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Circulating miRNA panels for specific and early detection in bladder cancer

Authors :
Takahiro Ochiya
Makiko Ichikawa
Hiroyuki Fujimoto
Satoko Takizawa
Juntaro Matsuzaki
Shumpei Niida
Wataru Usuba
Ken Kato
Yoshiaki Aoki
Hideo Sasaki
Shin Egawa
Yusuke Yamamoto
Fumihiko Urabe
Tatsuya Chikaraishi
Source :
Cancer Science
Publication Year :
2018
Publisher :
Wiley, 2018.

Abstract

Bladder cancer is the 9th leading cause of cancer death worldwide. The major problem in bladder cancer is primarily the high recurrence rate after drug treatment and resection. Although conventional screening methods, such as cystoscopy, urinary cytology and ultrasound sonography, have become widely used in clinical settings, the diagnostic performance of these modalities is unsatisfactory due to low accuracy or high invasiveness. Because circulating micro RNA (miRNA) profiles have recently been reported as an attractive tool for liquid biopsy in cancer screening, here, we performed global miRNA profiling of 392 serum samples of bladder cancer patients with 100 non-cancer samples and 480 samples of other types of cancer as controls. We randomly classified the bladder cancer and control samples into 2 cohorts, a training set (N = 486) and a validation set (N = 486). By comparing both controls, we identified specific miRNA, such as miR-6087, for diagnosing bladder cancer in the training and validation sets. Furthermore, we found that a combination of 7 miRNA (7-miRNA panel: miR-6087, miR-6724-5p, miR-3960, miR-1343-5p, miR-1185-1-3p, miR-6831-5p and miR-4695-5p) could discriminate bladder cancer from non-cancer and other types of tumors with the highest accuracy (AUC: .97; sensitivity: 95%; specificity: 87%). The diagnostic accuracy was high, regardless of the stage and grade of bladder cancer. Our data demonstrated that the 7-miRNA panel could be a biomarker for the specific and early detection of bladder cancer.

Details

ISSN :
13497006 and 13479032
Volume :
110
Database :
OpenAIRE
Journal :
Cancer Science
Accession number :
edsair.doi.dedup.....aec570bf784a2236a10638f07ad437e2