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整合多组学分析揭示了一种具有生物学和临床相关性的新型肝细胞癌亚型。

Integrative multi-omics analysis reveals a novel subtype of hepatocellular carcinoma with biological and clinical relevance.

作者信息

Li Shizhou, Lin Yan, Gao Xing, Zeng Dandan, Cen Weijie, Su Yuejiao, Su Jingting, Zeng Can, Huang Zhenbo, Zeng Haoyu, Huang Shilin, Tang Minchao, Li Xiaoqing, Luo Min, Huang Zhihu, Liang Rong, Ye Jiazhou

机构信息

Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, ;China.

Department of Medical Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, ;China.

出版信息

Front Immunol. 2024 Dec 6;15:1517312. doi: 10.3389/fimmu.2024.1517312. eCollection 2024.

Abstract

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and the development of accurate predictive models for prognosis and drug sensitivity remains challenging.

METHODS

We integrated laboratory data and public cohorts to conduct a multi-omics analysis of HCC, which included bulk RNA sequencing, proteomic analysis, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics sequencing (ST-seq), and genome sequencing. We constructed a tumor purity (TP) and tumor microenvironment (TME) prognostic risk model. Proteomic analysis validated the TP-TME-related signatures. Joint analysis of scRNA-seq and ST-seq revealed characteristic clusters associated with TP high-risk subtypes, and immunohistochemistry confirmed the expression of key genes. We conducted functional enrichment analysis, transcription factor activity inference, cell-cell interaction, drug efficacy analysis, and mutation information analysis to identify a novel subtype of HCC.

RESULTS

Our analyses constructed a robust HCC prognostic risk prediction model. The patients with TP-TME high-risk subtypes predominantly exhibit hypoxia and activation of the Wnt/beta-catenin, Notch, and TGF-beta signaling pathways. Furthermore, we identified a novel subtype, XPO1+Epithelial. This subtype expresses signatures of the TP risk subtype and aligns with the biological behavior of high-risk patients. Additional analyses revealed that XPO1+Epithelial is influenced primarily by fibroblasts via ligand-receptor interactions, such as FN1-(ITGAV+ITGB1), and constitute a significant component of the TP-TME subtype. Moreover, XPO1+Epithelial interact with monocytes/macrophages, T/NK cells, and endothelial cells through ligand-receptor pairs, including MIF-(CD74+CXCR4), MIF-(CD74+CD44), and VEGFA-VEGFR1R2, respectively, thereby promoting the recruitment of immune-suppressive cells and angiogenesis. The ST-seq cohort treated with Tyrosine Kinase Inhibitors (TKIs) and Programmed Cell Death Protein 1 (PD-1) presented elevated levels of TP and TME risk subtype signature genes, as well as XPO1+Epithelial, T-cell, and endothelial cell infiltration in the treatment response group. Drug sensitivity analyses indicated that TP-TME high-risk subtypes, including sorafenib and pembrolizumab, were associated with sensitivity to multiple drugs. Further exploratory analyses revealed that CTLA4, PDCD1, and the cancer antigens MSLN, MUC1, EPCAM, and PROM1 presented significantly increase expression levels in the high-risk subtype group.

CONCLUSIONS

This study constructed a robust prognostic model for HCC and identified novel subgroups at the single-cell level, potentially assisting in the assessment of prognostic risk for HCC patients and facilitating personalized drug therapy.

摘要

背景

肝细胞癌(HCC)是一种高度异质性肿瘤,建立准确的预后和药物敏感性预测模型仍然具有挑战性。

方法

我们整合实验室数据和公共队列对HCC进行多组学分析,包括批量RNA测序、蛋白质组分析、单细胞RNA测序(scRNA-seq)、空间转录组测序(ST-seq)和基因组测序。我们构建了肿瘤纯度(TP)和肿瘤微环境(TME)预后风险模型。蛋白质组分析验证了与TP-TME相关的特征。scRNA-seq和ST-seq的联合分析揭示了与TP高风险亚型相关的特征簇,免疫组织化学证实了关键基因的表达。我们进行了功能富集分析、转录因子活性推断、细胞间相互作用、药物疗效分析和突变信息分析,以识别HCC的一种新亚型。

结果

我们的分析构建了一个强大的HCC预后风险预测模型。TP-TME高风险亚型的患者主要表现为缺氧以及Wnt/β-连环蛋白、Notch和TGF-β信号通路的激活。此外,我们识别出一种新亚型,即XPO1+上皮型。该亚型表达TP风险亚型的特征,与高风险患者的生物学行为一致。进一步分析表明,XPO1+上皮型主要通过配体-受体相互作用受成纤维细胞影响,如FN1-(ITGAV+ITGB1),并构成TP-TME亚型的重要组成部分。此外,XPO1+上皮型分别通过配体-受体对与单核细胞/巨噬细胞、T/NK细胞和内皮细胞相互作用,包括MIF-(CD74+CXCR4)、MIF-(CD74+CD44)和VEGFA-VEGFR1R2,从而促进免疫抑制细胞的募集和血管生成。用酪氨酸激酶抑制剂(TKIs)和程序性细胞死亡蛋白1(PD-1)治疗的ST-seq队列在治疗反应组中呈现出TP和TME风险亚型特征基因以及XPO1+上皮型、T细胞和内皮细胞浸润水平升高。药物敏感性分析表明,包括索拉非尼和派姆单抗在内的TP-TME高风险亚型与对多种药物的敏感性相关。进一步的探索性分析显示,CTLA4、PDCD1以及癌抗原MSLN、MUC1、EPCAM和PROM1在高风险亚型组中的表达水平显著升高。

结论

本研究构建了一个强大的HCC预后模型,并在单细胞水平上识别出新型亚组,可能有助于评估HCC患者的预后风险并促进个性化药物治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f0f/11659151/39237d8f2b95/fimmu-15-1517312-g001.jpg

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