Song Yimeng, Zhong Lijun, Zhou Juntuo, Lu Min, Xing Tianying, Ma Lulin, Shen Jing
Department of Urology, Peking University Third Hospital, Beijing, China.
Medical and Health Analytical Center, Peking University Health Science Center, Beijing, China.
Proteomics Clin Appl. 2017 Dec;11(11-12). doi: 10.1002/prca.201700066. Epub 2017 Oct 27.
Renal cell carcinoma (RCC) is a malignant and metastatic cancer with 95% mortality, and clear cell RCC (ccRCC) is the most observed among the five major subtypes of RCC. Specific biomarkers that can distinguish cancer tissues from adjacent normal tissues should be developed to diagnose this disease in early stages and conduct a reliable prognostic evaluation.
Data-independent acquisition (DIA) strategy has been widely employed in proteomic analysis because of various advantages, including enhanced protein coverage and reliable data acquisition. In this study, a DIA workflow is constructed on a quadrupole-Orbitrap LC-MS platform to reveal dysregulated proteins between ccRCC and adjacent normal tissues.
More than 4000 proteins are identified, 436 of these proteins are dysregulated in ccRCC tissues. Bioinformatic analysis reveals that multiple pathways and Gene Ontology items are strongly associated with ccRCC. The expression levels of L-lactate dehydrogenase A chain, annexin A4, nicotinamide N-methyltransferase, and perilipin-2 examined through RT-qPCR, Western blot, and immunohistochemistry confirm the validity of the proteomic analysis results.
The proposed DIA workflow yields optimum time efficiency and data reliability and provides a good choice for proteomic analysis in biological and clinical studies, and these dysregulated proteins might be potential biomarkers for ccRCC diagnosis.
肾细胞癌（RCC）是一种恶性转移性癌症，死亡率达95%，透明细胞肾细胞癌（ccRCC）是RCC五种主要亚型中最常见的。应开发能够区分癌组织与相邻正常组织的特异性生物标志物，以便在早期诊断该疾病并进行可靠的预后评估。
数据非依赖采集（DIA）策略因其具有多种优势，包括增强蛋白质覆盖范围和可靠的数据采集，已在蛋白质组学分析中广泛应用。在本研究中，在四极杆-轨道阱液相色谱-质谱平台上构建了DIA工作流程，以揭示ccRCC组织与相邻正常组织之间的失调蛋白。
鉴定出4000多种蛋白质，其中436种蛋白质在ccRCC组织中失调。生物信息学分析表明，多种信号通路和基因本体条目与ccRCC密切相关。通过逆转录定量聚合酶链反应、蛋白质免疫印迹和免疫组织化学检测的L-乳酸脱氢酶A链、膜联蛋白A4、烟酰胺N-甲基转移酶和脂肪分化相关蛋白2的表达水平证实了蛋白质组学分析结果的有效性。
所提出的DIA工作流程具有最佳的时间效率和数据可靠性，为生物学和临床研究中的蛋白质组学分析提供了一个很好的选择，这些失调蛋白可能是ccRCC诊断的潜在生物标志物。
