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整合单细胞RNA测序和批量RNA测序以鉴定肝细胞癌中与免疫原性细胞死亡相关的5基因预后特征

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing to Identify an Immunogenic Cell Death-Related 5-Gene Prognostic Signature in Hepatocellular Carcinoma.

作者信息

Peng Liqun, Xu Shaohua, Xu Jian-Liang

机构信息

Department of Hepatobiliary and Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, People's Republic of China.

Clinical Medicine Research Center for Minimally Invasive Procedure of Hepatobiliary & Pancreatic Diseases of Hubei Province, Wuhan, People's Republic of China.

出版信息

J Hepatocell Carcinoma. 2024 May 16;11:879-900. doi: 10.2147/JHC.S449419. eCollection 2024.

DOI:10.2147/JHC.S449419
PMID:38770169
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11104445/
Abstract

INTRODUCTION

Immunogenic cell death (ICD) can enhance the potency of immunotherapy in cancer treatment. Nevertheless, it is ambiguous how ICD-related genes (ICDRGs) contribute to hepatocellular carcinoma (HCC).

METHODS

Single-cell RNA sequencing (scRNA-seq) data were used to distinguish malignant cells from normal cells in the HCC tumor microenvironment(TME). Bulk RNA sequencing data was employed to acquire the landscape of the 33 ICDRGs. Unsupervised clustering identified two ICD molecular subtypes. The cellular infiltration characteristics and biological behavior in different subtypes were analyzed by ssGSEA. Subsequently, differentially expressed genes (DEGs) between the two subtypes were determined, based on which patients were classified into three gene clusters. Then, the prognostic model was constructed by Lasso-Cox analysis. Finally, we investigated the expression of risk genes in cancer cell line encyclopedia (CCLE) and validated the function of NKX3-2 in vitro experiments.

RESULTS

ICD scores and ICDRGs expression in malignant cells were significantly lower than in normal cells by scRNA-seq analysis. ICD-high subtype was characterized by ICD-related gene overexpression and high levels of immune infiltration abundance and immune checkpoints; Three DEGs-related gene clusters were likewise strongly linked to stromal and immunological activation. In the ICD-related prognostic model consisting of NKX3-2, CHODL, MMP1, NR0B1, and CTSV, the low-risk group patients had a better endpoint and displayed increased susceptibility to immunotherapy and chemotherapeutic drugs like 5-Fluorouracil, afatinib, bortezomib, cediratinib, lapatinib, dasatinib, gefitinib and crizotinib. Moreover, NKX3-2 amplification in HCC samples has been verified by experiments, and its disruption suppressed the proliferation and invasion of tumor cells.

CONCLUSION

Our study highlighted the potential of the ICDRGs risk score as a prognostic indicator to aid in the accurate diagnosis and immunotherapy sensitivity of HCC.

摘要

引言

免疫原性细胞死亡(ICD)可增强免疫疗法在癌症治疗中的效力。然而,ICD相关基因(ICDRGs)如何影响肝细胞癌(HCC)尚不清楚。

方法

利用单细胞RNA测序(scRNA-seq)数据区分HCC肿瘤微环境(TME)中的恶性细胞与正常细胞。采用批量RNA测序数据来获取33个ICDRGs的概况。无监督聚类识别出两种ICD分子亚型。通过单样本基因集富集分析(ssGSEA)分析不同亚型中的细胞浸润特征和生物学行为。随后,确定两种亚型之间的差异表达基因(DEGs),并据此将患者分为三个基因簇。然后,通过套索-考克斯(Lasso-Cox)分析构建预后模型。最后,我们研究了癌细胞系百科全书(CCLE)中风险基因的表达,并在体外实验中验证了NKX3-2的功能。

结果

通过scRNA-seq分析,恶性细胞中的ICD评分和ICDRGs表达显著低于正常细胞。ICD高亚型的特征是与ICD相关的基因过表达以及高水平的免疫浸润丰度和免疫检查点;三个与DEGs相关的基因簇同样与基质和免疫激活密切相关。在由NKX3-2、CHODL、MMP1、NR0B1和CTSV组成的ICD相关预后模型中,低风险组患者的终点更好,并且对免疫疗法和化疗药物(如5-氟尿嘧啶、阿法替尼、硼替佐米、塞迪替尼、拉帕替尼、达沙替尼、吉非替尼和克唑替尼)的敏感性增加。此外,实验已验证HCC样本中NKX3-2的扩增,其破坏抑制了肿瘤细胞的增殖和侵袭。

结论

我们的研究强调了ICDRGs风险评分作为预后指标在辅助HCC准确诊断和免疫治疗敏感性方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/31fccb06a8d6/JHC-11-879-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/ab0d53c3bf81/JHC-11-879-g0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/31fccb06a8d6/JHC-11-879-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/ab0d53c3bf81/JHC-11-879-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/e077acb213ff/JHC-11-879-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/4e14a34d08b8/JHC-11-879-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/a0330867459a/JHC-11-879-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/869b13163e36/JHC-11-879-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/17ea6e4e13e3/JHC-11-879-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/68412ef4ddd0/JHC-11-879-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/2784eb6f13c5/JHC-11-879-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2de/11104445/31fccb06a8d6/JHC-11-879-g0009.jpg

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