Title |
Development of a fully automated chemiluminescence immunoassay for urine monomeric laminin-γ2 as a promising diagnostic tool of non-muscle invasive bladder cancer
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Published in |
Biomarker Research, October 2017
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DOI | 10.1186/s40364-017-0109-4 |
Pubmed ID | |
Authors |
Masatoshi Nakagawa, Takashi Karashima, Masayuki Kamada, Eisaku Yoshida, Toru Yoshimura, Masanori Nojima, Keiji Inoue, Taro Shuin, Motoharu Seiki, Naohiko Koshikawa |
Abstract |
Monomeric laminin-γ2 in urine is a potential biomarker for bladder cancer. However, the current detection system uses an antibody that cannot discriminate between monomeric laminin-γ2 and the heterotrimeric γ2 chain of laminin-332, which may cause false-positive reactions. The present study aimed to develop a fully automated chemiluminescence immunoassay system using a specific monoclonal antibody against monomeric laminin-γ2. In total, 237 urine specimens (84 from patients with bladder cancer, 48 from patients with benign urological disease, and 105 from healthy donors) were collected, and monomeric laminin-γ2 values in the urine were measured using a fully automated chemiluminescence immunoassay. The results revealed that laminin-γ2 values in patients with benign urological disease were comparable to those of healthy donors and that the chemiluminescence immunoassay's lower limit of detection was 10 pg/mL (approximately 20-fold better than the sandwich enzyme-linked immunosorbent assay's limit of 200 pg/mL). Moreover, the chemiluminescence immunoassay demonstrated that patients with bladder cancer, including non-muscle invasive bladder cancer (≤pT1), had higher laminin-γ2 values than patients with benign urological disease or healthy donors. These results suggest that urine monomeric laminin-γ2 may be a promising biomarker to diagnose cases of non-muscle invasive bladder cancer using a fully automated chemiluminescence immunoassay system. |
Mendeley readers
Geographical breakdown
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Demographic breakdown
Readers by professional status | Count | As % |
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Other | 1 | 8% |
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Decision Sciences | 1 | 8% |
Nursing and Health Professions | 1 | 8% |
Unknown | 4 | 33% |