Yimeng Song1, Lijun Zhong2, Juntuo Zhou3, Min Lu3, Tianying Xing1, Lulin Ma1, Jing Shen4. 1. Department of Urology, Peking University Third Hospital, Beijing, China. 2. Medical and Health Analytical Center, Peking University Health Science Center, Beijing, China. 3. Department of Pathology, School of Basic Medical Science, Peking University Health Science Center, Beijing, China. 4. Key Laboratory of Carcinogenesis and Translational Research Ministry of Education/Beijing, Central Laboratory, Peking University Cancer Hospital and Institute, Beijing, 100142, China.
Abstract
PURPOSE: 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. EXPERIMENTAL DESIGN: 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. RESULTS: 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. CONCLUSIONS AND CLINICAL RELEVANCE: 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.
PURPOSE: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. EXPERIMENTAL DESIGN: 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. RESULTS: 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. CONCLUSIONS AND CLINICAL RELEVANCE: 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.
Authors: Kyle T Siebenthall; Chris P Miller; Jeff D Vierstra; Julie Mathieu; Maria Tretiakova; Alex Reynolds; Richard Sandstrom; Eric Rynes; Eric Haugen; Audra Johnson; Jemma Nelson; Daniel Bates; Morgan Diegel; Douglass Dunn; Mark Frerker; Michael Buckley; Rajinder Kaul; Ying Zheng; Jonathan Himmelfarb; Hannele Ruohola-Baker; Shreeram Akilesh Journal: EBioMedicine Date: 2019-03-01 Impact factor: 8.143
Authors: Chase K A Neumann; Daniel J Silver; Varadharajan Venkateshwari; Renliang Zhang; C Alicia Traughber; Christopher Przybycin; Defne Bayik; Jonathan D Smith; Justin D Lathia; Brian I Rini; J Mark Brown Journal: Mol Metab Date: 2020-02-03 Impact factor: 7.422