Random gene sets in predicting survival of patients with hepatocellular carcinoma
Random gene sets in predicting survival of patients with hepatocellular carcinoma
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DOI：
10.1007/s00109-019-01764-2
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发表时间：
2019-06-01
影响因子：
4.7
通讯作者：
Teuf, Andreas
中科院分区：
文献类型：
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作者：
Itzel, Timo;Spang, Rainer;Teuf, Andreas
尽管有多篇出版物，但预测肝细胞癌（HCC）病程的分子特征尚未整合到临床常规决策中。鉴于已发表的特征的多样性，预后特征中基因的最佳数量、最佳组合和功能关联的益处仍有待确定。我们研究了大量随机选择的基因集（1至10，000个基因），以涵盖HCC患者242个转录组学特征的全部预后基因集。根据所选的大小，所有随机基因组的4.7%至23.5%通过分离具有显著不同生存期的患者亚组而显示出预后潜力。通过研究基因组和信号通路进一步证实了这一点，这些基因组和信号通路也导致相当高数量的显著预后基因组。然而，使用群体智能组合多个随机基因集，显著提高了约63%患者的可预测性。在这些患者中，70%的随机50个含有基因的基因组产生相等和稳定的生存预测。对于所有其他患者，任何选定的基因集似乎都不太可能得到可靠的预测。使用机器学习和独立验证方法，我们证明了随机基因集和群体智能在HCC预后中的高可靠性。最终，这些发现在两个独立的患者队列和独立的技术平台（微阵列、RNASeq）中得到了验证。总之，我们证明了使用多个基因集的群体智能进行预后预测可能不仅是上级的，而且对于预测目的更鲁棒。关键信息预测HCC的分子标记尚未整合到临床常规中取决于所选择的大小，所有随机基因集的4.7至23.5%表现出预后潜力;独立于技术平台（微阵列，RNASeq）使用群体智能导致显著改善的可预测性在这些患者中，约70%的随机50个基因包含的基因集导致相同和稳定的生存预测总的来说，群体智能是上级和更强大的预测肝癌的目的
Despite multiple publications, molecular signatures predicting the course of hepatocellular carcinoma (HCC) have not yet been integrated into clinical routine decision-making. Given the diversity of published signatures, optimal number, best combinations, and benefit of functional associations of genes in prognostic signatures remain to be defined. We investigated a vast number of randomly chosen gene sets (varying between 1 and 10,000 genes) to encompass the full range of prognostic gene sets on 242 transcriptomic profiles of patients with HCC. Depending on the selected size, 4.7 to 23.5% of all random gene sets exhibit prognostic potential by separating patient subgroups with significantly diverse survival. This was further substantiated by investigating gene sets and signaling pathways also resulting in a comparable high number of significantly prognostic gene sets. However, combining multiple random gene sets using swarm intelligence resulted in a significantly improved predictability for approximately 63% of all patients. In these patients, approx. 70% of all random 50-gene containing gene sets resulted in equal and stable prediction of survival. For all other patients, a reliable prediction seems highly unlikely for any selected gene set. Using a machine learning and independent validation approach, we demonstrated a high reliability of random gene sets and swarm intelligence in HCC prognosis. Ultimately, these findings were validated in two independent patient cohorts and independent technical platforms (microarray, RNASeq). In conclusion, we demonstrate that using swarm intelligence of multiple gene sets for prognosis prediction may not only be superior but also more robust for predictive purposes.Key messagesMolecular signatures predicting HCC have not yet been integrated into clinical routineDepending on the selected size, 4.7 to 23.5% of all random gene sets exhibit prognostic potential; independent of the technical platform (microarray, RNASeq)Using swarm intelligence resulted in a significantly improved predictabilityIn these patients, approx. 70% of all random 50-gene containing gene sets resulted in equal and stable prediction of survivalOverall, swarm intelligence is superior and more robust for predictive purposes in HCC