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Purpose <p>Molecular subtype of hepatocellular carcinoma (HCC) is primarily identified via high throughput expression profiles, largely ignoring the dynamic changes of gene expressions. Yet, biological networks remain steadily characterize disease state irrespective of time and conditions. We aim to utilize a metabolic genes interaction perturbation network-based approach to facilitate the subtyping and precision treatment of HCC patients.</p> Methods <p>We employed the metabolic genes interaction perturbation network-based approach to identify metabolic reprogramming (MR) subtypes in 922 HCC samples from four independent public datasets and further investigated their clinical and biofunctional implications, immune landscape, multi-omics features and biomarker.</p> Results <p>We stratified patients into three unique MR subtypes: (i) MR1 (“immune-deficiency”), frequent CTNNB1 mutation, and moderate prognosis; (ii) MR2 (“immune-activated”), advanced pathological staging and histological grading, frequent TP53 mutation, response to anti-PD-1 therapy, and the worst prognosis; (iii) MR3 (high metabolic activity), low-grade pathological staging and histological grading, fewer mutations and copy number variations, and the best prognosis. Besides, CD24 was identified and validated as a biomarker for MR2 which indicated a poor prognosis with higher expression.</p> Conclusion <p>Taken together, the interactome taxonomy could effectively facilitate the stratified management and precise treatment of heterogeneous HCC patients.</p>

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Metabolic genes interaction perturbation network identified and validated CD24 as a novel prognostic gene in hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月27日

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  • Hao Liu,
  • Hongyi Yan,
  • Ying Huang,
  • Xiangkun Meng,
  • Long Liu,
  • Yuyuan Zhang,
  • Hui Xu,
  • Yanchao Hu

摘要

Purpose

Molecular subtype of hepatocellular carcinoma (HCC) is primarily identified via high throughput expression profiles, largely ignoring the dynamic changes of gene expressions. Yet, biological networks remain steadily characterize disease state irrespective of time and conditions. We aim to utilize a metabolic genes interaction perturbation network-based approach to facilitate the subtyping and precision treatment of HCC patients.

Methods

We employed the metabolic genes interaction perturbation network-based approach to identify metabolic reprogramming (MR) subtypes in 922 HCC samples from four independent public datasets and further investigated their clinical and biofunctional implications, immune landscape, multi-omics features and biomarker.

Results

We stratified patients into three unique MR subtypes: (i) MR1 (“immune-deficiency”), frequent CTNNB1 mutation, and moderate prognosis; (ii) MR2 (“immune-activated”), advanced pathological staging and histological grading, frequent TP53 mutation, response to anti-PD-1 therapy, and the worst prognosis; (iii) MR3 (high metabolic activity), low-grade pathological staging and histological grading, fewer mutations and copy number variations, and the best prognosis. Besides, CD24 was identified and validated as a biomarker for MR2 which indicated a poor prognosis with higher expression.

Conclusion

Taken together, the interactome taxonomy could effectively facilitate the stratified management and precise treatment of heterogeneous HCC patients.