PDF(Pubmed)
Abstract：
BACKGROUND: Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear.
METHODS: Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed.
RESULTS: We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis.
CONCLUSIONS: By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.
METHODS: Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed.
RESULTS: We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis.
CONCLUSIONS: By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.
摘要：
背景：肿瘤异质性在理解驱动肿瘤进展和转移的机制方面提出了巨大的挑战。肝细胞癌（HCC）在细胞水平上的异质性尚不清楚。
方法：进行单细胞RNA测序数据和空间转录组学数据的整合分析。多种方法用于研究HCC肿瘤细胞的亚型。功能特点,翻译因素,分析了不同亚型肿瘤细胞的临床意义和微环境相关性.分析亚型与成纤维细胞的相互作用。
结果：我们通过整合52个单细胞RNA测序数据和5个空间转录组学数据，建立了HCC恶性细胞的异质性景观。我们在肿瘤细胞中发现了三种亚型,包括ARG1+代谢亚型（Metab亚型），TOP2A+增殖表型（Prol-表型），和S100A6+前转移亚型（EMT亚型）。富集分析发现，这三种亚型具有不同的特征，那就是新陈代谢，增殖,和上皮-间质转化。轨迹分析显示Metab亚型和EMT亚型均起源于Prol表型。翻译因子分析发现EMT亚型显示SMAD3和TGF-β信号通路的排他性激活。以EMT亚型细胞为主的HCC具有不利的预后和废弃的微环境。我们发现了由SPP1-CD44和CCN2/TGF-β-TGFBR1相互作用对介导的肿瘤细胞和成纤维细胞之间的阳性环。抑制CCN2破坏了回路，减轻了向EMT子类型的转换，并抑制转移。
结论：通过建立恶性细胞的异质性景观，我们确定了HCC的三亚型分类。其中,S100A6+肿瘤细胞在转移中起着至关重要的作用。靶向肿瘤细胞和成纤维细胞之间的反馈回路是一种有希望的抗转移策略。
方法：进行单细胞RNA测序数据和空间转录组学数据的整合分析。多种方法用于研究HCC肿瘤细胞的亚型。功能特点,翻译因素,分析了不同亚型肿瘤细胞的临床意义和微环境相关性.分析亚型与成纤维细胞的相互作用。
结果：我们通过整合52个单细胞RNA测序数据和5个空间转录组学数据，建立了HCC恶性细胞的异质性景观。我们在肿瘤细胞中发现了三种亚型,包括ARG1+代谢亚型（Metab亚型），TOP2A+增殖表型（Prol-表型），和S100A6+前转移亚型（EMT亚型）。富集分析发现，这三种亚型具有不同的特征，那就是新陈代谢，增殖,和上皮-间质转化。轨迹分析显示Metab亚型和EMT亚型均起源于Prol表型。翻译因子分析发现EMT亚型显示SMAD3和TGF-β信号通路的排他性激活。以EMT亚型细胞为主的HCC具有不利的预后和废弃的微环境。我们发现了由SPP1-CD44和CCN2/TGF-β-TGFBR1相互作用对介导的肿瘤细胞和成纤维细胞之间的阳性环。抑制CCN2破坏了回路，减轻了向EMT子类型的转换，并抑制转移。
结论：通过建立恶性细胞的异质性景观，我们确定了HCC的三亚型分类。其中,S100A6+肿瘤细胞在转移中起着至关重要的作用。靶向肿瘤细胞和成纤维细胞之间的反馈回路是一种有希望的抗转移策略。
