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Background <p>Hepatocellular carcinoma (HCC) poses considerable difficulties regarding the prognosis and the assessment of treatment efficacy. Additionally, while it is recognized that post-translational modification (PTM) plays a crucial role in modulating HCC progression, their specific prognostic implications in HCC have not been thoroughly investigated.</p> Methods <p>21 types of PTM (acetylation, succinylation, malonylation, crotonylation, β-hydroxybutyrylation, lactylation, palmitoylation, myristoylation, SUMOylation, NEDDylation, ISGylation, ATG8ylation, FAT10ylation, UFMylation, methylation, glycosylation, biotinylation, S-nitrosylation, phosphorylation, ubiquitination, deubiquitination) were generated consensus cluster. Then, WGCNA was utilized to identify module genes. Finally, a machine learning approach was employed to create PTM.score.</p> Results <p>This analysis revealed two distinct subtypes of PTMs, each characterized by unique molecular signatures. By integrating different categories of genes, including prognosis-related DEGs, module genes, and PTM-related genes, 15 hub genes were identified, and a PTM.score was developed. PTM.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. In addition, patients categorized with a low PTM.score displayed a TME that was more actively engaged, which corresponded with a poor prognosis. Furthermore, these patients demonstrated a high level of responsiveness to immunotherapy interventions. Furthermore, an examination using scRNA-seq and spatial transcriptomics indicated that patients with low PTM.score exhibited heightened cell proliferation and malignancy.</p> Conclusion <p>This novel PTM-related prognostic signature could effectively assess the prognosis and therapeutic responses of HCC patients, providing new perspectives for individualized treatment for the patient population.</p>

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Integrating bulk RNA-seq, scRNA-seq, and spatial transcriptomics data to identify novel post-translational modification-related molecular subtypes and therapeutic responses in hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Shiling Chen,
  • Yunjie Li,
  • Jichang Hu,
  • Heli Li,
  • Chen Hu,
  • Jinzhu Zhao,
  • Hong Qian,
  • Shuya Bai,
  • Zhouping Tang,
  • Yangyang Feng

摘要

Background

Hepatocellular carcinoma (HCC) poses considerable difficulties regarding the prognosis and the assessment of treatment efficacy. Additionally, while it is recognized that post-translational modification (PTM) plays a crucial role in modulating HCC progression, their specific prognostic implications in HCC have not been thoroughly investigated.

Methods

21 types of PTM (acetylation, succinylation, malonylation, crotonylation, β-hydroxybutyrylation, lactylation, palmitoylation, myristoylation, SUMOylation, NEDDylation, ISGylation, ATG8ylation, FAT10ylation, UFMylation, methylation, glycosylation, biotinylation, S-nitrosylation, phosphorylation, ubiquitination, deubiquitination) were generated consensus cluster. Then, WGCNA was utilized to identify module genes. Finally, a machine learning approach was employed to create PTM.score.

Results

This analysis revealed two distinct subtypes of PTMs, each characterized by unique molecular signatures. By integrating different categories of genes, including prognosis-related DEGs, module genes, and PTM-related genes, 15 hub genes were identified, and a PTM.score was developed. PTM.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. In addition, patients categorized with a low PTM.score displayed a TME that was more actively engaged, which corresponded with a poor prognosis. Furthermore, these patients demonstrated a high level of responsiveness to immunotherapy interventions. Furthermore, an examination using scRNA-seq and spatial transcriptomics indicated that patients with low PTM.score exhibited heightened cell proliferation and malignancy.

Conclusion

This novel PTM-related prognostic signature could effectively assess the prognosis and therapeutic responses of HCC patients, providing new perspectives for individualized treatment for the patient population.