Exploring tumor endothelial cells heterogeneity in hepatocellular carcinoma: insights from single-cell sequencing and pseudotime analysis

• Published in: PeerJ (San Francisco, CA) CA), 2024-10, Vol.12, p.e18362, Article e18362
• Main Authors: Sun, Jiachun, Zhang, Shujun, Liu, Yafeng, Liu, Kaijie, Gu, Xinyu
• Format: Article
• Language: English
• Subjects: B cells; Bioinformatics; Carcinoma, Hepatocellular - genetics; Carcinoma, Hepatocellular - pathology; Cell Biology; Cell Proliferation - genetics; Copy number variation; Development and progression; DNA Copy Number Variations - genetics; Endothelial Cells - metabolism; Endothelial Cells - pathology; Endothelium; Gene Expression Regulation, Neoplastic; Genes; Genetic transcription; Genomes; Genomics; Health aspects; Hepatocellular carcinoma; Hepatoma; Humans; Liver Neoplasms - genetics; Liver Neoplasms - pathology; Medical research; Medicine, Experimental; Oncology; Platelet-derived growth factor; Pseudotime trajectory analysis; RNA; RNA sequencing; Sequence Analysis, RNA; Single-Cell Analysis - methods; Single-cell RNA sequencing; Tumor endothelial cells
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.18362

This study aimed to explore the heterogeneity of tumor endothelial cells (TECs) in hepatocellular carcinoma (HCC) and their role in tumor progression, with the goal of identifying new therapeutic targets and strategies to improve patient prognosis. Single-cell RNA sequencing data from nine primary liver cancer samples were analyzed, obtained from the Gene Expression Omnibus (GEO) database. Data preprocessing, normalization, dimensionality reduction, and batch effect correction were performed based on the Seurat package. HCC cell types were identified using uniform manifold approximation and projection (UMAP) and cluster analysis, and the different cell types were annotated using the CellMarker database. Pseudotime trajectory analysis was conducted with Monocle to explore the differentiation trajectory of TECs. MAPK signaling pathway activity and copy number variations (CNV) in TECs were analyzed in conjunction with data from The Cancer Genome Atlas (TCGA), the trans-well and wound healing assay was used for cell invasion and migration activity assessment. Two subgroups of TECs (TECs 1 and TECs 2) were identified, exhibiting distinct functional activities and signaling pathways. Specifically, TECs 1 may be involved in tumor cell proliferation and inflammatory responses, whereas TECs 2 is not only involved in cell proliferation pathways, but also enriched in pathways such as metabolic synthesis. Pseudotime analysis revealed dynamic changes in TECs subgroups during HCC progression, correlating specific gene expressions (such as PDGFRB, PGF, JUN, and NR4A1). Subsequently, the JUN gene was predicted by performing binding sites and was shown to act as a transcription factor that may regulate the expression of the PGF gene. CNV analysis highlighted key genes and pathways in TECs that might influence HCC progression, and the PGF as key regulatory factor mediated cell proliferation and migration. The study revealed the heterogeneity of TECs in HCC and their potential roles in tumor progression, offering new perspectives and potential therapeutic targets for HCC molecular mechanisms. The findings emphasize the importance of further exploring TECs heterogeneity for understanding HCC pathogenesis and developing personalized treatment strategies.