Capillary electrophoretic profiling and pattern recognition analysis of urinary nucleosides from thyroid cancer patients

Recent developments in the determination of urinary cancer biomarkers by capillary electrophoresis

“…Since the first literature report on the quantitative differences of modified nucleosides in urinary excretion between normal and cancer patient urine in 1960 [43], there have been numerous reports on the modified nucleosides levels of different cancers. Because of those findings, modified nucleosides are now considered to be a general tumor marker for different cancer types [1], including leukemias and lymphomas [44,45], thyroid cancer [41], head and neck cancer [46,47], breast cancer [46,48,49], ovarian cancer [50,51], prostate cancer [46], lung cancer [52], and so on.…”

“…More and more studies have been recently reported in the area of determination and screening of cancer biomarkers by CE including nucleosides, ribonucleic acid (RNA) [41], 8-hydroxydeoxyguanosine, DNA mutation, DNA-adduct, glycans, proteins, glycoproteins, and small biomolecules. Especially the latest developments and advances will be focused on in the present review, including instrumental, methodical and data analysis of different cancer biomarkers.…”

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

“…Other spectroscopic techniques used less frequently for urinary fingerprinting include direct injection MS [8,9] and FTIR spectroscopy [10]. CE has recently been incorporated to the family of metabolic fingerprinting techniques used for clinical studies [11][12][13][14]. CE is an efficient and sensitive high-resolution separation technique that allows fast analysis of ionic species and the use of small samples.…”

“…In combination with PCA for dimensionality reduction, discriminant analysis (DA) methods can be used to classify samples according to their characteristic profiles [13,17,22,23]. Other methods used for the classification of CE data include neural networks [21] and support vector machines (SVMs) [17]. However, before such methods can be used for direct comparison of datasets, preprocessing is required to remove the influence of different instrumental factors and to normalise the data so that effects due to variation in concentration do not dominate the classification.…”

“…Unsupervised pattern recognition methods such as cluster analysis and principal component analysis (PCA) have been used to examine the structure of these datasets [13,17,21,22]. In combination with PCA for dimensionality reduction, discriminant analysis (DA) methods can be used to classify samples according to their characteristic profiles [13,17,22,23].…”