Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry.
Yuze LiLixia JiangZhenpeng WangYiran WangXiaohua CaoLingwei MengJinghan FanCaiqiao XiongZongxiu NiePublished in: Analytical chemistry (2022)
The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with N , N -dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Additionally, the DMED derivatization increases detection sensitivity and distinguishes carboxyl, aldehyde, and ketone groups in untreated urine samples. Carbonyl metabolic fingerprints of urine from 41 BC patients and 41 controls were portrayed and 9 potential biomarkers were identified. The mechanisms of the regulations of these biomarkers have been tentatively discussed. A logistic regression (LR) machine learning algorithm was applied to discriminate BC from controls, and an accuracy of 85% was achieved. We believe that the method proposed here may pave the way toward the point-of-care diagnosis of BC in a patient-friendly manner.
Keyphrases
- mass spectrometry
- gas chromatography
- liquid chromatography
- machine learning
- high performance liquid chromatography
- tandem mass spectrometry
- end stage renal disease
- high resolution mass spectrometry
- ms ms
- multiple sclerosis
- high resolution
- capillary electrophoresis
- chronic kidney disease
- high throughput
- ejection fraction
- simultaneous determination
- gas chromatography mass spectrometry
- ultra high performance liquid chromatography
- newly diagnosed
- single cell
- peritoneal dialysis
- air pollution
- deep learning
- liquid chromatography tandem mass spectrometry
- particulate matter
- patient reported outcomes
- muscle invasive bladder cancer