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Integrative analysis of extracellular and intracellular bladder cancer cell line proteome with transcriptome: improving coverage and validity of -omics findings

机译:细胞外和细胞内膀胱癌细胞系蛋白质组与转录组的整合分析:提高 - 组学研究结果的覆盖率和有效性

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摘要

Characterization of disease-associated proteins improves our understanding of disease\udpathophysiology. Obtaining a comprehensive coverage of the proteome is challenging, mainly due to limited statistical power and an inability to verify hundreds of putative biomarkers. In an effort to address these issues, we investigated the value of parallel analysis of compartment-specific proteomes with an assessment of findings by cross-strategy and cross-omics (proteomics-transcriptomics) agreement. The validity of the individual datasets and of a “verified” dataset based on crossstrategy/omics agreement was defined following their comparison with published literature. The proteomic analysis of the cell extract, Endoplasmic Reticulum/Golgi apparatus and conditioned medium of T24 vs. its metastatic subclone T24M bladder cancer cells allowed the identification of 253, 217 and 256 significant changes, respectively. Integration of these findings with transcriptomics resulted in 253 “verified” proteins based on the agreement of at least 2 strategies. This approach revealed findings of higher validity, as supported by a higher level of agreement in the literature data than those of individual datasets. As an example, the coverage and shortlisting of targets in the IL-8 signalling pathway are discussed. Collectively, an integrative analysis appears a safer way to evaluate -omics datasets and ultimately generate models from valid observations.
机译:疾病相关蛋白的表征提高了我们对疾病\病理生理学的理解。蛋白质组的全面覆盖具有挑战性,这主要是由于有限的统计能力以及无法验证数百种推定的生物标记。为了解决这些问题,我们调查了针对特定区室的蛋白质组进行并行分析的价值,并通过跨策略和跨组学(proteomics-transcriptomics)协议对发现进行了评估。在与公开文献进行比较之后,定义了各个数据集和基于跨策略/组学协议的“验证”数据集的有效性。通过对T24与其转移亚克隆T24M膀胱癌细胞的细胞提取物,内质网/高尔基体和条件培养基进行蛋白质组学分析,可以分别鉴定出253、217和256个显着变化。这些发现与转录组学的整合,基于至少两种策略的一致性,产生了253种“经过验证的”蛋白质。这种方法揭示了更高的有效性,因为与单个数据集相比,文献数据中的一致性更高。作为示例,讨论了IL-8信号传导途径中靶标的覆盖和入围。总体而言,综合分析似乎是评估组学数据集并最终根据有效观察结果生成模型的更安全方法。

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