Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Post-translational acylation modulates immunosuppression and immunotherapy efficacy in hepatocellular carcinoma

Genes & Immunity (2025)Cite this article

Subjects

Abstract

Acylation modification plays a crucial role in modulating hepatocellular carcinoma (HCC) progression, and their specific prognostic implications in HCC have not been thoroughly investigated. Eleven acylation modifications (crotonylation, lactylation, succinylation, benzoylation, butyrylation, malonylation, glutarylation, 2-hydroxyisobutyrylation, β-hydroxybutyrylation, palmitoylation, myristoylation, and prenylation) were generated consensus cluster. Then, WGCNA was utilized to identify module genes. Finally, machine learning approach was employed to create acylation modification related genes.score (AMRG.score). This analysis revealed two distinct subtypes of AMRG, each characterized by unique molecular signatures. Through the combination of DEGs, DEGs associated with prognosis, and WGCNA, a total of 21 key genes were identified, leading to the creation of AMRG.score. AMRG.score was rigorously validated across independent external cohorts (TCGA-LIHC, LIRI-JP, GSE10143, GSE14520, GSE27150, GSE36376, and GSE76427) and an in-house cohort, demonstrating its reliability and potential applicability. The AMRG.score serves a dual purpose in its application, as it encapsulates essential the clinical context and offers valuable insights regarding the immunotherapy. In particular, patients categorized with a high AMRG.score displayed an active TME and sensitive to immunotherapy. This novel acylation modification-related prognostic signature could effectively assess the prognosis and therapeutic responses of HCC patients, providing new perspectives for individualized treatment for the patient population.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2: Identification of molecular subtypes of HCC.
Fig. 3: Characteristics of the immune landscape in molecular subtypes.
Fig. 4: Identification of key module genes associated with molecular subtypes.
Fig. 5: Construction of AMRG.score.
Fig. 6: Characteristics of AMRG.score.
Fig. 7: Model evaluation of AMRG.score.
Fig. 8: Characteristics of the genomic alterations in molecular subtypes.
Fig. 9: Predictive value of the AMRG.score in immunotherapy response and drug response.
Fig. 10: ST analysis of AMRG.score.
Fig. 11: Validation of AMRG.score in house cohort.

Similar content being viewed by others

Data availability

The datasets generated for this study can be found in the GEO database (GSE10143, GSE14520, GSE27150, GSE36376, and GSE76427, GSE91061, GSE78220, Van Allen, and Nathanson; https://www.ncbi.nlm.nih.gov/geo/), and UCSC Xena website (https://gdc.xenahubs.net).

References

  1. Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA: A Cancer J Clin. 2025;75:10–45.

    Google Scholar 

  2. Brown ZJ, Tsilimigras DI, Ruff SM, Mohseni A, Kamel IR, Cloyd JM, et al. Management of hepatocellular carcinoma: a review. JAMA Surg. 2023;158:410–20.

    Article  PubMed  Google Scholar 

  3. Vogel A, Meyer T, Sapisochin G, Salem R, Saborowski A. Hepatocellular carcinoma. Lancet (Lond, Engl). 2022;400:1345–62.

    Article  CAS  Google Scholar 

  4. Yu SJ. Immunotherapy for hepatocellular carcinoma: Recent advances and future targets. Pharm Ther. 2023;244:108387.

    Article  CAS  Google Scholar 

  5. Rimassa L, Finn RS, Sangro B. Combination immunotherapy for hepatocellular carcinoma. J Hepatol. 2023;79:506–15.

    Article  PubMed  CAS  Google Scholar 

  6. Donne R, Lujambio A. The liver cancer immune microenvironment: therapeutic implications for hepatocellular carcinoma. Hepatol (Balt, Md). 2023;77:1773–96.

    Article  Google Scholar 

  7. Ma P, Zou C, Xia S. Oncogenic signaling pathway mediated by Notch pathway-related genes induces immunosuppression and immunotherapy resistance in hepatocellular carcinoma. Immunogenetics. 2022;74:539–57.

    Article  PubMed  CAS  Google Scholar 

  8. Li X, Liu S, Zou L, Dai M, Zhu C. RNA processing modification mediated subtypes illustrate the distinctive features of tumor microenvironment in hepatocellular carcinoma. Genes Immun. 2024;25:132–48.

