微血管侵犯（MVI）是肝细胞癌（HCC）的关键病理标志，与不良预后、早期复发和转移进展密切相关。然而，控制其发生和发展的精确机制基础仍然难以捉摸。在这项研究中，我们从 TCGA 和 HCCDB 存储库下载了批量 RNA-seq 数据，从 GEO 数据库下载了单细胞 RNA-seq 数据，以及从中信银行数据库。利用 Scissor 算法，我们描绘了与预后相关的细胞亚群，并辨别了一种独特的 MVI 相关恶性细胞亚型。通过伪时间分析和细胞间通讯审查对这些恶性细胞亚群进行了全面的探索。此外，我们设计了一个基于 MVI 相关基因的预后模型，在 TCGA 训练集上采用了 10 种机器学习算法集成的 101 种算法组合。采用C指数、校准曲线和决策曲线分析（DCA）对内部测试集和外部验证集进行严格评估。伪时间分析表明，恶性细胞与MVI呈正相关，主要集中在早中期分化阶段，与不良预后相关。重要的是，这些细胞在 MYC 通路中表现出显着富集，并通过 MIF 信号通路参与与不同细胞类型的广泛相互作用。通过空间转录组学数据的验证证实了恶性细胞与 MVI 表型的关联。我们设计的预后模型表现出卓越的敏感性和特异性，超越了大多数先前发布的模型的性能。校准曲线和DCA强调了该模型的临床实用性。通过综合多转录组学分析，我们描绘了MVI相关的恶性细胞并阐明了它们的生物学功能。这项研究为治疗 HCC 提供了新颖的见解，构建的预后模型为临床决策提供了宝贵的支持。版权所有 © 2024 Huang、Wu、Yu、Xu、Chen、Huo 和 Li。

Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinnings governing its onset and advancement remain elusive.In this research, we downloaded bulk RNA-seq data from the TCGA and HCCDB repositories, single-cell RNA-seq data from the GEO database, and spatial transcriptomics data from the CNCB database. Leveraging the Scissor algorithm, we delineated prognosis-related cell subpopulations and discerned a distinct MVI-related malignant cell subtype. A comprehensive exploration of these malignant cell subpopulations was undertaken through pseudotime analysis and cell-cell communication scrutiny. Furthermore, we engineered a prognostic model grounded in MVI-related genes, employing 101 algorithm combinations integrated by 10 machine-learning algorithms on the TCGA training set. Rigorous evaluation ensued on internal testing sets and external validation sets, employing C-index, calibration curves, and decision curve analysis (DCA).Pseudotime analysis indicated that malignant cells, showing a positive correlation with MVI, were primarily concentrated in the early to middle stages of differentiation, correlating with an unfavorable prognosis. Importantly, these cells showed significant enrichment in the MYC pathway and were involved in extensive interactions with diverse cell types via the MIF signaling pathway. The association of malignant cells with the MVI phenotype was corroborated through validation in spatial transcriptomics data. The prognostic model we devised demonstrated exceptional sensitivity and specificity, surpassing the performance of most previously published models. Calibration curves and DCA underscored the clinical utility of this model.Through integrated multi-transcriptomics analysis, we delineated MVI-related malignant cells and elucidated their biological functions. This study provided novel insights for managing HCC, with the constructed prognostic model offering valuable support for clinical decision-making.Copyright © 2024 Huang, Wu, Yu, Xu, Chen, Huo and Li.