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Updated: Jan 19, 2026
Multiplexed Fluorescent Microarray for Human Salivary Protein Analysis Using Polymer Microspheres and Fiber-optic Bundles
Published on: October 10, 2013
Innovative molecular diagnostic strategy for salivary gland lesions based on ATR-FTIR spectroscopy coupled with
Esra Öncü1, Fatma Demirkaya Miloğlu2, Ebru Şener3
1Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, Atatürk University, Erzurum, Türkiye.
Objective:
This study aimed to identify the biochemical signatures that distinguish pleomorphic adenoma (PA) and mucoepidermoid carcinoma (MEC) from normal parotid tissue (NP), while also exploring molecular differences relevant to benign-malignant tumor differentiation using Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with machine-learning (ML) approaches.
Study Design:
A total of 48 formalin-fixed, paraffin-embedded (FFPE) salivary gland tissue sections (5 µm thickness) were analyzed, including 8 NP, 24 PA, and 16 MEC specimens. ATR-FTIR spectra were acquired directly from routine histopathological slides over the 4000-400 per cm range without chemical deparaffinization. The spectral data were then processed using multivariate analysis (PCA and OPLS-DA) to reveal biochemical variations among the tissue groups. Supervised classification was then performed using Support Vector Machine (SVM) and k-Nearest Neighbours (kNN) algorithms with 6-fold cross-validation to evaluate classification performance.
Results:
Distinct spectral differences were detected among NP, PA, and MEC tissues, particularly in protein (amide I-II), lipid (CH-stretching), and nucleic acid (phosphate) regions. OPLS-DA demonstrated clear class discrimination (3D: J₁ = 4.1250, J₂ = 2.2121). The SVM classifier achieved the highest diagnostic accuracy (F1-scores: NP = 1.00, PA = 0.96, MEC = 0.94; overall accuracy = 95.83%), outperforming the kNN model (accuracy = 93.75%).
Conclusions:
Combining ATR-FTIR spectroscopy with ML-based multivariate analysis provides a highly accurate method of differentiating NP, PA and MEC salivary gland tissues based on their biochemical fingerprints. This integrative approach establishes the basis for a rapid, non-invasive, label-free diagnostic tool that is compatible with clinical practice and applicable to routine histopathological samples.