    PubMed  CAS  Google Scholar 

  9. Shang S, Liu J, Hua F. Protein acylation: mechanisms, biological functions and therapeutic targets. Signal Transduct Target Ther. 2022;7:396.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Jiang G, Li C, Lu M, Lu K, Li H. Protein lysine crotonylation: past, present, perspective. Cell Death Dis. 2021;12:703.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Wan J, Liu H, Chu J, Zhang H. Functions and mechanisms of lysine crotonylation. J Cell Mol Med. 2019;23:7163–9.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wang S, Mu G, Qiu B, Wang M, Yu Z, Wang W, et al. The function and related diseases of protein crotonylation. Int J Biol Sci. 2021;17:3441–55.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Yu J, Chai P, Xie M, Ge S, Ruan J, Fan X, et al. Histone lactylation drives oncogenesis by facilitating m(6)A reader protein YTHDF2 expression in ocular melanoma. Genome Biol. 2021;22:85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Liao L, He Y, Li SJ, Yu XM, Liu ZC, Liang YY, et al. Lysine 2-hydroxyisobutyrylation of NAT10 promotes cancer metastasis in an ac4C-dependent manner. Cell Res. 2023;33:355–71.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Shi H, Cui W, Qin Y, Chen L, Yu T, Lv J. A glimpse into novel acylations and their emerging role in regulating cancer metastasis. Cell Mol Life Sci. 2024;81:76.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Nagaraju GP, Dariya B, Kasa P, Peela S, El-Rayes BF. Epigenetics in hepatocellular carcinoma. Semin cancer Biol. 2022;86:622–32.

    Article  PubMed  CAS  Google Scholar 

  17. Hoshida Y, Villanueva A, Kobayashi M, Peix J, Chiang DY, Camargo A, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med. 2008;359:1995–2004.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Wu B, Liu DA, Guan L, Myint PK, Chin L, Dang H, et al. Stiff matrix induces exosome secretion to promote tumour growth. Nat cell Biol. 2023;25:415–24.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Lim HY, Sohn I, Deng S, Lee J, Jung SH, Mao M, et al. Prediction of disease-free survival in hepatocellular carcinoma by gene expression profiling. Ann Surg Oncol. 2013;20:3747–53.

    Article  PubMed  Google Scholar 

  20. Grinchuk OV, Yenamandra SP, Iyer R, Singh M, Lee HK, Lim KH, et al. Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma. Mol Oncol. 2018;12:89–113.

    Article  PubMed  CAS  Google Scholar 

  21. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinforma (Oxf, Engl). 2012;28:882–3.

    CAS  Google Scholar 

  22. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Sci (N Y, N Y). 2015;350:207–11.

    Article  Google Scholar 

  23. Nathanson T, Ahuja A, Rubinsteyn A, Aksoy BA, Hellmann MD, Miao D, et al. Somatic mutations and neoepitope homology in melanomas treated with CTLA-4 blockade. Cancer Immunol Res. 2017;5:84–91.

    Article  PubMed  CAS  Google Scholar 

  24. Ulloa-Montoya F, Louahed J, Dizier B, Gruselle O, Spiessens B, Lehmann FF, et al. Predictive gene signature in MAGE-A3 antigen-specific cancer immunotherapy. J Clin Oncol: J Am Soc Clin Oncol. 2013;31:2388–95.

    Article  CAS  Google Scholar 

  25. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165:35–44.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell. 2017;171:934–949.e16.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554:544–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Lu X, Meng J, Zhou Y, Jiang L, Yan F. MOVICS: an R package for multi-omics integration and visualization in cancer subtyping. Bioinforma (Oxf, Engl). 2021;36:5539–41.

    Google Scholar 

  29. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innov (Camb (Mass)). 2021;2:100141.

    CAS  Google Scholar 

  30. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008;9:559.

    Article  Google Scholar 

  31. Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y, et al. IOBR: multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front Immunol. 2021;12:687975.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Maeser D, Gruener RF, Huang RS. OncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22:bbab260.

  33. Oh M, Park S, Kim S, Chae H. Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations. Brief Bioinforma. 2021;22:66–76.

    Article  CAS  Google Scholar 

  34. Ma C, Wu M, Ma S. Analysis of cancer omics data: a selective review of statistical techniques. Brief Bioinform 2022;23:bbab585.

