Hepatoblastoma is the most common primary malignant liver tumor in infants and children with multiple differentiation patterns, mainly occurring in the first two years of life[1, 26]. Here we downloaded the GEO dataset (GSE133039, GSE180664) to study differential gene expression in hepatoblastoma at the transcriptional level, performed differential gene enrichment analysis as well as PPI reciprocal network analysis, and screened 50 hubgene. Followed by single-cell transcriptome analysis to classify hepatoblastoma into 11 cell subgroups. The cell-to-cell communication was analyzed, and the cell communication target LFNG was screened together with the transcriptomic data, and finally LFNG was validated as a key target for cell communication.
Using the GEO dataset, differentially expressed genes were obtained, PPI network analysis was performed and hubgene was obtained has been reported. In hepatoblastoma, a random forest classifier was established using the GEO database study, which yielded 10 core genes. (CDK1, TOP2A, ADRA1A, FANCI, XRCC1, TPX2, CCNB2, CDK4, GLYATL1 and CFHR3), which contribute to the diagnosis, prediction and targeted treatment of HB[27]. In hepatocellular carcinoma, the hubgene (COLEC10, TGFBR3 and FYN) was obtained by combining TCGA and GEO datasets. Finally it was clear that FYN expression was positively correlated with the prognosis of HCC patients[28]. In gastric cancer, three hubgene genes (ATP4A, ATP4B, ESRRG ) were screened using TCGA and GEO databases for analysis, and a diagnostic model for gastric cancer was established[29]. A risk-prognosis model of immune-related genes in colorectal cancer was constructed using a method using raw letter analysis to provide new molecular markers for predicting the immune microenvironment of colorectal cancer[30]. We used GEO datasets (GSE133039, GSE180664), performed differential expression analysis with GO and KEGG enrichment analysis, constructed PPI network, and screened 50 hubgene for further analysis. The altered immune microenvironment of differential genes was further investigated, and differential genes were found to be significantly associated with macrophages and endothelial cells.
The tumor ecosystem consists of multiple cell types, with different cells communicating through ligand-receptor interactions. Using single-cell sequencing to clarify intercellular communication and targeting ligand-receptor interactions can prolong the survival of tumor patients[31]. A new prognostic risk model has been developed based on the classification of lung adenocarcinoma into different molecular subtypes based on genes related to intercellular communication, providing a new idea for prognostic assessment of lung adenocarcinoma[32]. Studies have also analyzed single-cell transcriptome data of lung adenocarcinoma for intercellular communication between cell subpopulations and found functionally significant interactions between lung adenocarcinoma cells and T cells[33]. In studies of rectal cancer, interactions between epithelial cells and other cells influence tumor progression, which may provide potential targets for activating or blocking cellular communication[34]. Cellular communication analysis of the single cell transcriptome of hepatocellular carcinoma revealed interactions between hepatocytes and endothelial cells, which play a key role in tumor angiogenesis in the development of hepatocellular carcinoma[35]. In our study, endothelial cells are significantly associated with macrophages and monocytes and can signal through the VISFATIN signaling pathway, which may provide new insights into the treatment of hepatoblastoma.
LFNG is a protein-encoding gene that mediates the activity of glycosyltransferases, which initiate the extension of O-linked rockulose residues attached to EGF-like repeats in the extracellular structural domain of Notch molecules. In colorectal cancer, TGFBR2 signaling was found to affect Notch1 glycosylation by regulating glycosyltransferase LFNG expression, thereby shadowing colorectal progression[36]. In melanoma studies, LFNG expression was found to play an important role in the regulation of melanoma metastasis[37]. The absence of EOGT or LFNG expression inhibited the proliferation and migration of pancreatic cancer cells as observed by inhibiting Notch activation, EOGT expression was significantly increased in the basal subtype, and low expression of EOGT and LFNG predicted better overall survival in PDAC patients[38]. We found that in hepatoblastoma, LFNG acts as a key target for cellular communication and influences the progression of hepatoblastoma. LFNG affects the tumor microenvironment, especially among endothelial cells, MDSC and NK cells with significant effects.
In this study, we also have some shortcomings in not further investigating the direct targets of cell communication in hepatoblastoma cells, as well as not further investigating LFNG. In the next study, we will further investigate the direct targets and direct mechanisms of cellular communication, especially the experimental validation of LFNG in our experiments.
In conclusion, in this study, we performed differential expression analysis of transcriptomic data from hepatoblastoma by GEO dataset, enrichment analysis, and screened 50 hubgene for further study. The joint single cell transcriptome data was analyzed for communication between cell subpopulations, and LFNG was clarified as a target for intercellular communication, and LFNG was validated.