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Introduction <p>Hepatoblastoma (HB) with hepatocellular carcinoma (HCC) features (HBHF) is a rare liver malignancy. Due to its rarity and diverse histological presentations, the prognosis of HBHF remains controversial, and diagnostic differentiation poses significant challenges. To enable more accurate outcome evaluation and targeted therapeutic strategies, rapid, comprehensive, and cost-effective methods are needed to complement histopathological evaluation.</p> Methods <p>In this study, we conducted transcriptomic profiling on an HBHF cohort from our center and developed a machine-learning algorithm to quantify HCC-like expression features in HB tumors. Given overlapping histopathological and molecular charateristicss between HBHF and HCC, we further investigated shared risk factors associated with HBHF prognosis.</p> Results <p>Significantly poorer outcomes in HBHF patients suggest fundamental biological distinctions from classical HB. Transcriptomic analysis revealed comparable somatic mutation profiles between HB and HBHF cohorts but identified inflammation activation, rather than specific mutations, as a key high-risk factor in HBHF. Clinical outcomes aligned with risk stratification generated by our quantification model.</p> Conclusions <p>HBHF represents a distinct transitional entity between HB and HCC, exhibiting markedly worse clinical outcomes than HB. Our transcriptome-based computational model effectively discriminates HBHF and predicts its prognostic risk. Importantly, inflammatory activation emerges as a critical driver of tumor aggressiveness in this subtype.</p>

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A diagnostic model based on transcriptomic analysis reveals inflammation as a potential prognosis factor for hepatoblastoma with hepatocellular carcinoma features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月14日

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  • Yuhua Shan,
  • Min Zhang,
  • Hongxiang Gao,
  • Lei Zhang,
  • Chenjie Xie,
  • Jiquan Zhou,
  • Liyuan Yang,
  • Ji Ma,
  • Qiuhui Pan,
  • Zhen Zhang,
  • Min Xu,
  • Song Gu

摘要

Introduction

Hepatoblastoma (HB) with hepatocellular carcinoma (HCC) features (HBHF) is a rare liver malignancy. Due to its rarity and diverse histological presentations, the prognosis of HBHF remains controversial, and diagnostic differentiation poses significant challenges. To enable more accurate outcome evaluation and targeted therapeutic strategies, rapid, comprehensive, and cost-effective methods are needed to complement histopathological evaluation.

Methods

In this study, we conducted transcriptomic profiling on an HBHF cohort from our center and developed a machine-learning algorithm to quantify HCC-like expression features in HB tumors. Given overlapping histopathological and molecular charateristicss between HBHF and HCC, we further investigated shared risk factors associated with HBHF prognosis.

Results

Significantly poorer outcomes in HBHF patients suggest fundamental biological distinctions from classical HB. Transcriptomic analysis revealed comparable somatic mutation profiles between HB and HBHF cohorts but identified inflammation activation, rather than specific mutations, as a key high-risk factor in HBHF. Clinical outcomes aligned with risk stratification generated by our quantification model.

Conclusions

HBHF represents a distinct transitional entity between HB and HCC, exhibiting markedly worse clinical outcomes than HB. Our transcriptome-based computational model effectively discriminates HBHF and predicts its prognostic risk. Importantly, inflammatory activation emerges as a critical driver of tumor aggressiveness in this subtype.