Science for Life Laboratory, KTH Royal Institute of Technology, SE-17121 Stockholm, Sweden.
Centre for Host-Microbiome Interactions, Dental Institute, King's College London, SE1 9RT London, United Kingdom.
Proc Natl Acad Sci U S A. 2018 Dec 11;115(50):E11874-E11883. doi: 10.1073/pnas.1807305115. Epub 2018 Nov 27.
Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/β-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.
肝细胞癌 (HCC) 是最常见的肝癌形式之一，由于肿瘤异质性，有效的治疗方法有限。非常需要综合方法对 HCC 患者进行分层，深入了解亚型的生物学特性，并最终确定有效的治疗靶点。我们使用转录组学数据、基因组规模的代谢网络和网络拓扑/可控性分析对 HCC 患者进行分层并对每个亚型进行了特征描述。这种全面的系统水平分析确定了三种具有显著差异的不同亚型，其代谢和信号通路存在显著差异，反映在基因组、转录组和蛋白质组水平上。这些亚型在临床生存方面存在很大差异，与犬尿氨酸代谢、WNT/β-catenin 相关的脂质代谢和 PI3K/AKT/mTOR 信号改变有关。综合分析表明，这三种亚型依赖于替代酶（例如，ACSS1/ACSS2/ACSS3、PKM/PKLR、ALDOB/ALDOA、MTHFD1L/MTHFD2/MTHFD1）来催化相同的反应。基于系统水平分析，我们确定了 8 到 28 个具有关键作用的亚型特异性基因，这些基因可以控制代谢网络，通过进行计算机分析，我们预测这些基因可能成为针对 HCC 亚型治疗策略的靶点。为了验证我们的预测，我们在常氧和缺氧条件下使用 HepG2 细胞进行实验，观察到在缺氧条件下，高/中/低生存肿瘤组中表达的基因之间的表达模式相反，这反映了在生存不良的患者中激活了缺氧行为。总之，我们的分析表明，具有代谢网络驱动的方法可以对异质 HCC 肿瘤进行分层，这种方法也可能适用于其他癌症类型，这种分层可能具有临床意义，有助于推动精准医学的发展。
