Mingxin Zhang1,2, Xiaoyan Liu3, Xiang Liu3, Hanzhong Li1, Wei Sun4, Yushi Zhang5. 1. Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China. 2. Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China. 3. Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China. 4. Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China. sunweizmx@126.com. 5. Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China. zhangyushi2018@163.com.
Abstract
BACKGROUND: Renal cell carcinoma (RCC) is the most common and lethal malignancy of the kidney. Distinguishing RCC from benign renal tumors and healthy controls is still a clinical challenge. Urine metabolomics has been used to identify biomarkers of clinical diseases. METHODS: In the present study, we explored the urine metabolomes of a cohort of 61 patients with renal tumors (39 RCC and 22 benign renal tumors) and 68 healthy controls using liquid chromatography coupled with high-resolution mass spectrometry (LC-MS). RESULTS: Metabolic profiling of urine could significantly differentiate RCC from healthy controls and benign renal tumors. Metabolic pathways, including lysine metabolism and phenylalanine metabolism, were found to be disturbed in the RCC group. Steroid hormone biosynthesis was significantly different between the benign tumor group and the RCC group. RCC biomarkers were further explored. A metabolite panel consisting of cortolone, testosterone and L-2-aminoadipate adenylate was discovered to have good ability of distinguishing RCC from benign tumors, with an AUC of 0.868 for tenfold cross-validation and 0.873 for the validation group. In addition, the panel of aminoadipic acid, 2-(formamido)-N1-(5-phospho-D-ribosyl) acetamidine and alpha-N-phenylacetyl-L-glutamine could distinguish the RCC group from the healthy control group, with an AUC of 0.841 for tenfold cross-validation and 0.894 for the validation group. CONCLUSIONS: This pilot study suggests that urine metabolomics may be useful in differentiating RCC from healthy controls and benign renal tumors.
BACKGROUND:Renal cell carcinoma (RCC) is the most common and lethal malignancy of the kidney. Distinguishing RCC from benign renal tumors and healthy controls is still a clinical challenge. Urine metabolomics has been used to identify biomarkers of clinical diseases. METHODS: In the present study, we explored the urine metabolomes of a cohort of 61 patients with renal tumors (39 RCC and 22 benign renal tumors) and 68 healthy controls using liquid chromatography coupled with high-resolution mass spectrometry (LC-MS). RESULTS: Metabolic profiling of urine could significantly differentiate RCC from healthy controls and benign renal tumors. Metabolic pathways, including lysine metabolism and phenylalanine metabolism, were found to be disturbed in the RCC group. Steroid hormone biosynthesis was significantly different between the benign tumor group and the RCC group. RCC biomarkers were further explored. A metabolite panel consisting of cortolone, testosterone and L-2-aminoadipate adenylate was discovered to have good ability of distinguishing RCC from benign tumors, with an AUC of 0.868 for tenfold cross-validation and 0.873 for the validation group. In addition, the panel of aminoadipic acid, 2-(formamido)-N1-(5-phospho-D-ribosyl) acetamidine and alpha-N-phenylacetyl-L-glutamine could distinguish the RCC group from the healthy control group, with an AUC of 0.841 for tenfold cross-validation and 0.894 for the validation group. CONCLUSIONS: This pilot study suggests that urine metabolomics may be useful in differentiating RCC from healthy controls and benign renal tumors.
Authors: Márcia Monteiro; Nathalie Moreira; Joana Pinto; Ana S Pires-Luís; Rui Henrique; Carmen Jerónimo; Maria de Lourdes Bastos; Ana M Gil; Márcia Carvalho; Paula Guedes de Pinho Journal: J Cell Mol Med Date: 2017-04-04 Impact factor: 5.310
Authors: Jianhua Cao; Benjamin Balluff; Martijn Arts; Ludwig J Dubois; Luc J C van Loon; Tilman M Hackeng; Hans M H van Eijk; Gert Eijkel; Lara R Heij; Zita Soons; Steven W M Olde Damink; Ron M A Heeren Journal: Cancer Metab Date: 2021-06-11