Phenotypic Categorization and Profiles of Small and Large Hepatocellular Carcinomas.

Petr Pancoska, Sheng-Nan Lu, Brian I Carr
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引用次数: 14

Abstract

We used a database of 4139 Taiwanese HCC patients to take a new approach (Network Phenotyping Strategy) to HCC subset identification. Individual parameters for liver function tests, complete blood count, portal vein thrombosis, AFP levels and clinical demographics of age, gender, hepatitis or alcohol consumption, were considered within the whole context of complete relationships, being networked with all other parameter levels in the entire cohort. We identified 4 multi-parameter patterns for one tumor phenotype of patients and a separate 5 multi-parameter patterns to characterize another tumor phenotype of patterns. The 2 subgroups were quite different in their clinical profiles. The means of the tumor mass distributions in these phenotype subgroups were significantly different, one being associated with larger (L) and the other with smaller (S) tumor masses. These significant differences were seen systematically throughout the tumor mass distributions. Essential and common clinical components of L-phenotype patterns included simultaneously high blood levels of AFP and platelets plus presence of portal vein thrombosis. S included higher levels of liver inflammatory parameters. The 2 different parameter patterns of L and S subgroups suggest different mechanisms; L, possibly involving tumor-driven processes and S more associated with liver inflammatory processes.

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小、大肝细胞癌的表型分类和概况。
本研究以4139位台湾肝癌患者为研究对象,采用网路表型策略(Network Phenotyping Strategy)进行肝癌亚群鉴定。肝功能测试、全血细胞计数、门静脉血栓形成、甲胎蛋白水平和年龄、性别、肝炎或饮酒等临床人口统计数据的个体参数在完整关系的整体背景下被考虑,并与整个队列中的所有其他参数水平联网。我们确定了患者的一种肿瘤表型的4个多参数模式,以及表征另一种肿瘤表型模式的单独5个多参数模式。这两个亚组的临床表现有很大的不同。这些表型亚组中肿瘤质量分布的平均值有显著差异,一个与较大(L)的肿瘤质量相关,另一个与较小(S)的肿瘤质量相关。这些显著的差异在整个肿瘤肿块分布中都可以系统地看到。l表型模式的基本和常见的临床成分包括同时血液中AFP和血小板的高水平加上门静脉血栓的存在。S包括较高水平的肝脏炎症参数。L亚群和S亚群2种不同的参数模式提示不同的机制;L可能涉及肿瘤驱动过程,S更多地与肝脏炎症过程相关。
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