Morgan D Kuczler1, Richard C Zieren2, Liang Dong3, Theo M de Reijke4, Kenneth J Pienta5, Sarah R Amend5. 1. The James Buchanan Brady Urological Institute, Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: mkuczler@gmail.com. 2. The James Buchanan Brady Urological Institute, Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Urology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. 3. The James Buchanan Brady Urological Institute, Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Urology, Renji Hospital, Shanghai Jiao Tong University of Medicine, Shanghai, China. 4. Department of Urology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. 5. The James Buchanan Brady Urological Institute, Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Abstract
OBJECTIVE: To propose EV-derived mRNA as a potential diagnostic biomarker detecting the presence of clear cell renal cell carcinoma (ccRCC). There is currently no kidney cancer specific screening or diagnostic technology. Therefore, one-third of kidney cancer diagnoses occur after the cancer has metastasized and is past curative measures MATERIALS AND METHODS: Urine, plasma, normal tumor adjacent tissue, and tumor tissue was collected from a limited population of ccRCC patients. Extracellular vesicle (EV) isolation was performed on each sample, followed by mRNA extraction from isolated EVs. NanoString nCounter technology was utilized to count the mRNA transcripts present in matched plasma, urine, tumor tissue, and normal tumor adjacent tissue samples. RESULTS: 770 mRNA transcripts related to gene's affecting cancer's progression and metastasis processes were evaluated. Four EV derived mRNA transcripts (ALOX5, RBL2, VEGFA, TLK2) were found specific to urine and tumor tissue samples. CONCLUSION: Four candidate RCC-specific urine EV biomarkers were identified. However, due to the lack of a true negative control and urine collection techniques, further re-examination is necessary for validation. This study demonstrates the promise of defining disease-specific EV biomarkers in liquid biopsy patient samples.
OBJECTIVE: To propose EV-derived mRNA as a potential diagnostic biomarker detecting the presence of clear cell renal cell carcinoma (ccRCC). There is currently no kidney cancer specific screening or diagnostic technology. Therefore, one-third of kidney cancer diagnoses occur after the cancer has metastasized and is past curative measures MATERIALS AND METHODS: Urine, plasma, normal tumor adjacent tissue, and tumor tissue was collected from a limited population of ccRCC patients. Extracellular vesicle (EV) isolation was performed on each sample, followed by mRNA extraction from isolated EVs. NanoString nCounter technology was utilized to count the mRNA transcripts present in matched plasma, urine, tumor tissue, and normal tumor adjacent tissue samples. RESULTS: 770 mRNA transcripts related to gene's affecting cancer's progression and metastasis processes were evaluated. Four EV derived mRNA transcripts (ALOX5, RBL2, VEGFA, TLK2) were found specific to urine and tumor tissue samples. CONCLUSION: Four candidate RCC-specific urine EV biomarkers were identified. However, due to the lack of a true negative control and urine collection techniques, further re-examination is necessary for validation. This study demonstrates the promise of defining disease-specific EV biomarkers in liquid biopsy patient samples.
Authors: Richard C Zieren; Liang Dong; Phillip M Pierorazio; Kenneth J Pienta; Theo M de Reijke; Sarah R Amend Journal: Med Oncol Date: 2020-03-14 Impact factor: 3.064
Authors: Liang Dong; Richard C Zieren; Kengo Horie; Chi-Ju Kim; Emily Mallick; Yuezhou Jing; Mingxiao Feng; Morgan D Kuczler; Jordan Green; Sarah R Amend; Kenneth W Witwer; Theo M de Reijke; Yoon-Kyoung Cho; Kenneth J Pienta; Wei Xue Journal: J Extracell Vesicles Date: 2021-01-15
Authors: Xiao Zhang; Di Sun; Haiyan Zheng; Yamin Rao; Yuqi Deng; Xiao Liang; Jun Chen; Jun Yang Journal: Front Genet Date: 2022-09-05 Impact factor: 4.772