Clinical Application Study On Metabonomics In Lung Cancer

Background: Lung cancer is one of the rising constantly tumor in morbidity and mortality nearly half a century.It is the most worst malignant to human health and life. lung cancer of 5-year survival rate is not significantly improved in the past 20 years,about 14 percent. Because it is advanced when the majority of lung cancer patients were diagnosed.They were palliative treated and resulted in short survival time.So Early diagnosis and early treatment will significantly extend the survival time and improve the survival rate of lung cancer patients.At present, X-ray and sputum cytology are still the main means of screening for early lung cancer. However, there are some missed diagnosis and misdiagnosis when lung cancer patients Checked by X-ray and sputum cytology. Although pathological examination can diagnosis of lung cancer, a lot of trauma on the lung cancer patients'body.With the developing of molecular biology, tumor biomarkers has become a common ways of diagnosis in malignant tumors. Tumor biomarkers own important clinical value for screening malignant tumors. Therefore, how to research and develop lung cancer tumor biomarkers has been a hot issue of the current study. Metabonomics methods as a new detective tool, applied to the detection of tumor biomarkers is a hot current study. This method has been successfully applied to identificate of tumor biomarkers in breast cancer, prostate cancer. Thence,there is great guiding significance to applicate of metabonomics approach for early screening, prognosis assessment, pathological type, drug intervention treatment in lung cancer.Objectives: This experiment uses of the UPLC/Q-TOF metabonomics analysis methods. By qualitative and quantitative analysis of substances of lung cancer patients and healthy metabolism in human plasma. Studying of lung cancer patients and healthy metabolic profile changes to distinct between healthy people and lung cancer patients and find several potential tumor biomarkers of lung cancer. At the same time, detection of lung squamous cell carcinoma, lung adenocarcinoma and small cell lung cancer patients with changes in metabolite. In order to distinguish between the different pathological types of lung cancer patients. It will provide a theoretical basis for future non-pathological diagnosis of lung cancer patients the choice of treatment options.Methods: 24 cases of healthy people and 32 cases of three different pathological types of lung cancer patients's plasma were collected. Plasma was excepted protein. With ultra-high performance liquid chromatography/ mass-spectrometry(UPLC/Q-TOF) for chromatographic separation, mass spectrometry detection and data acquisition. Data-processing was norma- lized used markerlynx software.Then the data was imported SIMCA-P+ software for denoising. Finally, partial least squares (PLS) was employed for analysis.Results:1.24 cases of healthy patients and 32 cases of lung cancer were distinguish well by metabolomics methods. And found corresponding to 15 lung cancer tumor biomarkers.2.The same way on three different types of pathological analysis of patients with lung cancer group. The results of three groups were clearly distinct. And 4 cases of unknown pathological type of lung cancer patients had carried out a sub-type.Conclusions:1. It is distinguished between lung cancer patients and healthy people use established metabolomics methods.we found 15 potential tumor biomarkers .These tumor markers joint of other means of lung cancer examination.It is contributed to areas such as screening, early detection, prognosis and targeted therapy for lung cancer.2. Metabolomics established methods was applied to squamous cell carcinoma, adenocarcinoma, small cell carcinoma of the three different pathological types of lung cancer patients. the three different pathological types of lung cancer patients were fully distinguish. Clinically, this method can be used a preliminary classification for unknown pathological type of lung cancer patients. It was provided a theoretical basis for choosing of treatment options with non-pathological diagnosis of lung cancer patients in the future.