![]() The latter point is extremely important as EV spectral data can be automatically analyzed with machine learning (ML) methods, giving physicians a direct diagnosis instead of a biophysical parameter that needs further interpretation. Therefore, more data are needed to confirm the capability of FTIR spectroscopy to correctly classify EVs obtained from the serum of patients in different clinical conditions, with emphasis on cancer patients, as well as to test classification algorithms suitable for the high throughput analysis of FTIR spectra of EVs. on EVs obtained from neurological patients is the only article that uses serum as a biofluid of origin, and no studies have been yet published on cancer. To the best of our knowledge, the pioneering paper of Martins et al. These papers include Zlotogorski-Hurvitz et al., who studied salivary EVs of patients diagnosed with oral cancer, and Yap et al., who characterized EVs purified from the urine of prostate cancer patients. So far, very few FTIR papers have focused on EVs purified from biofluids of subjects enrolled in clinical studies. Most of these works involve in vitro experiments aimed at assessing sample composition and purity, distinguishing different EV classes or EVs derived from cells under different states/phenotypes/culture conditions. In this regard, Fourier-transform infrared spectroscopy (FTIR), especially in the Attenuated Total Reflection (ATR) mode, is emerging as a promising tool for label-free molecular profiling of EVs, with several papers published on the subject. Very interestingly, our analyses suggest that the discussed spectral biomarkers can outperform the classification ability of two widely used circulating HCC markers measured on the same groups of subjects, namely alpha-fetoprotein (AFP), and protein induced by the absence of vitamin K or antagonist-II (PIVKA-II). ![]() The diagnostic performances of the proposed spectral biomarkers, alone and combined, were evaluated using multivariate logistic regression followed by a Receiving Operator Curve analysis, obtaining large Areas Under the Curve (AUC = 0.91, 95% CI 0.81–1.0). ![]() Additionally, we used Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) for the automated classification of spectral data according to the shape of specific mid-IR spectral signatures. Our data point out the presence of statistically significant differences in the integrated intensities of major mid-IR absorption bands, including the carbohydrate and nucleic acids band, the protein amide I and II bands, and the lipid CH stretching band. For this purpose, EV-enriched suspensions were obtained from the serum of patients diagnosed with Hepatocellular Carcinoma (HCC) of nonviral origin and noncancer subjects. In this pilot study, we investigated, for the first time in the literature, the capability of FTIR spectroscopy to distinguish between EVs extracted from sera of cancer patients and controls based on their mid-IR spectral response. In this regard, Fourier Transform Infrared Spectroscopy (FTIR) has great potential as it provides a quick, reproducible, and informative biomolecular fingerprint of EVs. Despite this potential, none of the discovered biomarkers has entered the clinical practice so far, and novel approaches for the label-free characterization of EVs are highly demanded. Due to this characteristic, EVs are attracting increasing attention as a source of circulating biomarkers for cancer liquid biopsy and personalized medicine. Extracellular vesicles (EVs) are abundantly released into the systemic circulation, where they show remarkable stability and harbor molecular constituents that provide biochemical information about their cells of origin.
0 Comments
|
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |