1. MALBAC-based chromosomal imbalance analysis: a novel technique enabling effective non-invasive diagnosis and monitoring of bladder cancer
- Author
-
Hao Liu, Wang He, Bo Wang, Kewei Xu, Jinli Han, Junjiong Zheng, Jun Ren, Lin Shao, Shiping Bo, Sijia Lu, Tianxin Lin, and Jian Huang
- Subjects
Bladder Cancer ,CNV ,MALBAC ,NGS ,Chromosomal imbalance analysis ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Background The gold standard for bladder cancer detection is cystoscopy, which is an invasive procedure that causes discomfort in patients. The currently available non-invasive approaches either show limited sensitivity in low-grade tumours or possess unsatisfying specificity. The aim of the present study is to develop a new non-invasive strategy based on chromosomal imbalance levels to detect bladder cancer effectively. Methods We enrolled 74 patients diagnosed with bladder cancer (BC), 51 healthy participants and 27 patients who were diagnosed with non-malignant urinary disease (UD). The Chromosomal Imbalance Analysis (CIA) was conducted in the tumours and urine of participants via the multiple annealing and looping-based amplification cycles-next-generation sequencing (MALBAC-NGS) strategy. The threshold of the CIA was determined with the receiver operating characteristic (ROC) curve. The comparison of the CIA with voided urine cytology was also performed in a subgroup of 55 BC patients. The consistency and discrepancy of the different assays were studied with the Kappa analysis and the McNemar test, respectively. The performance of the urinary CIA was also validated in an additional group of 120 BC patients, 15 UD and 45 healthy participants. Results Good concordance (87.0%) in the assessments of patient tumour tissues and urine was observed. The urine-based evaluation also demonstrated a good performance (accuracy = 89.0%, sensitivity = 83.1%, specificity = 94.5%, NPV = 85.4% and PPV = 93.7%; AUC = 0.917, 95%CI =0.868–0.966, P
- Published
- 2018
- Full Text
- View/download PDF