首页> 外文期刊>Clinical chemistry and laboratory medicine: CCLM >Serum albumin-bound proteomic signature for early detection and staging of hepatocarcinoma: sample variability and data classification.
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Serum albumin-bound proteomic signature for early detection and staging of hepatocarcinoma: sample variability and data classification.

机译:血清白蛋白结合的蛋白质组学特征用于肝癌的早期检测和分期:样品变异性和数据分类。

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摘要

BACKGROUND: Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) proteomic signature might be of interest for the early detection and staging of hepatocellular carcinoma (HCC). However, published procedures have been criticized for the lack of data about analytical reproducibility, and the use of inadequate data processing. METHODS: MALDI-TOF profiling of peptides bound to serum albumin ("albuminome") was performed using 90 muL of serum from 45 study subjects (HCV-related cirrhosis, small, unifocal HCCs and advanced HCCs). To overcome the large intra-sample variability, a Quality Assurance protocol was implemented, and 4-8 samples for each subject were processed and analyzed. Overall, 522 subject samples and 299 quality-control spectra were analyzed. A machine-learning approach (Random Forest) was applied to analyze the data sets. RESULTS: Mean intra-sample coefficient of variation (CV) of the analytical procedure was 17.6%-30.0%; inter-subject CV was in the range 48.8%-71.3% among the three study groups. The Random Forest procedure correctly classified 433/522 patient samples classified following this approach. CONCLUSIONS: Our data suggest that, notwithstanding the large analytical variability found, multiple proteomic profiles obtained from each subject can differentiate cirrhosis with and without HCC, and HCCs with and without vascular invasion, warranting further investigation in a prospective setting.
机译:背景:基质辅助激光解吸电离飞行时间(MALDI-TOF)蛋白质组学特征可能对肝细胞癌(HCC)的早期检测和分期感兴趣。但是,由于缺乏有关分析重现性的数据以及使用的数据处理不充分,已公开的程序受到批评。方法:使用来自45个研究对象(HCV相关性肝硬化,小型,单灶性HCC和晚期HCC)的90μL血清,对与血清白蛋白(“白蛋白组”)结合的肽进行MALDI-TOF分析。为了克服较大的样本内变异性,实施了质量保证协议,并对每个受试者的4-8个样本进行了处理和分析。总体上,分析了522个受试者样品和299个质量控制光谱。应用了机器学习方法(Random Forest)来分析数据集。结果:分析程序的平均样本内变异系数(CV)为17.6%-30.0%;在三个研究组中,受试者间CV范围为48.8%-71.3%。随机森林程序正确分类了按照这种方法分类的433/522患者样本。结论:我们的数据表明,尽管发现较大的分析变异性,但从每名受试者获得的多种蛋白质组学谱图可以区分有无肝癌和无肝癌的肝硬化,以及有无血管侵犯的肝癌,值得在前瞻性研究中进行进一步研究。

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