AIM:Optimal molecular markers for detecting colorectal cancer(CRC)in a blood-based assay were evaluated.METHODS:A matched(by variables of age and sex)case-control design(111 CRC and 227 non-cancer samples)was applied....AIM:Optimal molecular markers for detecting colorectal cancer(CRC)in a blood-based assay were evaluated.METHODS:A matched(by variables of age and sex)case-control design(111 CRC and 227 non-cancer samples)was applied.Total RNAs isolated from the338 blood samples were reverse-transcribed,and the relative transcript levels of candidate genes were analyzed.The training set was made of 162 random samples of the total 338 samples.A logistic regression analysis was performed,and odds ratios for each gene were determined between CRC and non-cancer.The samples(n=176)in the testing set were used to validate the logistic model,and an inferred performance(generality)was verified.By pooling 12 public microarray datasets(GSE 4107,4183,8671,9348,10961,13067,13294,13471,14333,15960,17538,and 18105),which included 519 cases of adenocarcinoma and 88 controls of normal mucosa,we were able to verify the selected genes from logistic models and estimate their external generality.RESULTS:The logistic regression analysis resulted in the selection of five significant genes(P<0.05;MDM2,DUSP6,CPEB4,MMD,and EIF2S3),with odds ratios of 2.978,6.029,3.776,0.538 and 0.138,respectively.The five-gene model performed stably for the discrimination of CRC cases from controls in the training set,with accuracies ranging from 73.9%to 87.0%,a sensitivity of 95%and a specificity of 95%.In addition,a good performance in the test set was obtained using the discrimination model,providing 83.5%ac-curacy,66.0%sensitivity,92.0%specificity,a positive predictive value of 89.2%and a negative predictive value of 73.0%.Multivariate logistic regressions analyzed 12 pooled public microarray data sets as an external validation.Models that provided similar expected and observed event rates in subgroups were termed well calibrated.A model in which MDM2,DUSP6,CPEB4,MMD,and EIF2S3 were selected showed the result in logistic regression analysis(H-L P=0.460,R2=0.853,AUC=0.978,accuracy=0.949,specificity=0.818 and sensitivity=0.971).CONCLUSION:A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.展开更多
Antibiotic resistance genes(ARGs)have been considered as emerging contaminants in nature owing to their wide distribution and human health risk.Anthropogenic activities can increase the diversity and abundance of ARGs...Antibiotic resistance genes(ARGs)have been considered as emerging contaminants in nature owing to their wide distribution and human health risk.Anthropogenic activities can increase the diversity and abundance of ARGs and promote their spread in environment.Offshore environment is affected by multiple types of anthropogenic activities,of which excessive accumulation of petroleum substances poses a serious threat.Our previous experimental study has demonstrated that petroleum can increase the abundance of sulfonamide resistance genes(SRGs)in the seawater through horizontal gene transfer.However,the influence of petroleum substances on SRGs in offshore environment,especially adjacent the petroleum exploitation platform,is still unclear.Therefore,the effect of offshore oil exploitation on SRGs was investigated in the surface sediments collected from the Liaodong Bay,north China.The genes of sul1 and sul2 were present in all of the collected samples,while the sul3 gene was not detected in any sediments.The absolute abundance of sul2 gene in each sample was higher than sul1 gene.Class 1 integrons enhanced the maintenance and propagation of sul1 gene but not sul2 gene.More importantly,the results indicate that the absolute abundance of sul2 gene present in the offshore sediments that affected by petroleum exploitation was significantly higher than those in control.These findings provided direct evidence that offshore oil exploitation can influence the propagation of SRGs and implied that a more comprehensive risk assessment of petroleum substances to public health risks should be conducted.展开更多
基金Supported by Taiwan’s SBIR promoting program from the De-partment of Industrial Technology of the Ministry of Economic Affairs,Advpharma,Incthe National Defense Medical Cen-ter(NDMC),Bureau of Military Medicine,Ministry of Defense,Taiwan
文摘AIM:Optimal molecular markers for detecting colorectal cancer(CRC)in a blood-based assay were evaluated.METHODS:A matched(by variables of age and sex)case-control design(111 CRC and 227 non-cancer samples)was applied.Total RNAs isolated from the338 blood samples were reverse-transcribed,and the relative transcript levels of candidate genes were analyzed.The training set was made of 162 random samples of the total 338 samples.A logistic regression analysis was performed,and odds ratios for each gene were determined between CRC and non-cancer.The samples(n=176)in the testing set were used to validate the logistic model,and an inferred performance(generality)was verified.By pooling 12 public microarray datasets(GSE 4107,4183,8671,9348,10961,13067,13294,13471,14333,15960,17538,and 18105),which included 519 cases of adenocarcinoma and 88 controls of normal mucosa,we were able to verify the selected genes from logistic models and estimate their external generality.RESULTS:The logistic regression analysis resulted in the selection of five significant genes(P<0.05;MDM2,DUSP6,CPEB4,MMD,and EIF2S3),with odds ratios of 2.978,6.029,3.776,0.538 and 0.138,respectively.The five-gene model performed stably for the discrimination of CRC cases from controls in the training set,with accuracies ranging from 73.9%to 87.0%,a sensitivity of 95%and a specificity of 95%.In addition,a good performance in the test set was obtained using the discrimination model,providing 83.5%ac-curacy,66.0%sensitivity,92.0%specificity,a positive predictive value of 89.2%and a negative predictive value of 73.0%.Multivariate logistic regressions analyzed 12 pooled public microarray data sets as an external validation.Models that provided similar expected and observed event rates in subgroups were termed well calibrated.A model in which MDM2,DUSP6,CPEB4,MMD,and EIF2S3 were selected showed the result in logistic regression analysis(H-L P=0.460,R2=0.853,AUC=0.978,accuracy=0.949,specificity=0.818 and sensitivity=0.971).CONCLUSION:A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.
文摘Antibiotic resistance genes(ARGs)have been considered as emerging contaminants in nature owing to their wide distribution and human health risk.Anthropogenic activities can increase the diversity and abundance of ARGs and promote their spread in environment.Offshore environment is affected by multiple types of anthropogenic activities,of which excessive accumulation of petroleum substances poses a serious threat.Our previous experimental study has demonstrated that petroleum can increase the abundance of sulfonamide resistance genes(SRGs)in the seawater through horizontal gene transfer.However,the influence of petroleum substances on SRGs in offshore environment,especially adjacent the petroleum exploitation platform,is still unclear.Therefore,the effect of offshore oil exploitation on SRGs was investigated in the surface sediments collected from the Liaodong Bay,north China.The genes of sul1 and sul2 were present in all of the collected samples,while the sul3 gene was not detected in any sediments.The absolute abundance of sul2 gene in each sample was higher than sul1 gene.Class 1 integrons enhanced the maintenance and propagation of sul1 gene but not sul2 gene.More importantly,the results indicate that the absolute abundance of sul2 gene present in the offshore sediments that affected by petroleum exploitation was significantly higher than those in control.These findings provided direct evidence that offshore oil exploitation can influence the propagation of SRGs and implied that a more comprehensive risk assessment of petroleum substances to public health risks should be conducted.
