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首页> 外文期刊>BMC Medical Genomics >Curation-free biomodules mechanisms in prostate cancer predict recurrent disease
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Curation-free biomodules mechanisms in prostate cancer predict recurrent disease

机译：前列腺癌的无治疗生物模块机制可预测复发性疾病

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Motivation Gene expression-based prostate cancer gene signatures of poor prognosis are hampered by lack of gene feature reproducibility and a lack of understandability of their function. Molecular pathway-level mechanisms are intrinsically more stable and more robust than an individual gene. The Functional Analysis of Individual Microarray Expression (FAIME) we developed allows distinctive sample-level pathway measurements with utility for correlation with continuous phenotypes (e.g. survival). Further, we and others have previously demonstrated that pathway-level classifiers can be as accurate as gene-level classifiers using curated genesets that may implicitly comprise ascertainment biases (e.g. KEGG, GO). Here, we hypothesized that transformation of individual prostate cancer patient gene expression to pathway-level mechanisms derived from automated high throughput analyses of genomic datasets may also permit personalized pathway analysis and improve prognosis of recurrent disease. Results Via FAIME, three independent prostate gene expression arrays with both normal and tumor samples were transformed into two distinct types of molecular pathway mechanisms: (i) the curated Gene Ontology (GO) and (ii) dynamic expression activity networks of cancer (Cancer Modules). FAIME-derived mechanisms for tumorigenesis were then identified and compared. Curated GO and computationally generated "Cancer Module" mechanisms overlap significantly and are enriched for known oncogenic deregulations and highlight potential areas of investigation. We further show in two independent datasets that these pathway-level tumorigenesis mechanisms can identify men who are more likely to develop recurrent prostate cancer (log-rank_p = 0.019). Conclusion Curation-free biomodules classification derived from congruent gene expression activation breaks from the paradigm of recapitulating the known curated pathway mechanism universe.
机译：动机由于缺乏基因特征的可重复性和对其功能的可理解性，阻碍了预后不良的基于基因表达的前列腺癌基因签名。分子途径水平的机制在本质上比单个基因更稳定，更可靠。我们开发的个体微阵列表达功能分析（FAIME）可以进行独特的样品水平途径测量，并具有与连续表型（例如存活率）相关的效用。此外，我们和其他人先前已经证明，使用可能隐含地包含确定性偏倚（例如KEGG，GO）的精选基因集，途径水平的分类器可以与基因水平的分类器一样准确。在这里，我们假设个体前列腺癌患者基因表达向基因组数据集的自动化高通量分析衍生的途径水平机制的转化也可能允许个性化途径分析并改善复发性疾病的预后。结果通过FAIME，将具有正常和肿瘤样品的三个独立的前列腺基因表达阵列转化为两种不同类型的分子途径机制：（i）策划的基因本体论（GO）和（ii）癌症的动态表达活性网络（Cancer Modules） ）。然后确定并比较了FAIME衍生的肿瘤发生机制。精心设计的GO和以计算方式生成的“癌症模块”机制明显重叠，并且丰富了已知的致癌法规，并突出了潜在的研究领域。我们进一步在两个独立的数据集中显示，这些途径水平的肿瘤发生机制可以确定更可能复发前列腺癌的男性（log-rank_p = 0.019）。结论源自一致基因表达激活的无管理生物模块分类打破了概括已知的策划途径机制宇宙的范式。

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