Detection of human cholangiocarcinoma markers in serum using infrared spectroscopy

Patutong Chatchawal, Molin Wongwattanakul, Patcharaporn Tippayawat, Kamilla Kochan, Nichada Jearanaikoon, Bayden R. Wood, Patcharee Jearanaikoon

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Cholangiocarcinoma (CCA) is a malignancy of the bile duct epithelium. Opisthorchis vi-verrini infection is a known high‐risk factor for CCA and in found, predominantly, in Northeast Thailand. The silent disease development and ineffective diagnosis have led to late‐stage detection and reduction in the survival rate. Attenuated total reflectance‐Fourier transform infrared spectroscopy (ATR‐FTIR) is currently being explored as a diagnostic tool in medicine. In this study, we apply ATR‐FTIR to discriminate CCA sera from hepatocellular carcinoma (HCC), biliary disease (BD) and healthy donors using a multivariate analysis. Spectral markers differing from healthy ones are observed in the collagen band at 1284, 1339 and 1035 cm−1, the phosphate band (vsPO2-) at 1073 cm−1, the polysaccharides band at 1152 cm−1 and 1747 cm−1 of lipid ester carbonyl. A Principal Com-ponent Analysis (PCA) shows discrimination between CCA and healthy sera using the 1400–1000 cm−1 region and the combined 1800—1700 + 1400–1000 cm−1 region. Partial Least Square‐Discrimi-nant Analysis (PLS‐DA) scores plots in four of five regions investigated, namely, the 1400–1000 cm−1, 1800–1000 cm−1, 3000–2800 + 1800–1000 cm−1 and 1800–1700 + 1400–1000 cm−1 regions, show discrimination between sera from CCA and healthy volunteers. It was not possible to separate CCA from HCC and BD by PCA and PLS‐DA. CCA spectral modelling is established using the PLS‐DA, Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). The best model is the NN, which achieved a sensitivity of 80–100% and a specificity between 83 and 100% for CCA, de-pending on the spectral window used to model the spectra. This study demonstrates the potential of ATR‐FTIR spectroscopy and spectral modelling as an additional tool to discriminate CCA from other conditions.

Original languageEnglish
Article number5109
Number of pages13
JournalCancers
Volume13
Issue number20
DOIs
Publication statusPublished - Oct 2021

Keywords

  • Attenuated total reflectance‐Fourier transform infrared (ATR‐FTIR) spectroscopy
  • Biliary disease (BD)
  • Cholangiocarcinoma (CCA)
  • Hepatocellular carcinoma (HCC)
  • Machine learning
  • Multivariate analysis

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