Data-Independent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Biomarkers of Kidney Cancer

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.

Proteomics. Clinical applications. 2017 Oct 27 [Epub]

Yimeng Song, Lijun Zhong, Juntuo Zhou, Min Lu, Tianying Xing, Lulin Ma, Jing Shen

Department of Urology, Peking University Third Hospital, Beijing, China., Medical and Health Analytical Center, Peking University Health Science Center, Beijing, China., Department of Pathology, School of Basic Medical Science, Peking University Health Science Center, Beijing, China., Key Laboratory of Carcinogenesis and Translational Research Ministry of Education/Beijing, Central Laboratory, Peking University Cancer Hospital and Institute, Beijing, 100142, China.