Prognostic Relevance of Neddylation-Related Genes in Hepatocellular Carcinoma.

Authors

Qiyue Sun, Guang Tan

Published in

Journal of hepatocellular carcinoma. Volume 13. Pages 610030. Epub Jul 11, 2026.

Abstract

Targeting neddylation offers a potential therapeutic strategy for hepatocellular carcinoma (HCC). This retrospective study aims to identify genes linked to HCC prognosis associated with neddylation through bioinformatics.
The research used publicly available datasets. Neddylation-related genes and survival-associated differentially expressed genes (DEGs) were intersected to identify key genes, which underwent enrichment analysis. Least absolute selection and shrinkage operator (LASSO)-Cox and multivariate Cox regression analyses were performed to identify prognostic genes and build a risk model. Potential mechanisms were explored via immune microenvironment and single-cell analysis. Finally, prognostic gene expression was validated in clinical tumor samples using real-time quantitative PCR (RT-qPCR).
Through methodical investigation, 62 intersection genes were found, mainly enriched in "DNA duplex unwinding" and "p53 signaling pathway". Six prognostic genes were further determined (SOCS2, DIRAS2, LPL, KRT17, BFSP1, and POF1B). RT-qPCR results confirmed the downregulation of SOCS2 and the upregulation of DIRAS2, LPL, KRT17, BFSP1, and POF1B in HCC. Risk prediction model based on these genes exhibited high predictive accuracy. Subsequently, analysis of the immunological microenvironment showed that the infiltration levels of 8 immune cell types varied significantly among risk categories. M0 macrophages had the highest negative correlation with SOCS2 and the strongest positive association with DIRAS2. Single-cell analysis showed that SOCS2 had higher expression levels in endothelial cells and significant differential expression in most cellular classifications.
Six prognosis-associated genes (SOCS2, DIRAS2, LPL, KRT17, BFSP1, and POF1B) as predictive biomarkers for HCC were identified, and a high-accuracy risk model was developed, providing predictive and prognostic references for HCC risk stratification and clinical management.

PMID:
42460258
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.

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