Liver cancer presents divergent clinical behaviors.There remain opportunities for molecular markers to improve liver cancer diagnosis and prognosis,especially since tRNA-derived small RNAs(tsRNA)have rarely been studi...Liver cancer presents divergent clinical behaviors.There remain opportunities for molecular markers to improve liver cancer diagnosis and prognosis,especially since tRNA-derived small RNAs(tsRNA)have rarely been studied.In this study,a random forests(RF)diagnostic model was built based upon tsRNA profiling of paired tumor and adjacent normal samples and validated by independent validation(IV).A LASSO model was used to developed a seven-tsRNA-based risk score signature for liver cancer prognosis.Model performance was evaluated by a receiver operating characteristic curve(ROC curve)and Precision-Recall curve(PR curve).The five-tsRNA-based RF diagnosis model had area under the receiver operating characteristic curve(AUROC)88%and area under the precision–recall curve(AUPR)87%in the discovery cohort and 87%and 86%in IV-AUROC and IV-AUPR,respectively.The seven-tsRNA-based prognostic model predicts the overall survival of liver cancer patients(Hazard Ratio 2.02,95%CI 1.36–3.00,P<0.001),independent of standard clinicopathological prognostic factors.Moreover,the model successfully categorizes patients into high-low risk groups.Diagnostic and prognostic modeling can be reliably utilized in the diagnosis of liver cancer and high-low risk classification of patients based upon tsRNA characterization.展开更多
We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data analysis.GranatumX is inspired by the interactive webtool Granatum.GranatumX enables biologists to access the l...We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data analysis.GranatumX is inspired by the interactive webtool Granatum.GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment.It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines.The architecture of GranatumX allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various platforms.GranatumX can be run on the cloud or private servers and generate reproducible results.It is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench scientists.GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.展开更多
Small non-coding RNAs are potential diagnostic biomarkers for lung cancer. Mitochondria-derived small RNA (mtRNA) is a novel regulatory small non-coding RNA that only recently has been identified and cataloged. Curren...Small non-coding RNAs are potential diagnostic biomarkers for lung cancer. Mitochondria-derived small RNA (mtRNA) is a novel regulatory small non-coding RNA that only recently has been identified and cataloged. Currently, there are no reports of studies of mtRNA in human lung cancer. Currently, normalization methods are unstable, and they often fail to identify differentially expressed small non-coding RNAs (sncRNAs). In order to identify reliable biomarkers for lung cancer screening, we used a ratio-based method using mtRNAs newly discovered in human peripheral blood mononuclear cells. In the discovery cohort (AUC = 0.981) and independent validation cohort (AUC = 0.916) the prediction model of eight mtRNA ratios distinguished lung cancer patients from controls. The prediction model will provide reliable biomarkers that will allow blood-based screening to become more feasible and will help make lung cancer diagnosis more accurate in clinical practice.展开更多
基金This work was also supported by the NIH Grants(No.5P30GM114737,P20GM103466,U54MD007584 and 2U54MD007601)Natural Science Foundation of Hubei Province(No.2019CFB417).
文摘Liver cancer presents divergent clinical behaviors.There remain opportunities for molecular markers to improve liver cancer diagnosis and prognosis,especially since tRNA-derived small RNAs(tsRNA)have rarely been studied.In this study,a random forests(RF)diagnostic model was built based upon tsRNA profiling of paired tumor and adjacent normal samples and validated by independent validation(IV).A LASSO model was used to developed a seven-tsRNA-based risk score signature for liver cancer prognosis.Model performance was evaluated by a receiver operating characteristic curve(ROC curve)and Precision-Recall curve(PR curve).The five-tsRNA-based RF diagnosis model had area under the receiver operating characteristic curve(AUROC)88%and area under the precision–recall curve(AUPR)87%in the discovery cohort and 87%and 86%in IV-AUROC and IV-AUPR,respectively.The seven-tsRNA-based prognostic model predicts the overall survival of liver cancer patients(Hazard Ratio 2.02,95%CI 1.36–3.00,P<0.001),independent of standard clinicopathological prognostic factors.Moreover,the model successfully categorizes patients into high-low risk groups.Diagnostic and prognostic modeling can be reliably utilized in the diagnosis of liver cancer and high-low risk classification of patients based upon tsRNA characterization.
基金This research was supported by grants from the National Institute of Environmental Health Sciences(NIEHS)through funds provided by the trans-NIH Big Data to Knowledge(BD2K)initiative(www.bd2k.nih.govGrant No.K01ES025434)+4 种基金the National Institutes of Health/National Institute of General Medical Sciences(NIH/NIGMSGrant No.P20 COBRE GM103457)the National Library of Medicine(NLMGrant No.R01 LM012373)the National Institute of Child Health and Human Development(NICHD,Grant No.R01 HD084633)awarded to LXG.
文摘We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data analysis.GranatumX is inspired by the interactive webtool Granatum.GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment.It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines.The architecture of GranatumX allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various platforms.GranatumX can be run on the cloud or private servers and generate reproducible results.It is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench scientists.GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.
基金supported by the National Institutes of Health(NIH)grants 1R01CA223490,5P30GM114737,5P20GM103466,5U54MD007601,5P30CA071789,1R01CA230514,U54CA143727 and P20GM139753.
文摘Small non-coding RNAs are potential diagnostic biomarkers for lung cancer. Mitochondria-derived small RNA (mtRNA) is a novel regulatory small non-coding RNA that only recently has been identified and cataloged. Currently, there are no reports of studies of mtRNA in human lung cancer. Currently, normalization methods are unstable, and they often fail to identify differentially expressed small non-coding RNAs (sncRNAs). In order to identify reliable biomarkers for lung cancer screening, we used a ratio-based method using mtRNAs newly discovered in human peripheral blood mononuclear cells. In the discovery cohort (AUC = 0.981) and independent validation cohort (AUC = 0.916) the prediction model of eight mtRNA ratios distinguished lung cancer patients from controls. The prediction model will provide reliable biomarkers that will allow blood-based screening to become more feasible and will help make lung cancer diagnosis more accurate in clinical practice.
