Single-cell RNA-seq and bulk RNA-seq explore the prognostic value of exhausted T cells in hepatocellular carcinoma

IF 1.9 4区 生物学 Q4 CELL BIOLOGY IET Systems Biology Pub Date : 2023-07-11 DOI:10.1049/syb2.12072
Xiaolong Tang, Yandong Miao, Lixia Yang, Wuhua Ha, Zheng Li, Denghai Mi
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Abstract

Hepatocellular carcinoma (HCC) remains a worldwide health problem. Mounting evidence indicates that exhausted T cells play a critical role in the progress and treatment of HCC. Therefore, a detailed characterisation of exhausted T cells and their clinical significance warrants further investigation in HCC. Based on the GSE146115, we presented a comprehensive single-cell Atlas in HCC. Pseudo-time analysis revealed that tumour heterogeneity progressively increased, and the exhausted T cells gradually appeared during tumour progression. Functional enrichment analysis revealed that the evolutionary process of exhausted T cells mainly contained the pathway of cadherin binding, proteasome, cell cycle, and T cell receptor regulation of apoptosis. In the International Cancer Genome Consortium database, we divided patients into three clusters with the T cell evolution-associated genes. We found that the exhausted T cells are significantly related to poor outcomes through immunity and survival analysis. In The Cancer Genome Atlas database, the authors enrolled weighted gene co-expression network analysis, univariate Cox analysis, and Lasso Cox analysis, then screened the 19 core genes in T cells evolution and built a robust prognostic model. This study offers a fresh view on evaluating the patients' outcomes from an exhausted T cells perspective and might help clinicians develop therapeutic systems.

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单细胞RNA-seq和大量RNA-seq探讨耗尽T细胞在肝癌中的预后价值
肝细胞癌(HCC)仍然是一个全球性的健康问题。越来越多的证据表明,耗竭的T细胞在HCC的进展和治疗中起着关键作用。因此,耗尽T细胞的详细特征及其在HCC中的临床意义值得进一步研究。基于GSE146115,我们提出了HCC的单细胞图谱。伪时间分析显示,肿瘤异质性逐渐增加,耗竭的T细胞在肿瘤进展过程中逐渐出现。功能富集分析显示,衰竭T细胞的进化过程主要包含钙粘蛋白结合、蛋白酶体、细胞周期、T细胞受体调控凋亡等途径。在国际癌症基因组联盟的数据库中,我们将患者与T细胞进化相关的基因分为三组。我们通过免疫和生存分析发现,耗竭的T细胞与不良预后显著相关。在The Cancer Genome Atlas数据库中,作者采用加权基因共表达网络分析、单变量Cox分析和Lasso Cox分析,筛选T细胞进化中的19个核心基因,建立稳健的预后模型。这项研究为从耗尽T细胞的角度评估患者的结果提供了一个新的观点,可能有助于临床医生开发治疗系统。
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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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