Exhaled breath analysis for gastric cancer diagnosis in Colombian patients.

Autor:	Durán-Acevedo CM; Multisensor System and Pattern Recognition Research Group (GISM), Electronic Engineering Program, Universidad de Pamplona, Pamplona, Colombia., Jaimes-Mogollón AL; Multisensor System and Pattern Recognition Research Group (GISM), Electronic Engineering Program, Universidad de Pamplona, Pamplona, Colombia., Gualdrón-Guerrero OE; Multisensor System and Pattern Recognition Research Group (GISM), Electronic Engineering Program, Universidad de Pamplona, Pamplona, Colombia., Welearegay TG; Department of Electronics, Electrical and Automatic Engineering, Rovira i Virgili University, Tarragona, Spain., Martinez-Marín JD; GASTROSUR S.A., Universidad Nacional de Colombia, Facultad de Medicina, Bogotá, Colombia.; Hospital Universitario la Samaritana, Bogotá, Colombia., Caceres-Tarazona JM; Multisensor System and Pattern Recognition Research Group (GISM), Electronic Engineering Program, Universidad de Pamplona, Pamplona, Colombia., Sánchez-Acevedo ZC; Multisensor System and Pattern Recognition Research Group (GISM), Electronic Engineering Program, Universidad de Pamplona, Pamplona, Colombia., Beleño-Saenz KJ; Mechatronics Engineering Department, Universidad Autónoma del Caribe, Barranquilla, Colombia., Cindemir U; Molecular Fingerprint Sweden AB, Uppsala, Sweden.; Department of Solid State Physics, The Ångström Laboratory, Uppsala University, Uppsala, Sweden., Österlund L; Molecular Fingerprint Sweden AB, Uppsala, Sweden.; Department of Solid State Physics, The Ångström Laboratory, Uppsala University, Uppsala, Sweden., Ionescu R; Department of Electronics, Electrical and Automatic Engineering, Rovira i Virgili University, Tarragona, Spain.
Jazyk:	angličtina
Zdroj:	Oncotarget [Oncotarget] 2018 Jun 22; Vol. 9 (48), pp. 28805-28817. Date of Electronic Publication: 2018 Jun 22 (Print Publication: 2018).
DOI:	10.18632/oncotarget.25331
Abstrakt:	We present here the first study that directly correlates gastric cancer (GC) with specific biomarkers in the exhaled breath composition on a South American population, which registers one of the highest global incidence rates of gastric affections. Moreover, we demonstrate a novel solid state sensor that predicts correct GC diagnosis with 97% accuracy. Alveolar breath samples of 30 volunteers (patients diagnosed with gastric cancer and a controls group formed of patients diagnosed with other gastric diseases) were collected and analyzed by gas-chromatography/mass-spectrometry (GC-MS) and with an innovative chemical gas sensor based on gold nanoparticles (AuNP) functionalized with octadecylamine ligands. Our GC-MS analyses identified 6 volatile organic compounds that showed statistically significant differences between the cancer patients and the controls group. These compounds were different from those identified in previous studied performed on other populations with high incidence rates of this malady, such as China (representative for Eastern Asia region) and Latvia (representative for Baltic States), attributable to lifestyle, alimentation and genetics differences. A classification model based on principal component analysis of our sensor data responses to the breath samples yielded 97% accuracy, 100% sensitivity and 93% specificity. Our results suggest a new and non-intrusive methodology for early diagnosis of gastric cancer that may be deployed in regions lacking well-developed health care systems as a prediagnosis test for selecting the patients that should undergo deeper investigations ( e.g. , endoscopy and biopsy). Competing Interests: CONFLICTS OF INTEREST The authors declare no conflicts of interest.
Databáze:	MEDLINE
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