Background:The Genotype-Tissue Expression was used to expanded normal tissue of the Cancer Genome Atlas database.This study aimed to investigate genes associated with the pathogenesis and prognosis of prostate cancer....Background:The Genotype-Tissue Expression was used to expanded normal tissue of the Cancer Genome Atlas database.This study aimed to investigate genes associated with the pathogenesis and prognosis of prostate cancer.Methods:We conducted prognostic related genes for prostate cancer by using transcriptome data from the Genotype-Tissue Expression Project and the Cancer Genome Atlas data sources,which were analyzed using an integrated bioinformatics strategy.Clinically significant modules were distinguished,and GO and KEGG analysis were used to Database for Annotation,Visualization and Integrated Discovery.Further annotation was performed through Gene set enrichment analysis.Logistic regression was carried out to analyze the associations between clinicopathologic characteristics and the hub genes.Logistic regression model and survival analysis were performed.Results:By using data available from the Cancer Genome Atlas and the Genotype-Tissue Expression databases,we here show that 53 differential expression genes were identified.Through GO and KEGG analysis a prognostic related gene signature consisted of GOLM1,EIF4A1,ABCC4,RPL7P16,NPIPB12 and PCA3 was constructed with a good performance in predicting overall survivals.The majority of the six hub genes were associated with clinical characteristics of prostate cancer.Conclusion:These genes might be considered as new targets for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy since they showed differently expressed in prostate cancer and correlate with overall survival prognosis.展开更多
Background:The Genotype-Tissue Expression(GTEx)Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased indiv iduals,providing an opportunity to study the regulat...Background:The Genotype-Tissue Expression(GTEx)Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased indiv iduals,providing an opportunity to study the regulatory roles of genetic variants in transcriptome activities from both cross-tissue and tissue-specific perspectives.Moreover,transeriptome activities(e.g.,transcript abundance and alternative splicing)can be treated as mediators between genotype and phenotype to achieve phenotypic alteration.Knowing the genotype associated transcriptome status,researchers can better understand the biological and molecular mechanisms of genetic risk variants in complex traits.Results:In this article,we first explore the genetic architecture of gene expression traits,and then review recent methods on quantitative trait locus(QTL)and co-expression network analysis.To further exemplify the usage of associations between genotype and transcriptome status,we briefly review methods that either directly or indirectly integrate expression/splicing QTL information in genome wide association studies(GWASs).Conclusions:The GTEx Project provides the largest and useful resouree to investigate the associations between genotype and transcriptome status.The integration of results from the GTEx Project and existing GWASs further advances our understanding of roles of gene expression changes in bridging both the genetic variants and complex traits.展开更多
基金grants from the National Natural Science Foundation of China(No.81603438 and 81802568).
文摘Background:The Genotype-Tissue Expression was used to expanded normal tissue of the Cancer Genome Atlas database.This study aimed to investigate genes associated with the pathogenesis and prognosis of prostate cancer.Methods:We conducted prognostic related genes for prostate cancer by using transcriptome data from the Genotype-Tissue Expression Project and the Cancer Genome Atlas data sources,which were analyzed using an integrated bioinformatics strategy.Clinically significant modules were distinguished,and GO and KEGG analysis were used to Database for Annotation,Visualization and Integrated Discovery.Further annotation was performed through Gene set enrichment analysis.Logistic regression was carried out to analyze the associations between clinicopathologic characteristics and the hub genes.Logistic regression model and survival analysis were performed.Results:By using data available from the Cancer Genome Atlas and the Genotype-Tissue Expression databases,we here show that 53 differential expression genes were identified.Through GO and KEGG analysis a prognostic related gene signature consisted of GOLM1,EIF4A1,ABCC4,RPL7P16,NPIPB12 and PCA3 was constructed with a good performance in predicting overall survivals.The majority of the six hub genes were associated with clinical characteristics of prostate cancer.Conclusion:These genes might be considered as new targets for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy since they showed differently expressed in prostate cancer and correlate with overall survival prognosis.
基金This work was supported by the National Institutes of Health(NIH)grants R01 GM134005,and the National Science Foundation(NSF)grants DMS 1902903.Dr.Sheng Chih Jin's effort was supported by the Pathway to Independence Award(K99/R00)program,grants K99HL143036-01A1 and R00HL143036-02.
文摘Background:The Genotype-Tissue Expression(GTEx)Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased indiv iduals,providing an opportunity to study the regulatory roles of genetic variants in transcriptome activities from both cross-tissue and tissue-specific perspectives.Moreover,transeriptome activities(e.g.,transcript abundance and alternative splicing)can be treated as mediators between genotype and phenotype to achieve phenotypic alteration.Knowing the genotype associated transcriptome status,researchers can better understand the biological and molecular mechanisms of genetic risk variants in complex traits.Results:In this article,we first explore the genetic architecture of gene expression traits,and then review recent methods on quantitative trait locus(QTL)and co-expression network analysis.To further exemplify the usage of associations between genotype and transcriptome status,we briefly review methods that either directly or indirectly integrate expression/splicing QTL information in genome wide association studies(GWASs).Conclusions:The GTEx Project provides the largest and useful resouree to investigate the associations between genotype and transcriptome status.The integration of results from the GTEx Project and existing GWASs further advances our understanding of roles of gene expression changes in bridging both the genetic variants and complex traits.
