FTIR-based spectrum of salivary exosomes coupled with computational-aided discriminating analysis in the diagnosis of oral cancer

Ayelet Zlotogorski-Hurvitz, Ben Zion Dekel, Dov Malonek, Ran Yahalom, Marilena Vered

Research output: Contribution to journalArticlepeer-review


Purpose: To determine the Fourier-transform infrared (FTIR) spectra of salivary exosomes from oral cancer (OC) patients and healthy individuals (HI) and to assess its diagnostic potential using computational-aided models. Methods: Whole saliva samples were collected from 21 OC patients and 13 HI. Exosomes were pelleted using differential centrifugation (12,000g, 120,000g). The mid-infrared (IR) absorbance spectra (900–5000 cm − 1 range) were measured using MIR8025 Oriel Fourier-transform IR equipped with a PIKE MIRacle ZnSe attenuated total reflectance attachment. Machine learning techniques, utilized to build discrimination models for the absorbance data of OC and HI, included the principal component analysis–linear discriminant analysis (PCA–LDA) and support vector machine (SVM) classification. Sensitivity, specificity and the area under the receiver operating characteristic curve were calculated. Results: IR spectra of OC were consistently different from HI at 1072 cm − 1 (nucleic acids), 2924 cm − 1 and 2854 cm − 1 (membranous lipids), and 1543 cm − 1 (transmembrane proteins). The PCA–LDA discrimination model correctly classified the samples with a sensitivity of 100%, specificity of 89% and accuracy of 95%, and the SVM showed a training accuracy of 100% and a cross-validation accuracy of 89%. Conclusion: We showed the specific IR spectral signature for OC salivary exosomes, which was accurately differentiated from HI exosomes based on detecting subtle changes in the conformations of proteins, lipids and nucleic acids using optimized artificial neural networks with small data sets. This non-invasive method should be further investigated for diagnosis of oral cancer at its very early stages or in oral lesions with potential for malignant transformation.

Original languageEnglish
Pages (from-to)685-694
Number of pages10
JournalJournal of Cancer Research and Clinical Oncology
Issue number3
StatePublished - 13 Mar 2019


  • Diagnosis
  • Exosomes
  • Fourier-transform infrared (FTIR)
  • Machine learning
  • Oral cancer
  • Saliva

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research


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