Aplicação de métodos quimiométricos e espectrometria de massa para detectar câncer de próstata através da urina

Abstract

Prostate cancer (PCa) is the second most diagnosed type of cancer in men worldwide, it’s estimated 358,989 deaths in 2020. The main barrier to reducing PCa mortality is early diagnosis of the disease. The most man commonly used clinical tests currently applied to the diagnosis of PCa are: the prostate-specific antigen (PSA) blood test and the digital rectal exam (DRE). However, these tests have numerous disadvantages. The PSA blood test has low specificity having a high rate of false positives or negatives, and biopsies are uncomfortable procedures that can produce several health complications such as infections, incontinence, and erectile dysfunction. Beside, biopsies often miss cancer detection due to tumor heterogeneity. It’s necessary development of a new efficient test, using a non-invasive approach for the rapid and reproducible diagnosis of PCa, is an urgent clinical need. The emerging field of metabolomics, in which a large-scale analysis of small molecules (metabolites) from biological organisms is detected qualitatively and quantitatively in a single step, has immense potential for the early diagnosis of many diseases. The metabolic profile provides an overview of an individual's cellular and physiological status in clinical diagnoses. The metabolomics has been increasingly used for the diagnosis of several diseases in humans, such as Alzheimer's, Parkinson's and diabetes. So, paper spray ionization mass spectrometry (PSI-MS) appears to be a promising alternative to apply the metabolomics approach in large-scale studies and then establish a tool for clinical trials used for disease diagnosis. Therefore, it is essential to develop analytical methods to overcome the challenges proposed in this project using the analytical technique of mass spectrometry in combination with chemometric methods, aiming to correlate the information extracted from the spectra with the properties of interest, in this case, to diagnose patients with prostate cancer by urine analysis. Thus, based on the results obtained, the chemometric methods of discriminant analysis by partial least squares (PLS-DA) and data driven-soft independent modelling of class analogy (DD-SIMCA) are presented as a viable alternative to detect cancer of prostate by the analysis of urine samples, as they presented classification rates of 100% in the differentiation between samples from patients with prostate cancer and healthy patients.