Rapid and low-cost liquid biopsy with ATR-FTIR spectroscopy to discriminate the molecular subtypes of breast cancer.

Breast cancer (BC) is the most prevalent cancer worldwide. The prognosis and survival of these patients are directly related to the diagnostic stage. Even so, the gold standard screening method (mammography) has a long waiting period, high rates of false positives, anxiety for patients, and consequently delays the diagnosis by core needle biopsy (invasive method). Alternatively, the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy is a noninvasive, low-cost, rapid, and reagent-free technique that generates the spectral metabolomic profile of biomolecules. This makes it possible to assess systemic repercussions, such as the BC carcinogenesis process. Blood plasma samples (n = 56 BC and n = 18 controls) were analyzed in the spectrophotometer in the ATR-FTIR mode. For the exploratory analysis of the data, interval Principal Component Analysis (iPCA) was used, and for predictive chemometric modeling, the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) algorithm with validation by leave-one-out cross-validation. iPCA in the region of 1118-1052 cm-1 (predominantly DNA/RNA bands) showed significant clustering of molecular subtypes and control. The OPLS-DA model achieved 100% accuracy with only 1 latent variable and Root Mean Square Error of Cross-Validation (RMSECV) < 0.005 for all molecular subtypes and control. The wavenumbers (cm-1) with the highest iPCA peaks (loadings: 1117, 1089, 1081, 1075, 1057, and 1052) were used as input to MANOVA (Wilks' Lambda, p < 0.001 between molecular subtypes and control). The rapid and low-cost detection of BC molecular subtypes by ATR-FTIR spectroscopy would plausibly allow initial screening and clinical management, improving prognosis, reducing mortality and costs for the health system.

Using infrared spectroscopy of serum and chemometrics for diagnosis of paracoccidioidomycosis.

Paracoccidioidomycosis (PCM) is a systemic granulomatous mycosis endemic to Latin America, whose etiologic agents are fungi of the genus Paracoccidioides. PCM is usually diagnosed by microscopic observation of the fungus in biological samples, combined or not with other techniques such as serological methods. However, all currently used diagnostic methods have limitations. The objective of this study was to develop a method based on Fourier transform infrared spectroscopy (FTIR) and chemometric analysis for PCM diagnosis. We included 224 serum samples: 132 PCM sera, 24 aspergillosis sera, 10 cryptococcosis sera, 8 histoplasmosis sera, and 50 sera from healthy blood donors. Samples were analyzed by attenuated total reflection (ATR), and chemometric analyses including exploratory analysis through principal component analysis (PCA) and a classification method (PCM and non-PCM) through orthogonal partial least squares discriminant analysis (OPLS-DA). The spectra were similar, with the main bands up to approximately 1652 cm-1 and 1543 cm-1 (amide I and amide II bands). This same region was mainly responsible for the partial separation of the samples in PCA. The OPLS-DA model correctly classified all serum samples with only one latent variable, with a determination coefficient (R²) higher than 0.999 for both the calibration set and prediction set. Sensitivity and specificity were 100% for both sets, showing better performance than the reference diagnostic methods. Therefore, the use of FTIR/ATR together with OPLS-DA modeling proved to be a promising method for PCM diagnosis.