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. 2025 Aug 7;16(1):1488.
doi: 10.1007/s12672-025-03339-9.

Prognostic and immunotherapeutic response prediction in hepatocellular carcinoma: role of non-histone acetylation/deacetylation scoring

Jizhen Li #  1 , Sisi Huang #  2 , Fangling Yuan  1 , Xunjun Yang  1 , Qipeng Xie  3
Affiliations

Prognostic and immunotherapeutic response prediction in hepatocellular carcinoma: role of non-histone acetylation/deacetylation scoring

Jizhen Li et al. Discov Oncol. .

Abstract

Background: Post-translational modification is crucial, with acetylation and deacetylation enzymes playing important roles. Their roles in liver cancer, however, remain unclear.

Methods: In this study, a KAT + KDAC score was developed to assess the prognosis and response to immunotherapy in liver cancer patients. The association of HDAC1, HDAC2, and HDAC3 with patient prognosis was analyzed, and the PPI network was constructed. The correlation between the score and P53 mutation status, stage, and TNM staging was also investigated. Additionally, the response to chemotherapy was evaluated. The TIDE score was also assessed.

Results: Our analysis showed that HDAC1, HDAC2, and HDAC3 are highly associated with patient prognosis and are centrally located in the PPI network. Patients with high KAT + KDAC scores had a better prognosis, and the score correlated with P53 mutation status, stage, and TNM staging. Notably, patients with low KAT + KDAC scores demonstrate resistance to cisplatin and gemcitabine, and have a lower TIDE score. Moreover, the KAT + KDAC score accurately predicted patient response to immunotherapy.

Conclusion: The KAT + KDAC modification pattern is critical in the initiation and progression of liver cancer. This score can be used to predict patient prognosis and immunotherapy response. Future studies are needed to further explore the underlying mechanisms and potential therapeutic applications.

Keywords: Acetylation; Consistent clustering; Deacetylation; Immunotherapy; Prognosis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This retrospective study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (Date 2025-04-09/No. KY2025-R120). Informed consent: Given the retrospective nature of the study and the use of anonymized data, the requirement for written informed consent was waived by the Ethics Committee. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart providing an overview of the study design and workflow
Fig. 2
Fig. 2
Genetic variation of acetylated and deacetylated genes in HCC. a Box plots showing differences in the mRNA expression of acetylated and deacetylated genes between HCC samples and normal control tissue samples in the TCGA database. b Gene mutation landscape of acetylated and deacetylated genes. c Frequency of acetylated and deacetylated gene CNVs. Blue indicates deletion. Orange indicates amplification. The sample size was 379. d Chromosomal position of acetylated and deacetylated genes. e PCA of acetylated and deacetylated genes in TCGA samples. nsP >0.05; *P < 0.05; ***P < 0.001; ****P < 0.0001
Fig. 3
Fig. 3
Identification of key acetylated and deacetylated genes in HCC. a PPI network of acetylated and deacetylated genes. b The top ten hub genes in the PPI network with the highest degrees of connectivity. c Forest plot showing the relationship between acetylated and deacetylated genes and the prognosis of liver cancer. Bold genes were significantly associated with prognosis. d Venn diagram showing that HDAC1, HDAC2, and HDAC3 are the only three PPI network hub genes significantly associated with prognosis. e–g qRT-qPCR analysis of HDAC1, HDAC2 and HDAC3 expression in LO2 and LM3 cells. h Western blot analysis of HDAC1, HDAC2, and HDAC3 expression in LO2 and LM3 cells. i–k HDAC1, HDAC2, and HDAC3 protein expression in liver and normal tissues were analyzed by IHC staining. *P < 0.05
Fig. 4
Fig. 4
Unsupervised clustering of acetylated and deacetylated genes in HCC. a Interactions between acetylated and deacetylated genes. Circle size represents the effect of each gene on survival prediction. Green dots represent prognostic protective factors. Black dots represent prognostic risk factors. Connecting lines indicate interactions. Negative correlations are marked in blue. Positive correlations are marked in red. b Consistent clustering of acetylated and deacetylated genes. 1 and 2 indicate two subgroups. c Kaplan-Meier curve of the 2 KAT + KDAC clusters. d KEGG analysis of biological pathways related to different KAT + KDAC clusters. e Differences in the infiltration levels of 28 immune cells between the two groups. f Differential cell prognosis analysis. nsP >0.05; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001
Fig. 5
Fig. 5
Differences in clinical characteristics between the two KA + KDAC.clusters. a TP53 mutation status; b stage; c T stage; and d age. e Differences in enrichment score between the two KAT + KDAC.clusters. nsP >0.05; *P < 0.05; ***P < 0.001; ****P < 0.0001
Fig. 6
Fig. 6
Heatmap of acetylated and deacetylated gene expression. Heatmap showing differences in the mRNA expression of acetylated and deacetylated genes between the two groups
Fig. 7
Fig. 7
KAT + KDAC phenotype-related DEGs in HCC. a Unsupervised clustering based on acetylation and deacetylation-related genes in the KAT + KDAC.geneclusterA, KAT + KDAC.geneclusterB, and KAT + KDAC.geneclusterC groups. b KEGG enrichment analysis of DEGs. c Survival analysis of the three groups. d Expression status of 26 acetylation and deacetylation genes in three gene clusters. nsP >0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001
Fig. 8
Fig. 8
Construction of KAT + KDAC.score. a Changes in KAT + KDAC clusters, gene clusters, and KAT + KDAC.score. b High and low KAT + KDAC.score grouping has a significant association with overall survival. c Correlation between KAT + KDAC.score and known gene signatures in liver cancer. Negative correlations are marked in blue. Positive correlations are marked in red. X indicates that the correlation is not significant. Higher values indicate greater significance. d Differences in tumor-related biological activity scores between high and low KAT + KDAC.score groups. e KAT + KDAC.score in the KAT + KDAC.cluster. f KAT + KDAC.score distributio STRING-db.orgn in the KAT + KDAC.genecluster. nsP >0.05; **P < 0.01; ****P < 0.0001
Fig. 9
Fig. 9
Correlation between KAT + KDAC.score and clinical features. a–e KAT + KDAC.score distribution in different classification subgroups. f Significant difference in survival between KAT + KDAC.score high and low groups in TCGA samples
Fig. 10
Fig. 10
Molecular characteristic analysis of KAT + KDAC.score high and low TCGA data sets. a, b Gene mutation distribution in KAT + KDAC.score high-low groups. c, d Copy number amplification and deletion region distribution in the KAT + KDAC.score high (c) and low (d) groups
Fig. 11
Fig. 11
Immunotherapy response in the KAT + KDAC.score high and low groups. a, b Difference between the IC50 values of cisplatin and gemcitabine in the KAT + KDAC.score high and low groups. c TIDE scores in the KAT + KDAC.score high and low groups. d Ability of the KAT + KDAC.score to predict the response to immunotherapy
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