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Noninvasive urinary protein signatures associated with colorectal cancer diagnosis and metastasis. Nat Commun 2022; 13:2757. [PMID: 35589723 PMCID: PMC9119985 DOI: 10.1038/s41467-022-30391-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 04/25/2022] [Indexed: 12/24/2022] Open
Abstract
Currently, imaging, fecal immunochemical tests (FITs) and serum carcinoembryonic antigen (CEA) tests are not adequate for the early detection and evaluation of metastasis and recurrence in colorectal cancer (CRC). To comprehensively identify and validate more accurate noninvasive biomarkers in urine, we implement a staged discovery-verification-validation pipeline in 657 urine and 993 tissue samples from healthy controls and CRC patients with a distinct metastatic risk. The generated diagnostic signature combined with the FIT test reveals a significantly increased sensitivity (+21.2% in the training set, +43.7% in the validation set) compared to FIT alone. Moreover, the generated metastatic signature for risk stratification correctly predicts over 50% of CEA-negative metastatic patients. The tissue validation shows that elevated urinary protein biomarkers reflect their alterations in tissue. Here, we show promising urinary protein signatures and provide potential interventional targets to reliably detect CRC, although further multi-center external validation is needed to generalize the findings. More sensitive and specific non-invasive biomarkers are desired for early detection of cancer. Here, the authors show a protein signature in the urine that increases sensitivity for colorectal cancer detection when combined with fecal immunochemical tests and corrects diagnosis in some fecal immunochemical tests-negative patients.
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Wei J, Huan Y, Heng Z, Zhao C, Jia L, Yu Y, Gao Y. Dynamic urine proteome changes in a rat model of simvastatin-induced skeletal muscle injury. J Proteomics 2022; 254:104477. [PMID: 34990819 DOI: 10.1016/j.jprot.2021.104477] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 10/19/2022]
Abstract
Statin-associated muscle symptoms (SAMS) are the main side effects of statins. Currently, there are no effective biomarkers for accurate clinical diagnosis. Urine is not subject to homeostatic control and therefore accumulates early changes, making it an ideal biomarker source. We therefore examined urine proteome changes associated with SAMS. Here, we established a SAMS rat model by intragastric intubation with simvastatin (80 mg/kg). Biochemical analyses and hematoxylin and eosin staining were used to evaluate the degree of muscle injury. The urine proteome on days 3, 6, 9 and 14 was profiled using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Differential proteins on day 14 of SAMS were mainly associated with glycolysis/gluconeogenesis, pyruvate metabolism, metabolism of reactive oxygen species and apoptosis, which were associated with the pathological mechanism of SAMS. Among the 14 differential proteins on day 3, Fibrinogen gamma chain (FIBG), Osteopontin (OSTP) and C-reactive protein (CRP) were associated with muscle damage, while EH domain-containing protein 1(EHD1), Cubilin (CUBN) and Fibronectin (FINC) were associated with the pathogenic mechanisms of SAMS. Our preliminary results indicated that the urine proteome can reflect early changes in the SAMS rat model, providing the potential for monitoring drug side effects in future clinical research. SIGNIFICANCE: This study demonstrate that the early muscle damage caused by simvastatin can be reflected in urinary proteins. The urine proteome also has the potential to reflect the pharmacology and toxicology of drugs in future clinical research.
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Affiliation(s)
- Jing Wei
- Clinical Research Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China; Department of Biochemistry and Molecular Biology, Beijing Normal University, Gene Engineering Drug and Biotechnology Beijing Key Laboratory, Beijing 100875, China
| | - Yuhang Huan
- Department of Biochemistry and Molecular Biology, Beijing Normal University, Gene Engineering Drug and Biotechnology Beijing Key Laboratory, Beijing 100875, China; Beijing Advanced Innovation Center for Genomics (ICG), Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
| | - Ziqi Heng
- Department of Biochemistry and Molecular Biology, Beijing Normal University, Gene Engineering Drug and Biotechnology Beijing Key Laboratory, Beijing 100875, China
| | - Chenyang Zhao
- Department of Biochemistry and Molecular Biology, Beijing Normal University, Gene Engineering Drug and Biotechnology Beijing Key Laboratory, Beijing 100875, China
| | - Lulu Jia
- Clinical Research Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Yuncui Yu
- Clinical Research Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Youhe Gao
- Department of Biochemistry and Molecular Biology, Beijing Normal University, Gene Engineering Drug and Biotechnology Beijing Key Laboratory, Beijing 100875, China.
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