Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma

• Published in: Frontiers in immunology Vol. 15; p. 1436131
• Main Authors: Huang, Haoran, Wu, Feifeng, Yu, Yang, Xu, Borui, Chen, Dehua, Huo, Yuwei, Li, Shaoqiang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 08.08.2024
• Subjects: Biomarkers, Tumor - genetics; Carcinoma, Hepatocellular - diagnosis; Carcinoma, Hepatocellular - genetics; Carcinoma, Hepatocellular - pathology; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; hepatocellular carcinoma; Humans; Immunology; Liver Neoplasms - diagnosis; Liver Neoplasms - genetics; Liver Neoplasms - pathology; Machine Learning; microvascular invasion; Microvessels - pathology; Neoplasm Invasiveness; Prognosis; prognostic model; scRNA-seq; spatial transcriptome; Transcriptome; tumor microenvironment; scRNA-seq; spatial transcriptome; tumor microenvironment; prognostic model; hepatocellular carcinoma; microvascular invasion
• DOI: 10.3389/fimmu.2024.1436131
• ISSN: 1664-3224

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.