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Clin Chim Acta

Title:		Identification and validation of volatile organic compounds in bile for differential diagnosis of perihilar cholangiocarcinoma
Author(s):		Gui X; Zhang X; Xin Y; Liu Q; Wang Y; Zhang Y; Xu Y; Liu Z; Liu W; Schioth HB; Sun C; Zhang Z; Zhang Y;
Address:		"Department of Clinical Laboratory, Qilu Hospital of Shandong University, 107 Wenhua Xi Road, Jinan, Shandong 250012, China; Shandong Engineering Research Center of Biomarker and Artificial Intelligence Application, 107 Wenhua Xi Road, Jinan, Shandong 250012, China. Department of Clinical Laboratory, Shandong Provincial Third Hospital, 12 Wuyingshan Zhong Road, Jinan, Shandong 250012, China. Department of General Surgery, Qilu Hospital of Shandong University, 107 Wenhua Xi Road, Jinan, Shandong 250012, China. Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, SE-751 24 Uppsala, Sweden. Department of General Surgery, Qilu Hospital of Shandong University, 107 Wenhua Xi Road, Jinan, Shandong 250012, China. Electronic address: zzlzzl1900@163.com. Department of Clinical Laboratory, Qilu Hospital of Shandong University, 107 Wenhua Xi Road, Jinan, Shandong 250012, China; Shandong Engineering Research Center of Biomarker and Artificial Intelligence Application, 107 Wenhua Xi Road, Jinan, Shandong 250012, China. Electronic address: yizhang@sdu.edu.cn"
Journal Title:		Clin Chim Acta
Year:		2023
Volume:		20230127
Issue:
Page Number:		117235 -
DOI:		10.1016/j.cca.2023.117235
ISSN/ISBN:		1873-3492 (Electronic) 0009-8981 (Linking)
Abstract:		"Early and differential diagnosis of perihilar cholangiocarcinoma (PHCCA) is highly challenging. This study aimed to evaluate whether volatile organic compounds (VOCs) in bile samples could be emerging diagnostic biomarkers for PHCCA. We collected 200 bile samples from patients with PHCCA and benign biliary diseases (BBD), including a 140-patient training cohort and an 60-patient test cohort. Gas chromatography-ion mobility spectrometry (GC-IMS) was used for VOCs detection. The predictive models were constructed using machine learning algorithms. Our analysis detected 19 VOC substances using GC-IMS in the bile samples and resulted in the identification of three new VOCs, 2-methoxyfuran, propyl isovalerate, and diethyl malonate that were found in bile. Unsupervised hierarchical clustering analysis supported that VOCs detected in the bile could distinguish PHCCA from BBD. Twelve VOCs defined according to 32 signal peaks had significant statistical significance between BBD and PHCCA, including four up-regulated VOCs in PHCCA, such as 2-ethyl-1-hexanol, propyl isovalerate, cyclohexanone, and acetophenone, while the rest eight VOCs were down-regulated. ROC curve analysis revealed that machine learning models based on VOCs could help diagnosing PHCCA. Among them, SVM provided the highest AUC of 0.966, with a sensitivity and specificity of 93.1% and 100%, respectively. The diagnostic model based on different VOC spectra could be a feasible method for the differential diagnosis of PHCCA"
Keywords:		"Humans *Klatskin Tumor/diagnosis *Volatile Organic Compounds/analysis Bile/chemistry Diagnosis, Differential Gas Chromatography-Mass Spectrometry *Bile Duct Neoplasms/diagnosis Bile Differential diagnosis Perihilar cholangiocarcinoma Volatile organic comp;neuroscience;"
Notes:		"MedlineGui, Xinru Zhang, Xin Xin, Yiwei Liu, Qi Wang, Yifeng Zhang, Yanli Xu, Yunfei Liu, Zengli Liu, Wen Schioth, Helgi B Sun, Chengxi Zhang, Zongli Zhang, Yi eng Netherlands 2023/01/31 Clin Chim Acta. 2023 Feb 15; 541:117235. doi: 10.1016/j.cca.2023.117235. Epub 2023 Jan 27"