Objective To constructed a predictive model of gastric adenocarcinoma prognosis-related proteins by using TCPA and UCSC Xena databases to obtain sample information.And its application value was tested in order to provide reference for clinical treatment and related mechanism research.Methods Using R project,univariate Cox analysis and LASSO-Cox regression analysis were used to select the proteins related to the prognosis of gastric adenocarcinoma,and the risk scores were calculated.According to the risk scores,the patients were divided into high risk group and low risk group for survival analysis.At the same time,draw the ROC curve of the risk score and the protein heat map to evaluate the predictive ability of these proteins.Then,risk scores were combined with other clinical phenotype data of the patients,in order to establish a prognostic model through multivariate regression analysis,and draw a nomogram.The accuracy of the prognostic model was evaluated by C-index,ROC curve and decision curve.GO enrichment analysis and KEGG enrichment analysis were carried out to explore the proteins involved in biological processes.Results Fifteen differential proteins(CKIT,CHK1,CLAUDIN7,COLLAGENVI,DVL3,EGFR_pY1173,ERALPHA_pS11,NFKBP65_pS536,SYK,ETS1,MYOSINIIA_pS1943,P21,P90RSK,RAPTOR,XBP1)were selected by univariate Cox analysis and LASSO-Cox analysis,for calculating the protein risk scores.Through multivariate regression analysis,five factors including age,gender,M stage,N stage and risk score were selected to construct a prognostic model.The results of survival analysis showed that there were significant differences in overall survival between high and low risk groups(P＜0.0001).The nomogram can be used to predict 3-year and 5-year survival rate of patients,and it is considered to have good predictive value(C-index=0.7257).The ROC curve showed that the model exhibited stable sensitivity and specificity in prediction(AUC=0.79 and 0.88).Conclusion The prognostic model of gastric adenocarcinoma prognosis-related proteins can be used to predict the clinical prognosis of patients,and the research results will help to further guide clinical treatment,which could provide a reference for personalized treatment and management.

Objective To constructed a predictive model of gastric adenocarcinoma prognosis-related proteins by using TCPA and UCSC Xena databases to obtain sample information.And its application value was tested in order to provide reference for clinical treatment and related mechanism research.Methods Using R project,univariate Cox analysis and LASSO-Cox regression analysis were used to select the proteins related to the prognosis of gastric adenocarcinoma,and the risk scores were calculated.According to the risk scores,the patients were divided into high risk group and low risk group for survival analysis.At the same time,draw the ROC curve of the risk score and the protein heat map to evaluate the predictive ability of these proteins.Then,risk scores were combined with other clinical phenotype data of the patients,in order to establish a prognostic model through multivariate regression analysis,and draw a nomogram.The accuracy of the prognostic model was evaluated by C-index,ROC curve and decision curve.GO enrichment analysis and KEGG enrichment analysis were carried out to explore the proteins involved in biological processes.Results Fifteen differential proteins(CKIT,CHK1,CLAUDIN7,COLLAGENVI,DVL3,EGFR_pY1173,ERALPHA_pS11,NFKBP65_pS536,SYK,ETS1,MYOSINIIA_pS1943,P21,P90RSK,RAPTOR,XBP1)were selected by univariate Cox analysis and LASSO-Cox analysis,for calculating the protein risk scores.Through multivariate regression analysis,five factors including age,gender,M stage,N stage and risk score were selected to construct a prognostic model.The results of survival analysis showed that there were significant differences in overall survival between high and low risk groups(P＜0.0001).The nomogram can be used to predict 3-year and 5-year survival rate of patients,and it is considered to have good predictive value(C-index=0.7257).The ROC curve showed that the model exhibited stable sensitivity and specificity in prediction(AUC=0.79 and 0.88).Conclusion The prognostic model of gastric adenocarcinoma prognosis-related proteins can be used to predict the clinical prognosis of patients,and the research results will help to further guide clinical treatment,which could provide a reference for personalized treatment and management.

Objective: To establish a real-time quantitative PCR detection system for Torque teno virus (TTV) and verify the sensitivity and specificity of the detection system. Methods: Primers and FAM-Eclipse probes were designed based on the TTV6 gene sequence registered in GenBank, and were to establish a real-time fluorescent quantitative PCR detecting way based on the FAM-Eclipse probe, the standard curve was constructed and sensitivity and specificity were analyzed. Results: A quantitative PCR method for the specific detection of TTV6 were established that the standard curve equation was y=-3.0921x + 28.36, and the amplification efficiency and R2 were 99.6% and 1.000, respectively. The sensitivity of TTV6 was 1.0×10 copies/μl, and there was no cross-reactivity with other viruses. There was 1 case positive for TTV6 out of 56 throat swab samples from the patients with clinical respiratory infection. Conclusions The real-time fluorescent quantitative PCR for detecting TTV6 established by FAM-Eclipse probe had the advantages of high sensitivity and specificity. It provides an effective way for detection and quantification of viral content of TTV6 in clinical specimens.