Heparanase/Syndecan-1 Axis Regulates the Grade of Liver Cancer and Proliferative Ability of Hepatocellular Carcinoma Cells

Yu, Shengjin; Lv, Huiming; Zhang, Jinhui; Zhang, He; Ju, Weiwei; Jiang, Yu; Lin, Lijuan

doi:10.32604/oncologie.2022.024882

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…The proportion of Epithelial cells in HCC tissues increased with the increase of clinical stage. We also detected two distinct fates of epithelial cells during the evolution from primary tumor to PVTT, one showing a trend toward progressively diminished immune activity and the other showing a trend toward increased proliferative and metabolic capacity of the cells similar to Heparanase/Syndecan-1 Axis (Yu et al, 2022). Epithelial cells during the progression from primary tumor to metastatic lymph Techniques such as single-cell omics in combination with molecular and functional studies will help reveal the remaining unknowns in this field (Gadd et al, 2020).…”

Section: Discussionmentioning

confidence: 79%

Insights on epithelial cells at the single-cell level in hepatocellular carcinoma prognosis and response to chemotherapy

Qi,

Zhang

2023

Front. Pharmacol.

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Background: Hepatocellular carcinoma (HCC) originates from Epithelial cells, and epithelial lineage plasticity has become a promising research direction for advancing HCC treatment. This study aims to focus on Epithelial cells to provide target insights for detecting HCC prognosis and response to drug therapy.Methods: Single-cell RNA sequencing (scRNA-seq) data from GSE149614 were clustered using Seurat, and the differentiation and evolution of epithelial cells were analyzed by Monocle 2. Scissor+ and Scissor− Epithelial cells associated with the prognostic phenotypes of bulk RNA-seq of HCC were screened using the Scissor algorithm for differential analysis to screen candidate genes. Candidate genes were overlapped with prognostic related genes screened by univariate Cox regression, and the Least Absolute Shrinkage and Selection Operator (LASSO) sparse penalty was imposed on the intersection genes to construct a risk assessment system.Results: Eight major cell subpopulations of HCC were identified, among which the proportion of epithelial cells in non-tumor liver tissues and HCC tissues was significantly different, and its proportion increased with advanced clinical stage. During the progression of HCC, the whole direction of epithelial cells differentiation trajectory was towards enhanced cell proliferation. Differential analysis between Scissor+ and Scissor− epithelial cells screened 1,265 upregulated and 191 downregulated prognostic candidate genes. Wherein, the upregulated genes were enriched in Cell processes, Genetic information processing, Metabolism and Human disease with Infection. Nevertheless, immune system related pathways took the main proportions in downregulated genes enriched pathways. There were 17 common genes between upregulated candidate genes and prognostic risk genes, of which CDC20, G6PD and PLOD2 were selected as components for constructing the risk assessment system. Risk score showed a significant correlation with tumor stage, epithelial-mesenchymal transition (EMT) related pathways and 22 therapeutic drugs, and was an independent prognostic factor for HCC.Conclusion: This study revealed the cellular composition of HCC, the differentiation evolution and functional landscape of epithelial cells in the further deterioration of HCC, and established a 3-gene risk model, which was closely related to clinical features, EMT, and drug sensitivity prediction. These findings provided insights in patient prognosis and drug therapy detection for HCC.

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Section: Discussionmentioning

confidence: 79%

Insights on epithelial cells at the single-cell level in hepatocellular carcinoma prognosis and response to chemotherapy

Qi,

Zhang

2023

Front. Pharmacol.

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“…The differentiation, indefinite proliferation, and self-renewal ability of CSCs are closely associated with the development, progression, and relapse of tumor and drug resistance ( 28 ). Earlier studies on HCC mostly focused on a single gene, ignoring the importance of multi-gene combination in predicting HCC ( 29 , 30 ). Chemoresistance and increased incidences of cancer relapse significantly increase the mortalities of patients with HCC.…”

Section: Discussionmentioning

confidence: 99%

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Chen,

Zou,

Wang

et al. 2024

Front. Immunol.

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ObjectiveSignificant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.MethodsWe retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the “one-class logistic regression (OCLR)” algorithm to obtain the mRNAsi of patients with HCC. We performed “unsupervised consensus clustering” to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient’s prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.ResultsWe screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient’s prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.ConclusionThe novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

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“…LIHC is a frequent primary cancer that seriously threatens the life and health of patients [ 1 , 51 ]. Target therapy and immunotherapy have improved the survival outcome of LIHC patients.…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients

Zhou,

Wu,

Cai

et al. 2024

Heliyon

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Heparanase/Syndecan-1 Axis Regulates the Grade of Liver Cancer and Proliferative Ability of Hepatocellular Carcinoma Cells

Cited by 6 publications

References 30 publications

Insights on epithelial cells at the single-cell level in hepatocellular carcinoma prognosis and response to chemotherapy

Insights on epithelial cells at the single-cell level in hepatocellular carcinoma prognosis and response to chemotherapy

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients

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