Gas Chromatography - Mass Spectrometry Technology-based Metabolomics Research Methods In The Diagnosis Of Hepatocellular Carcinoma And Gastrointestinal Tumors

Part One Metabolomic profiling of human urine in hepatocellular carcinoma patients using gas chromatography/mass spectrometryObjective:We aimed to establish a diagnosis model and explore the potential metabolic biomarkers distinguishing HCC from the normal subjects.Methods:Metabolomic studies have been applied to explore disease biomarkers. With the technique of metabolomics, gas chromatography/mass spectrometry (GC/MS), urine or serum metabolites can be detected and identified. In this work, we present a metabolomic method to investigate the urinary metabolic difference between hepatocellular carcinoma (HCC, n=20) male patients and normal male subjects (n=20). The urinary endogenous metabolome was detected through chemical derivatization followed by GC/MS. A diagnostic model was constructed with a combination of marker metabolites or together with alphafetoprotein (AFP), using Principal component analysis (PCA) and receiver-operator characteristic (ROC) curves.Results:After GC/MS analysis,103 metabolites were detected, of which 79 were annotated as known compounds. By a two sample t-test statistics with P< 0.05,18 metabolites were significantly different between HCC group and control group. The multivariate statistics yielded the strongest separation between the two groups. The diagnostic model combining 18 marker metabolites with AFP could discriminate HCC patients from normal subjects with an area under the curve (AUC) value of 0.9275.Conclusion:This non-invasive technique of identifying HCC biomarkers from urine is special and worthy of further validations for clinical application.Part Two Metabolomic study for diagnostic model of oesophageal cancer using gas chromatogragphy/mass spectrometryObjective:The aim of the study was to profile biopsy specimens from oesophageal cancer and their corresponding normal mucosae in the same patients using gas chromatography mass spectrometry (GC/MS) metabolomics following chemical derivatization.Methods:We hypothesized that tissue metabolomic biomarkers may be identifiable and diagnostically useful for oesophageal cancer. We present a metabolomic method of chemical derivatization followed by GC/MS to analyze the metabolic difference in biopsied specimens between oesophageal cancer and corresponding normal mucosae obtained from 20 oesophageal cancer patients. The GC/MS data was analyzed using a two sample t-test to explore the potential metabolic biomarkers for oesophageal cancer. A diagnostic model was constructed to discriminate normal from malignant samples, using principal component analysis (PCA) and receiver-operating characteristic (ROC) curves.Results:T-test showed a total of 20 marker metabolites detected were found to be different with statistical significance (P< 0.05). The multivariate logistic analysis yielded a complete distinction between the two groups. The diagnostic model could discriminate tumors from normal mucosae with an area under the curve (AUC) value of 1.Conclusion:Our findings suggest that this assay may potentially provide a new metabolomic biomarker for oesophageal cancer.Part Three Metabolomic investigation of gastric cancer tissue using gas chromatography/mass spectrometryObjective:Gastric cancer screening or diagnosis is mainly based on endoscopy and biopsy. The aim of this study was to identify the difference of metabolomic profile between normal and malignant gastric tissue, and to further explore tumor biomarkers.Methods:Chemical derivatization together with gas chromatography/mass spectrometry (GC/MS) was utilized to obtain the metabolomic information of the malignant and non-malignant tissues of gastric mucosae in 18 gastric cancer patients. Aquired metabolomic data was analyzed using Wilcoxon rank sum test to find the tissue metabolic biomarkers for gastric cancer. A diagnostic model for gastric cancer was constructed using principal component analysis (PCA), and was assessed with receiver-operating characteristic (ROC) curves.Results:Results showed that 18 metabolites were differently detected between the malignant tissues and the adjacent non-malignant tissues of gastric mucosa. Five metabolites were also differently detected between the non-invasive tumors and the invasive tumors. The diagnostic model could discriminate tumors from normal mucosae with an area under the curve (AUC) value of 0.9629, and another diagnostic model constructed for clinical staging was assessed with an AUC value of 0.969.Conclusion:We conclude that the metabolomic profile of malignant gastric tissue was different from normal, and that the selected tissue metabolites could probably be applied for clinical diagnosis or staging for gastric cancer.