Research Of Clinical Characteristics And Pulmonic Microbiological Metaproteomics Of Lung Cancer

Objective: To provide a new method for distinguishing lung cancer(LC)from chronic benign lesions(CBL)via analyzing pulmonic microbiome characteristics of lung cancer patients using non-standard quantitative metaproteomics based on the clinical characteristics of lung cancer patients..Method: One hundred and fifteen cases of LC patients as well as one hundred and six patients with CBL were enrolled,and their clinical data were recorded,followed by the statistical analysis.Bronchoalveolar lavage fluid(BALF)of 46 cases were selected for further detection,After BALF protein was extracted and performed the quality control,18 cases(11 patients with LC,7 cases of CBL)of BALF were finally qualified for metaproteomics.All of the identified proteins were analyzed via bioinformatic methods,and the proteins with different expression level between two groups were conducted the functional classification,the functional enrichment and clustering analysis;The identified peptides species-composition analyzed via Unipept.Result: There were statistical differences as for the smoking history,nutritional risk score,anxiety score and history of chronic lung disease between the two groups(P< 0.05).Such indices in LC group were statistically higher than those in CBL group as squamous cell carcinoma antigen(Scc),pro-gastrin-releasing peptide,(Pro GRP),cytokeratin-19-fragment(Cyf-211),neuron-specific enolase(NSE),carcino-embryonic antigen(CEA),carbohydrate antigen 125(CA125),and the serum tumor markers of carbohydrate antigen 153(CA153)(P< 0.05).A logistic prediction model was established based on the results of multi-factor analysis of clinical data,which CA153,NSE,smoking history and history of chronic lung disease were the independent risk factors for LC.Metaproteomics in the BALF from 18 patients were performed,and 5332 proteins were identified,within which 4343 proteins contained quantitative information,and 132 proteins were upregulated and 104 proteins were downregulated in LC group when compared with those in CBL group.Quantitative proteins were derived from 13 phyla and 66 genera.Conclusion: The prediction model of LC established in this study has ideal diagnostic specificity and positive-predictive value.Compared with CBL patients,the pulmonary metaproteomics of LC patients showed characteristic changes,and the differential proteins that might be related to the incidence of LC were found,which could provide a new method and new idea for identify LC from CBL for the patients with high risk after evaluated by our prediction model.