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Functional enrichment analysis of branch 2. (a) GO enrichment analysis revealed the primary biological function and processes (BP), cellular component (CC), and molecular function (MF). (b) KEGG enrichment analysis revealed the primary pathways. (c) The Bioplanet model of Enrichr showed the primary pathways. (d) Clue GO's immunoassay module revealed the main pathways of immunity.
Source publication
Hepatocellular carcinoma (HCC) remains a worldwide health problem. Mounting evidence indicates that exhausted T cells play a critical role in the progress and treatment of HCC. Therefore, a detailed characterisation of exhausted T cells and their clinical significance warrants further investigation in HCC. Based on the GSE146115, we presented a com...
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Background
Proteasome 26S subunit ATPase 2 (PSMC2) is a part of the 19S regulatory complex, which catalyzes the unfolding and transport of substrates into the 20S proteasome. Our previous research demonstrated that PSMC2 participates in the tumorigenesis and progression of pancreatic cancer (PC). However, no systematic analysis has been conducted t...
Citations
... Another study by Li et al. developed a model based on NK cell marker genes, demonstrating its efficacy in predicting patient outcomes and therapeutic responses [29]. Tang et al. (2023) explored the prognostic value of exhausted T cells, constructing a robust model from bulk and scRNA-seq data to predict patient survival [30]. However, most of these studies focused on the relationship between tumor and the tumor microenvironment (TME), which might cause researchers to overlook the impact of the characteristics of tumor cells themselves on tumor development. ...
... Another study by Li et al. developed a model based on NK cell marker genes, demonstrating its efficacy in predicting patient outcomes and therapeutic responses [29]. Tang et al. (2023) explored the prognostic value of exhausted T cells, constructing a robust model from bulk and scRNA-seq data to predict patient survival [30]. However, most of these studies focused on the relationship between tumor and the tumor microenvironment (TME), which might cause researchers to overlook the impact of the characteristics of tumor cells themselves on tumor development. ...
Background
Liver cancer has a high global incidence, particularly in East Asia. Early detection difficulties lead to poor prognosis. Single-cell sequencing precisely identifies gene expression differences in specific cell types, making it valuable in tumor microenvironment research and immune drug development. However, the characteristics of tumor cells themselves are equally important for patient prognosis and treatment.
Methods
We downloaded single-cell sequencing data from GSE189903, grouped cells by cluster markers, and classified epithelial cells into adjacent non-tumor, normal, and tumor cells. Differential gene and survival analyses identified significant differential genes. Using TCGA-LIHC data, we divided 370 patients into test and training sets. We constructed and validated a LASSO model based on these genes in both sets and two external datasets. Functional, immune infiltration, and mutation analyses were performed on high and low-risk groups. We also used RNA-seq and IC50 data of 15 liver cancer cell lines from GDSC, scoring them with our prognostic model to identify potential drugs for high-risk patients.
Results
Dimensionality reduction and clustering of 34 single-cell samples identified five subgroups, with epithelial cells further classified. Differential gene analysis identified 124 significant genes. An 11-gene prognostic model was constructed, effectively stratifying patient prognosis (p < 0.05) and achieving an AUC above 0.6 for 5 year survival prediction in multiple cohorts. Functional analysis revealed that upregulated genes in high-risk groups were enriched in cell adhesion pathways, while downregulated genes were enriched in metabolic pathways. Mutation analysis showed more TP53 mutations in the high-risk group and more CTNNB1 mutations in the low-risk group. Immune infiltration analysis indicated higher immune scores and less CD8 + naive T cell infiltration in the high-risk group. Drug sensitivity analysis identified 14 drugs with lower IC50 in the high-risk group, including clinically approved Sorafenib and Axitinib for treating unresectable HCC.
Conclusion
We established an 11-gene prognostic model that effectively stratifies liver cancer patients based on differentially expressed genes between tumor and adjacent non-tumor cells clustered by scRNA-seq data. The two risk groups had significantly different molecular characteristics. We identified 14 drugs that might be effective for high-risk HCC patients. Our study provides novel insights into tumor cell characteristics, aiding in research on tumor development and treatment.
