Hybrid volatilomics in cancer diagnosis by HS-GC-FID fingerprinting

Bruno Ruiz Brandão da Costa; Ricardo Roberto da Silva; Vítor Luiz Caleffo Piva Bigão; Fernanda Maris Peria; Bruno Spinosa De Martinis

doi:10.1088/1752-7163/acb284

Hybrid volatilomics in cancer diagnosis by HS-GC-FID fingerprinting

J Breath Res. 2023 Jan 27;17(2). doi: 10.1088/1752-7163/acb284.

Authors

Bruno Ruiz Brandão da Costa¹, Ricardo Roberto da Silva², Vítor Luiz Caleffo Piva Bigão¹, Fernanda Maris Peria³, Bruno Spinosa De Martinis⁴

Affiliations

¹ Department of Clinical, Toxicological and Food Sciences, School of Pharmaceutical, Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto CEP 14040-903, Brazil.
² Núcleo de Pesquisa em Produtos Naturais e Sintéticos (NPPNS), Department of Biomolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto CEP 14040-903, Brazil.
³ Division of Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto CEP 14049-900, Brazil.
⁴ Department of Chemistry, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto CEP 14040-901, Brazil.

PMID: 36634358
DOI: 10.1088/1752-7163/acb284

Abstract

Assessing volatile organic compounds (VOCs) as cancer signatures is one of the most promising techniques toward developing non-invasive, simple, and affordable diagnosis. Here, we have evaluated the feasibility of employing static headspace extraction (HS) followed by gas chromatography with flame ionization detector (GC-FID) as a screening tool to discriminate between cancer patients (head and neck-HNC,n= 15; and gastrointestinal cancer-GIC,n= 19) and healthy controls (n= 37) on the basis of a non-target (fingerprinting) analysis of oral fluid and urine. We evaluated the discrimination considering a single bodily fluid and adopting the hybrid approach, in which the oral fluid and urinary VOCs profiles were combined through data fusion. We used supervised orthogonal partial least squares discriminant analysis for classification, and we assessed the prediction power of the models by analyzing the values of goodness of prediction (Q²Y), area under the curve (AUC), sensitivity, and specificity. The individual models HNC urine, HNC oral fluid, and GIC oral fluid successfully discriminated between healthy controls and positive samples (Q²Y = 0.560, 0.525, and 0.559; AUC = 0.814, 0.850, and 0.926; sensitivity = 84.8, 70.2, and 78.6%; and specificity = 82.3; 81.5; 87.5%, respectively), whereas GIC urine was not adequate (Q²Y = 0.292, AUC = 0.694, sensitivity = 66.1%, and specificity = 77.0%). Compared to the respective individual models, Q²Y for the hybrid models increased (0.623 for hybrid HNC and 0.562 for hybrid GIC). However, sensitivity was higher for HNC urine and GIC oral fluid than for hybrid HNC (75.6%) and hybrid GIC (69.8%), respectively. These results suggested that HS-GC-FID fingerprinting is suitable and holds great potential for cancer screening. Additionally, the hybrid approach tends to increase the predictive power if the individual models present suitable quality parameter values. Otherwise, it is more advantageous to use a single body fluid for analysis.

Keywords: DoE; VOCs; head and neck cancer; headspace; metabolomics; optimization; oral fluid.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Breath Tests
Chromatography, Gas / methods
Flame Ionization / methods
Humans
Least-Squares Analysis
Neoplasms* / diagnosis
Volatile Organic Compounds* / analysis

Substances

Volatile Organic Compounds