Genomic and immune landscape in hepatocellular carcinoma: Implications for personalized therapeutics

ENVIRONMENTAL TOXICOLOGY(2023)

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摘要
Hepatocellular carcinoma (HCC) is a globally prevalent malignancy, marked by genetic heterogeneity and intricate tumor microenvironment interactions. In this study, we undertook a detailed single-cell analysis of six active HCC patients, highlighting strong correlations between gene expression levels and cellular characteristics. UMAP clustering revealed seven distinct cell categories with associated gene expressions. A divergence was observed in tumor cells into high and low cuproptosis groups, each associated with distinct pathways: oxidative stress for the high cuproptosis group and inflammatory and angiogenesis pathways for the low group. CellChat analysis on the TCGA-LIHC cohort displayed unique intercellular interactions among hepatocytes, T cells, and other cells, with pathways like COLLAGEN and VEGF being pivotal. Functional enrichment analyses exposed pathways enriched between cuproptosis groups, with KEGG emphasizing diseases like Parkinson's. COX survival analysis identified key prognostic genes, revealing distinct survival rates between risk groups in TCGA and GSE14520 cohorts. Mutation data highlighted missense mutations, with TTN, TP53, and CTNNB1 being the most mutated in HCC. Immune infiltration analysis via CIBERSORTx indicated differences between risk groups in NK cells, neutrophils, and other cells. Our drug sensitivity investigation showed significant correlations between model genes and drug responsiveness, emphasizing the importance of patient risk stratification for therapeutic approaches. Further, ATP6V1G1 was recognized in its role in apoptosis and migration in HCC cells. In conclusion, our findings illuminate the complexities of HCC progression, potential predictive genetic markers for drug response, and the pivotal role of ATP6V1G1, suggesting avenues for targeted therapeutic strategies in HCC.
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关键词
cuproptosis pathways, drug sensitivity, hepatocellular carcinoma, single-cell analysis
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