# A Tissue Section-Based Mid-Infrared Spectroscopical Analysis of Salivary Gland Tumors Based on Enzymatic Deglycosylation

## TL;DR

This study uses infrared spectroscopy and enzyme treatment to detect chemical changes in salivary gland tumors, helping distinguish them from normal tissue.

## Contribution

The novel use of enzymatic deglycosylation combined with mid-infrared spectroscopy to detect glycosylation changes in salivary gland tumors.

## Key findings

- ATR-FTIR spectroscopy with enzymatic deglycosylation can distinguish tumor from normal tissue with 81.9% accuracy.
- Glycosylation-related spectral changes were confirmed, but benign/malignant tumor classification remained challenging.
- Spectral overlap and tumor heterogeneity limit the technique's effectiveness for benign/malignant discrimination.

## Abstract

Salivary gland tumors are rare and can be difficult to diagnose accurately because of their wide variety and overlapping features. Traditional methods using microscope-based tissue analysis sometimes struggle to clearly distinguish between benign and malignant tumors. In this study, we explored a new approach that uses infrared light to detect chemical changes in tumor tissue, specifically looking at sugar structures on the surface of cells that tend to change when tumors become cancerous. We also took some of the tissue samples and treated them with enzymes to eliminate these sugar structures. Our results indicate that this technique can be used to distinguish between tumor tissue and normal tissue, but not as readily to distinguish between various types of tumors. This could assist pathologists in making more precise diagnoses and could also translate into quicker or more precise cancer detection.

Background/Objectives: Salivary gland tumors (SGTs) are a rare and histologically heterogeneous group of neoplasms that are challenging to diagnose due to phenotypic heterogeneity and overlapping histomorphological markers. Accurate diagnosis is required for clinical management, particularly in unusual subtypes. The objective of this study was to ascertain whether attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, in combination with enzymatic deglycosylation, would be useful in SGT classification by detecting glycosylation-related metabolic variations. Methods: 155 tissue sections, consisting of 80 SGTs and 75 controls, were analyzed. ATR-FTIR spectroscopy was used to record the mid-infrared (MIR) spectra (4000–400 cm−1) of enzymatically untreated and deglycosylated samples. Spectral data were preprocessed and analyzed by principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Enzymatic deglycosylation focused on sialic acid and fucose residues with α2-3,6,8 neuraminidase, α1-2,4,6 fucosidase O, and α1-3,4 fucosidase. Results: Tumor and control samples were discriminated with an OPLS-DA model, achieving an accuracy of 81.9% (78.7% for controls and 85.0% for tumors), especially in the glycosylation-relevant spectral range (850–1250 cm−1). Classification between benign and malignant tumors was more challenging, with an accuracy of 70.0% (72.5% for benign and 67.5% for malignant cases). Enzymatic deglycosylation resulted in detectable changes in the MIR spectra, confirming the contribution of glycosylation to tumor-specific signatures. Benign vs. malignant tumor discrimination was still poor and was not much enhanced in the sense of incorporating glycosylation-specific regions. Conclusions: ATR-FTIR spectroscopy coupled with enzymatic deglycosylation can distinguish tumor and control tissues based on glycan-associated spectral differences. Application of the technique to benign/malignant SGT discrimination is hampered by spectral overlap and tumor heterogeneity. Further research will be necessary to explore other clustering algorithms and larger and more homogeneous datasets for improved diagnostic accuracy.