Diagnosis of Gastric Lesions From Exhaled Breath and Saliva
- Conditions
- Stomach Diseases
- Registration Number
- NCT01420588
- Lead Sponsor
- Anhui Medical University
- Brief Summary
The investigators study the feasibility of a novel method in oncology based on breath analysis with a nanosensors array for identifying gastric diseases. Alveolar exhaled breath samples collected from volunteers referred for upper endoscopy or surgery are analyzed using a custom-designed array of chemical nanosensors based on organically functionalized gold nanoparticles and carbon nanotubes. Predictive models are built employing discriminant factor analysis (DFA) pattern recognition method. Classification accuracy, sensitivity and specificity are determined using leave-one-out cross-validation or an independent blind test set. The chemical composition of the breath samples is studied using gas chromatography coupled with mass spectrometry (GC-MS).
A pilot study is conducted first (enlistment of 160 subjects at the Department of Oncology, The First Affiliated Hospital of Anhui Medical University, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.)
The pilot study is followed by a large-scale clinical trial to confirm the preliminary results of the Chinese pilot study (enlistment of 800 subjects at the Digestive Diseases Centre GASTRO, Riga East University Hospital, 6 Linezera iela, LV1006 Riga, Latvia). 25% of the samples are used as independent blind test set. The samples are blinded by the medical team and are not disclosed until prediction of blind sample identity is complete.
To further prove the diagnosis of GC from exhaled breath and seek the interrelationship among Breathomics, metabolomics and transcriptomics, saliva samples from about 200 patients are collected from volunteers referred for upper endoscopy or surgery are analyzed using Ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). Simultaneously, RNA sequencing are preformed on gastric cancer tissue samples and paracancerous tissue samples collected from same group of volunteers. The data of salivary metabonomics and transcriptomics were integrated and analyzed on the on Kyoto Encyclopedia of Genes and Genomes to confirm the diagnostic validity of salivary metabonomics.
- Detailed Description
Number of patients that will have a definitive diagnosis and Alveolar exhaled breath samples collected from individuals with Tedlar® bags (Keika Ventures, LLC) after endoscopy.. Two breath samples were collected from each person tested.
Two-bed sorption tubes filled with the following sorbents were used as traps for sample collection with simultaneous preconcentration: 100mg matrix Tenax TA and 50mg matrix Tenax TA (35-60 mesh; purchased from Supelo, Bellefonte, PA). Sorbents were separated by glass wool. The samples were collected at a total flow through sorption trap of 200ml/min.
One sample was used for analysis with the nanosensors array, and the other sample was used for Gas Chromatography coupled with Mass Spectrometry (GC-MS) analysis.
Cancer tissue and paracancerous tissue samples were collected in the process of surgical resection. After collection in the operating room, the samples were immediately placed in - 5 ℃ dry ice and transferred to the laboratory. Then, the samples were frozen in liquid nitrogen for 30 minutes, and then placed in - 80℃ freezer for cold storage. After that, the samples were divided into several batches and transported in dry ice for subsequent transcriptome analysis. All the saliva samples were collected using 2ml cryopreservation tube during early morning before surgery or endoscopic resection. The patient had been told not to eat after 22 o'clock the night, and not to drink water, smoke, brush teeth or exercise violently one hour before the collection. The saliva samples were sealed in the -80 C refrigerator after collection and then transported in a foam box equipped with dry ice, followed by UHPLC-MS analysis.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 1000
- 18-75 years
- Gastric cancer, gastric ulcer, gastritis,
- No previous adjuvant treatment (surgery, radiotherapy, chemotherapy)
- Gastric lesions are diagnosed by gastroendoscopy and histopathologic.
- ECOG < 2
- Other palliative chemotherapy and radiotherapy for this cancer
- Other cancer
- diabetes , Fatty liver
- Autoimmune disease
- Ventilation and transaired function obstacle
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Discrimination between Malignant and Benign Gastric Lesions with Na-nose 2 weeks after the collection of breath Proof-of-concept:
Alveolar exhaled breath samples collected from 160 subjects referred for upper endoscopy at The First Affiliated Hospital of Anhui Medical University are analyzed using a custom-designed array of chemical nanosensors. Predictive models are built employing discriminant factor analysis (DFA). Classification accuracy, sensitivity and specificity were determined using leave-one-out cross-validation. The chemical composition is studied using gas chromatography coupled with mass spectrometry (GC-MS).
Confirmation of proof-of-concept:
Alveolar exhaled breath samples collected from 800 subjects referred for upper endoscopy at Riga East University Hospital are analyzed as was used in the pilot study. Predictive models are built as in the pilot study,using a training set of only 75% of the samples. Classification accuracy, sensitivity and specificity are determined using an independent blind test set (25% of the samples)
- Secondary Outcome Measures
Name Time Method Geographical comparison of VOCs between China and Latvia 2 weeks after the data analyses Specifically, to compare the VOCs that distinguish between malignant and benign gastric lesions in the Chinese and Latvian cohorts. The cohorts from China and Latvia are matched in terms of sample size, gender ratio, average age, and smoking habits.
Trial Locations
- Locations (2)
Department of Oncology, The First Affiliated Hospital of Anhui Medical University
🇨🇳Hefei, Anhui, China
Faculty of Medicine, University of Latvia
🇱🇻Riga, Latvia