Proteomic analysis enables distinction of early- versus advanced-stage lung adenocarcinomas
Proteomic analysis enables distinction of early- versus advanced-stage lung adenocarcinomas
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DOI：
10.1002/ctm2.106
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发表时间：
2020-06-14
影响因子：
10.6
通讯作者：
Marko-Varga, Gyorgy
中科院分区：
文献类型：
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作者：
Kelemen, Olga;Pla, Indira;Marko-Varga, Gyorgy
背景：采用无凝胶蛋白质组学方法对早期和晚期肺腺癌（ADC）和正常肺组织进行深入的组织蛋白质谱分析。这项研究的长期目标是从组织学分类良好的肺ADC中生成大规模的无标记蛋白质组学数据集，可用于进一步增加我们对疾病进展的理解，并有助于识别新的生物标志物。方法和结果选择早期（I-II）和晚期（III-IV）肺ADC病例，并与22例患者的正常肺组织配对。通过液相色谱-串联质谱法分析组织学和临床分层的人原发性肺ADC。从ADC和正常标本的分析中，鉴定出4863个蛋白质组。为了检查ADC的蛋白质表达谱，使用基于峰面积的定量方法。在早期和晚期ADC中，365和366种蛋白质在正常和肿瘤组织中分别差异表达（校正P值<0.01，倍数变化>= 4）。共155种蛋白质在早期和晚期ADC之间失调，其中18种被认为是早期特异性ADC。对两个肿瘤组中上调蛋白质的计算机功能分析显示，大多数富集途径参与mRNA代谢。此外，ADC特有的蛋白质中最多的途径与mRNA代谢过程有关。结论对这些数据的进一步分析可能会提供对疾病病因学中涉及的分子途径的深入了解，并可能导致生物标志物候选者和潜在治疗靶点的鉴定。我们的研究为肺ADC提供了潜在的诊断生物标志物，并为合理干预提供了新的阶段特异性药物靶标。
Background A gel-free proteomic approach was utilized to perform in-depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long-term goal of this study is to generate a large-scale, label-free proteomics dataset from histologically well-classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers. Methods and results Cases of early-stage (I-II) and advanced-stage (III-IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung ADCs were analyzed by liquid chromatography-tandem mass spectrometry. From the analysis of ADC and normal specimens, 4863 protein groups were identified. To examine the protein expression profile of ADC, a peak area-based quantitation method was used. In early- and advanced-stage ADC, 365 and 366 proteins were differentially expressed, respectively, between normal and tumor tissues (adjustedP-value < .01, fold change >= 4). A total of 155 proteins were dysregulated between early- and advanced-stage ADCs and 18 were suggested as early-specific stage ADC. In silico functional analysis of the upregulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most overrepresented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes. Conclusions Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage-specific drug targets for rational intervention.