫细胞在TIME中的认识,提供了免疫细胞衰老与免疫特性之间的癌症特异性关系的见解,并提供了识别这些衰老免疫细胞的模型。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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