ArticlePeerJ2024
Integrating single-cell and bulk sequencing data to identify glycosylation-based genes in non-alcoholic fatty liver disease-associated hepatocellular carcinoma.
Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Mendelian randomization analysis reveals no causal association between non-alcoholic fatty liver disease and hepatocellular carcinoma: implications for lipid metabolomics and shared pathophysiological mechanisms.Discover oncology · 2025Article
- Integrated multi-omics analysis and machine learning refine molecular subtypes and prognosis in hepatocellular carcinoma through O-linked glycosylation genes.Functional & integrative genomics · 2025Article
- RNA-seq analysis reveals transcriptome changes in livers fromBiochemistry and biophysics reports · 2025Article
- Comprehensive Bioinformatics Analyses and Experimental Validation of the Cell Cycle Related Protein SAPCD2 as a New Biomarker and Potential Therapeutic Target in Pancreatic Cancer.Journal of inflammation research · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
Background: The incidence of non-alcoholic fatty liver disease (NAFLD) associated hepatocellular carcinoma (HCC) has been increasing. However, the role of glycosylation, an important modification that alters cellular differentiation and immune regulation, in the progression of NAFLD to HCC is rare. Methods: We used the NAFLD-HCC single-cell dataset to identify variation in the expression of glycosylation patterns between different cells and used the HCC bulk dataset to establish a link between these variations and the prognosis of HCC patients. Then, machine learning algorithms were used to identify those glycosylation-related signatures with prognostic significance and to construct a model for predicting the prognosis of HCC patients. Moreover, it was validated in high-fat diet-induced mice and clinical cohorts. Results: The NAFLD-HCC Glycogene Risk Model (NHGRM) signature included the following genes: SPP1, SOCS2, SAPCD2, S100A9, RAMP3, and CSAD. The higher NHGRM scores were associated with a poorer prognosis, stronger immune-related features, immune cell infiltration and immunity scores. Animal experiments, external and clinical cohorts confirmed the expression of these genes. Conclusion: The genetic signature we identified may serve as a potential indicator of survival in patients with NAFLD-HCC and provide new perspectives for elucidating the role of glycosylation-related signatures in this pathologic process.
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