  35. Wu X, Zhou Z, Cao Q, Chen Y, Gong J, Zhang Q, et al. Reprogramming of Treg cells in the inflammatory microenvironment during immunotherapy: a literature review. Front Immunol. 2023;14:1268188.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Bittner S, Hehlgans T, Feuerer M. Engineered Treg cells as putative therapeutics against inflammatory diseases and beyond. Trends Immunol. 2023;44:468–83.

    Article  PubMed  CAS  Google Scholar 

  37. Tanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27:109–18.

    Article  PubMed  CAS  Google Scholar 

  38. Savage PA, Klawon DEJ, Miller CH. Regulatory T Cell Development. Annu Rev Immunol. 2020;38:421–53.

    Article  PubMed  CAS  Google Scholar 

  39. Craig AJ, von Felden J, Garcia-Lezana T, Sarcognato S, Villanueva A. Tumour evolution in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2020;17:139–52.

    Article  PubMed  Google Scholar 

  40. Oura K, Morishita A, Tani J, Masaki T. Tumor immune microenvironment and immunosuppressive therapy in hepatocellular carcinoma: a review. Int J Mol Sci. 2021;22:5801.

  41. Caja L, Dituri F, Mancarella S, Caballero-Diaz D, Moustakas A, Giannelli G, et al. TGF-β and the tissue microenvironment: relevance in fibrosis and cancer. Int J Mol Sci. 2018;19:1294.

  42. Akkız H. Emerging role of cancer-associated fibroblasts in progression and treatment of hepatocellular carcinoma. Int J Mol Sci. 2023;24:3941.

  43. Wu X, Gu Z, Chen Y, Chen B, Chen W, Weng L, et al. Application of PD-1 blockade in cancer immunotherapy. Comput Struct Biotechnol J. 2019;17:661–74.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Wang Z, Wang Y, Gao P, Ding J. Immune checkpoint inhibitor resistance in hepatocellular carcinoma. Cancer Lett. 2023;555:216038.

    Article  PubMed  CAS  Google Scholar 

  45. Greten TF, Villanueva A, Korangy F, Ruf B, Yarchoan M, Ma L, et al. Biomarkers for immunotherapy of hepatocellular carcinoma. Nat Rev Clin Oncol. 2023;20:780–98.

    Article  PubMed  CAS  Google Scholar 

Download references

Funding

This study was supported by the National Natural Science Foundation of China, No. 82203835.

Author information

Author notes

  1. These authors contributed equally: Yunjie Li, Shuya Bai.

Authors and Affiliations

  1. Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Yunjie Li, Heli Li, Chen Hu, Jinzhu Zhao, Hong Qian & Yangyang Feng

  2. Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Shuya Bai

  3. Department of Pathophysiology, School of Basic Medicine, Key Laboratory of Education Ministry of China for Neurological Disorders, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Jichang Hu

  4. Department of Neurology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Zhouping Tang

Authors
  1. Yunjie Li
    View author publications

    Search author on:PubMed Google Scholar

  2. Shuya Bai
    View author publications

    Search author on:PubMed Google Scholar

  3. Jichang Hu
    View author publications

    Search author on:PubMed Google Scholar

  4. Heli Li
    View author publications

    Search author on:PubMed Google Scholar

  5. Chen Hu
    View author publications

    Search author on:PubMed Google Scholar

  6. Jinzhu Zhao
    View author publications

    Search author on:PubMed Google Scholar

  7. Hong Qian
    View author publications

    Search author on:PubMed Google Scholar

  8. Zhouping Tang
    View author publications

    Search author on:PubMed Google Scholar

  9. Yangyang Feng
    View author publications

    Search author on:PubMed Google Scholar

Contributions

YL and SB: Conceptualization, Resources. JH and HL: Data curation. CH and JZ: Formal analysis. HQ: Software. YF: Writing—original draft. ZT: Methodology, Visualization. YF and YL: Data curation, Writing—review & editing, Supervision, Investigation. YF: Project administration.

Corresponding author

Correspondence to Yangyang Feng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Ethics Review Board of the Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. All experiments complied with the relevant regulations, and all patients provided written informed consent (TJ-2024-105).

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Bai, S., Hu, J. et al. Post-translational acylation modulates immunosuppression and immunotherapy efficacy in hepatocellular carcinoma. Genes Immun (2025). https://doi.org/10.1038/s41435-025-00362-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41435-025-00362-2

Search

Advanced search

Quick links