Detection of Human Cholangiocarcinoma Markers in Serum Using Infrared Spectroscopy.

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

doi:10.3390/cancers13205109

Abstract

Simple SummaryCholangiocarcinoma is a form of liver cancer that is found, predominantly, in Thailand. Due to the non-specific symptoms and laboratory investigation, it is difficult to rule out cholangiocarcinoma from other liver conditions. Here, we demonstrate the development of a diagnostic tool for cholangiocarcinoma, based on the ATR-FTIR analyses of sera, coupled with multivariate analyses and machine learning tools to obtain a better specificity. The innovative approach that shows highly promising results for this otherwise difficult to diagnose cancer.Cholangiocarcinoma (CCA) is a malignancy of the bile duct epithelium. Opisthorchis viverrini 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 () at 1073 cm−1, the polysaccharides band at 1152 cm−1 and 1747 cm−1 of lipid ester carbonyl. A Principal Component 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-Discriminant 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, depending 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.

Highlights

Cholangiocarcinoma (CCA) is a malignancy arising from the bile duct epithelium, which is found, sporadically, all over the world
Sixty samples of CCA, twenty samples of hepatocellular carcinoma (HCC) and twenty samples of biliary disease (BD) sera were supplied by the Cholangiocarcinoma Research Institute (CARI), Faculty of Medicine, Khon
The band at ~1074 cm−1 observed in serum was tentatively assigned to circulating tumor DNA fragments that were characteristic of cancer and were released into the blood stream [11,24] or, alternatively, from phosphorylated proteins, which were found in the serum [25]

Summary

Introduction

Cholangiocarcinoma (CCA) is a malignancy arising from the bile duct epithelium, which is found, sporadically, all over the world. Imaging techniques (ultrasound, magnetic resonance imaging (MRI), magnetic resonance cholangiopancreatography (MRCP), computerized tomography (CT) scan) are used to investigate CCA by detecting biliary obstruction, biliary stricture and mass forming. These techniques are limited by the cancer itself, as the accuracy depends on the type of tumor, anatomical lesion and tumor size [6]. A pathological examination of stained biopsy tissue is the most precise technique and is currently used as a confirmation method This technique requires an invasive sample collection, complicated sample handling, time consumingsample preparation and is labor intensive, which is not suitable for CCA screening or large-scale studies. A combination of markers may provide more accurate results [9], the analysis of all markers of interest renders a high cost and is time consuming

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