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A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model

机译:基于亚细胞分布的新型蛋白质生物标志物:在小鼠肝癌模型中的应用

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摘要

To-date, most proteomic studies aimed at discovering tissue-based cancer biomarkers have compared the quantity of selected proteins between case and control groups. However, proteins generally function in association with other proteins to form modules localized in particular subcellular compartments in specialized cell types and tissues. Sub-cellular mislocalization of proteins has in fact been detected as a key feature in a variety of cancer cells. Here, we describe a strategy for tissue-biomarker detection based on a mitochondrial fold enrichment (mtFE) score, which is sensitive to protein abundance changes as well as changes in subcellular distribution between mitochondria and cytosol. The mtFE score integrates protein abundance data from total cellular lysates and mitochondria-enriched fractions, and provides novel information for the classification of cancer samples that is not necessarily apparent from conventional abundance measurements alone. We apply this new strategy to a panel of wild-type and mutant mice with a liver-specific gene deletion of Liver receptor homolog 1 (Lrh-1hep−/−), with both lines containing control individuals as well as individuals with liver cancer induced by diethylnitrosamine (DEN). Lrh-1 gene deletion attenuates cancer cell metabolism in hepatocytes through mitochondrial glutamine processing. We show that proteome changes based on mtFE scores outperform protein abundance measurements in discriminating DEN-induced liver cancer from healthy liver tissue, and are uniquely robust against genetic perturbation. We validate the capacity of selected proteins with informative mtFE scores to indicate hepatic malignant changes in two independent mouse models of hepatocellular carcinoma (HCC), thus demonstrating the robustness of this new approach to biomarker research. Overall, the method provides a novel, sensitive approach to cancer biomarker discovery that considers contextual information of tested proteins.
机译:迄今为止,大多数旨在发现基于组织的癌症生物标志物的蛋白质组学研究都比较了病例组和对照组之间选定蛋白质的数量。但是,蛋白质通常与其他蛋白质结合起作用,以形成位于特定细胞类型和组织中特定亚细胞区室的模块。实际上,蛋白质的亚细胞错位已被检测为多种癌细胞的关键特征。在这里,我们描述了一种基于线粒体倍富集(mtFE)分数的组织生物标志物检测策略,该分数对蛋白质丰度变化以及线粒体和细胞质之间的亚细胞分布变化敏感。 mtFE得分整合了来自总细胞裂解液和线粒体富集馏分的蛋白质丰度数据,并为癌症样品的分类提供了新颖的信息,而这仅从常规的丰度测量中并不一定显而易见。我们将此新策略应用于一组具有肝脏特异性基因缺失的肝受体同源物1(Lrh-1 hep-/-)的野生型和突变小鼠,其中两个品系均包含对照个体以及由二乙基亚硝胺(DEN)诱发的肝癌患者。 Lrh-1基因缺失通过线粒体谷氨酰胺加工减弱了肝细胞中癌细胞的代谢。我们表明,基于mtFE分数的蛋白质组变化在将DEN诱发的肝癌与健康肝组织区分开来的过程中,胜过蛋白质丰度测量,并且对遗传扰动具有独特的鲁棒性。我们用信息丰富的mtFE分数验证所选蛋白的能力,以表明在两个独立的肝细胞癌(HCC)小鼠模型中肝恶性变化,从而证明了这种新的生物标记物研究方法的稳健性。总体而言,该方法为癌症生物标志物的发现提供了一种新颖,敏感的方法,该方法考虑了所测试蛋白质的背景信息。

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