肝细胞癌 (HCC) 复杂的分子景观对通过临床基因检测实现精确的风险分层提出了重大挑战。目前，缺乏可靠的基因特征可以帮助临床医生为 HCC 患者做出临床决策。我们从公共数据库中的 20 个独立队列中获得了 HCC 患者的基因表达谱。基因签名是通过采用两种机器学习算法开发的。除了在公共队列中使用高通量数据验证签名之外，我们还通过 RT-PCR 方法在 64 个 HCC 病例中验证了签名。我们比较了不同亚组之间的基因组、转录组和蛋白质组特征。我们还将我们的特征与已发表的 130 个基因特征进行了比较。我们开发了一种新型四基因特征，命名为 HCC4，它显示了预测 1300 多名 HCC 患者生存结果的巨大潜力。 HCC4 还具有预测复发和肿瘤体积倍增时间、评估经导管动脉化疗栓塞和免疫治疗反应以及 HCC 无创检测的潜力。 HCC4高评分组显示TP53、RB1和TSC1/2基因突变频率较高，细胞周期、糖酵解和缺氧信号通路活性增加，癌症干性评分较高，脂质代谢活性较低。在 7 个 HCC 队列中，与之前发布的 130 个特征相比，HCC4 在预测总生存期方面表现出更高的平均 C 指数。药物筛选表明，HCC4 评分高的患者对针对 AURKA、TUBB、JMJD6 和 KIFC1 的药物更敏感。我们的研究结果表明，HCC4 是改善风险分层和识别最有可能从 TACE 治疗中受益的 HCC 患者的强大工具、免疫疗法和其他实验疗法。版权所有 © 2023 Elsevier Ltd。保留所有权利。

The intricate molecular landscape of hepatocellular carcinoma (HCC) presents a significant challenge to achieving precise risk stratification through clinical genetic testing. At present, there is a paucity of robust gene signatures that could assist clinicians in making clinical decisions for patients with HCC.We obtained gene expression profiles of patients with HCC from 20 independent cohorts available in public databases. A gene signature was developed by employing two machine learning algorithms. In addition to validating the signature with high-throughput data in public cohorts, we external validated the signature in 64 HCC cases by RT-PCR method. We compared genomic, transcriptomic and proteomic features between different subgroups. We also compared our signature to 130 gene signatures that have already been published.We developed a novel four-gene signature, designated as HCC4, that demonstrates significant potential for the prediction of survival outcomes in more than 1300 patients with HCC. The HCC4 also has potential for predicting recurrence and tumor volume doubling time, assessing transcatheter arterial chemoembolization and immunotherapy responses, and non-invasive detection of HCC. The high HCC4 score group shows a higher frequency of mutations in genes TP53, RB1 and TSC1/2, as well as increased activity of cell-cycle, glycolysis and hypoxia signaling pathways, higher cancer stemness score, and lower lipid metabolism activity. In seven HCC cohorts, HCC4 exhibited a higher average C-index in predicting overall survival compared to the 130 signatures previously published. Drug screening indicated that patients with high HCC4 scores were more sensitive to agents targeting AURKA, TUBB, JMJD6 and KIFC1.Our findings demonstrated that HCC4 is a powerful tool for improving risk stratification and for identifying HCC patients who are most likely to benefit from TACE treatment, immunotherapy, and other experimental therapies.Copyright © 2023 Elsevier Ltd. All rights reserved.