Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet...Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet these requirements,we present here an updated version of our popular Bioconductor package,clusterProfiler 4.0.This package has been enhanced considerably compared with its original version published 9 years ago.The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases.It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization.Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists.We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.展开更多
Objective:To identify potential drug targets for metastasis colorectal cancer(CRC)patients with low mutational burden by examining differences in immune-related gene expression.Methods:For this study,623 samples were ...Objective:To identify potential drug targets for metastasis colorectal cancer(CRC)patients with low mutational burden by examining differences in immune-related gene expression.Methods:For this study,623 samples were collected from The Cancer Genome Atlas(TCGA)database,comprising tumor mutational burden(TMB),RNA sequencing(RNA-Seq),and clinical data.Differential gene expression analysis,Gene Ontology(GO),and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis of the identified genes were conducted using the R package.Additionally,a comparative analysis of immune infiltrating cell composition in metastatic and non-metastatic groups was performed.Hub genes,exhibiting high levels of interaction,were selected using the Search Tool for the Retrieval of Interacting Genes/Proteins(STRING)database.The Drug Gene Interaction Database(DGIdb)was then utilized to estimate drugs targeting the identified hub genes.Results:The transcriptome data of 326 colorectal cancer patients with low TMB were analyzed,comprising 58 patients with metastasis and 268 patients without metastasis.Among the differential expression in 1,111 genes for patients with metastasis compared to those without metastasis,733 genes were upregulated,and 378 genes were downregulated.KEGG and GO enrichment analysis indicated significant differences in gene expression in CRC metastatic patients with low TMB compared to non-metastasis patients with low TMB.Enriched pathways included humoral immune response,immunoglobulin production,and regulation of AMPA receptor activity.Two genes related to interleukin-12 were identified through secondary enrichment for immune-related genes.Analysis of tumor-infiltrating immune cell data revealed significant differences in memory-activated T cell CD4 and T cell CD8.Conclusions:This analysis of RNA sequencing data and immune-filtrating cell data revealed significant differences between metastatic colorectal cancer patients with low TMB and their non-metastatic counterparts.These distinctions suggest the possibility o展开更多
BACKGROUND Multiple myeloma(MM)is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow.Th...BACKGROUND Multiple myeloma(MM)is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow.The translocation,(t)(4;14),results in high-risk MM with limited treatment alternatives.Thus,there is an urgent need for identification and validation of potential treatments for this MM subtype.Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.AIM To elucidate the molecular basis and search for potential effective drugs of t(4;14)MM subtype by employing a comprehensive approach.METHODS The transcriptional signature of t(4;14)MM was sourced from the Gene Expression Omnibus.Two datasets,GSE16558 and GSE116294,which included 17 and 15 t(4;14)MM bone marrow samples,and five and four normal bone marrow samples,respectively.After the differentially expressed genes were identified,the Cytohubba tool was used to screen for hub genes.Then,the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis.Using the STRING database and Cytoscape,protein–protein interaction networks and core targets were identified.Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis,respectively.RESULTS In this study,a total of 258 differentially expressed genes with enriched functions in cancer pathways,namely cytokine receptor interactions,nuclear factor(NF)-κB signaling pathway,lipid metabolism,atherosclerosis,and Hippo signaling pathway,were identified.Ten hub genes(cd45,vcam1,ccl3,cd56,app,cd48,btk,ccr2,cybb,and cxcl12)were identified.Nine drugs,including ivermectin,deforolimus,and isoliquiritigenin,were predicted by the Connectivity Map database to have potential therapeutic effects on t(4;14)MM.In molecular docking,ivermectin showed strong binding affinity to all 10 identified targets,especially cd45 and cybb.Iver展开更多
Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest.However,it may produce a long list of significant...Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest.However,it may produce a long list of significant terms with highly redundant information that is difficult to summarize.Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.We propose a new method named binary cut for clustering similarity matrices of functional terms.Through comprehensive benchmarks on both simulated and real-world datasets,we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups.We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut,while similarity matrices based on gene overlap showed less consistent patterns.We implemented the binary cut algorithm in the R package simplifyEnrichment,which additionally provides functionalities for visualizing,summarizing,and comparing the clustering.The simplifyEnrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.展开更多
[目的]运用网络药理学方法分析白术-白芍药对治疗肝纤维化的有效活性成分,预测其作用靶点,并分析其可能的作用机制,为肝脾同治理论提供网络药理学依据。[方法]利用中药系统药理学数据库与分析平台(Traditional Chinese Medicine Systems...[目的]运用网络药理学方法分析白术-白芍药对治疗肝纤维化的有效活性成分,预测其作用靶点,并分析其可能的作用机制,为肝脾同治理论提供网络药理学依据。[方法]利用中药系统药理学数据库与分析平台(Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,TCMSP)筛选白术-白芍药对的活性成分及其对应靶点,结合人类基因组注释数据库(Human Genome Annotation Database,GeneCards)、人类孟德尔遗传在线(Online Mendelian Inheritance in Man,OMIM)数据库筛选治疗肝纤维化的潜在作用靶点。利用网站Venny 2.1.0绘制韦恩图,然后通过Cytoscape 3.7.2软件和String数据库构建成分-靶点-疾病网络图和靶点间的蛋白互作(protein-protein interaction,PPI)网络,通过Metoscape平台对靶点进行基因本体(gene ontology,GO)富集分析和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路与基因功能富集分析。[结果]经过筛选,共获得白术-白芍的20个有效药物成分,与肝纤维化相关的关键化学成分9个,药对-肝纤维化的共同靶点53个,PPI关键靶点11个,蛋白激酶B1(protein kinase B1,AKT1)、白细胞介素-6(interleukin-6,IL-6)、丝裂原激活蛋白激酶8(mitogen activated protein kinase 8,MAPK8)、肿瘤坏死因子(tumor necrosis factor,TNF)是白术-白芍作用的关键靶点。GO富集分析结果包括生物过程(biological process,BP)条目20条、细胞组分(celluar component,CC)11条、分子功能(molecular function,MF)18条;KEGG富集分析发现相关信号通路18条,涉及糖尿病并发症中的晚期糖基化终末产物-糖基化终末产物受体(advanced glycosylation end product-receptor of advanced glycosylation end product,AGE-RAGE)信号通路、癌症信号通路、流体剪切应力和动脉粥样硬化通路、核因子-κB(nuclear factor-κB,NF-κB)信号通路、铂剂耐药通路、大肠癌通路、小细胞肺癌通路、肝细胞癌通路等。[结论]白展开更多
旨在检测永登七山羊群体基因组选择信号,挖掘永登七山羊有价值的种质特性基因。本研究以4个绵羊群体(永登七山羊、岷县黑裘皮羊、兰州大尾羊和滩羊)共40个个体为研究对象,利用简化基因组测序(specific-locus amplified fragment sequenc...旨在检测永登七山羊群体基因组选择信号,挖掘永登七山羊有价值的种质特性基因。本研究以4个绵羊群体(永登七山羊、岷县黑裘皮羊、兰州大尾羊和滩羊)共40个个体为研究对象,利用简化基因组测序(specific-locus amplified fragment sequencing,SLAF-seq)技术检测全基因组范围内的单核苷酸多态性位点(SNPs)。基于SNPs数据集,通过elgensoft软件进行主成分分析;运用Treemix软件分析基因流事件;利用群体遗传分化指数(Fst)和核苷酸多样性比值(πratio)进行全基因组选择性清除分析,取top 5%Fst和πratio的交集以确定基因组受选择区域,并对候选基因进行GO和KEGG富集分析。结果共得到1658596个群体SNPs;主成分分析(PCA)发现永登七山羊能够独立分群,基因流表明永登七山羊和兰州大尾羊存在较弱的基因交流。以永登七山羊为试验群体,岷县黑裘皮羊、兰州大尾羊和滩羊为参考群体进行选择清除分析,3个比较组的受选择区域分别检测出424、294、301个候选基因;GO和KEGG分析结果表明,候选基因分别显著富集在65、79、41个GO条目及15、22、10条KEGG通路上(P<0.05)。此外,将岷县黑裘皮羊、兰州大尾羊和滩羊3个群体的数据合并为一个数据集与永登七山羊进行比较,共筛选到466个候选基因,显著富集到124个GO条目及7条KEGG通路(P<0.05)。从中筛选到永登七山羊重要经济性状相关的功能基因BMP2、GRM 1和ALDH 1A1。研究结果表明,在永登七山羊全基因组范围内进行选择信号检测,鉴定到与生长发育、脂尾进化相关的候选基因,为永登七山羊的分子遗传标记挖掘提供参考。展开更多
基金This work was supported by a startup fund from Southern Medical University.
文摘Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet these requirements,we present here an updated version of our popular Bioconductor package,clusterProfiler 4.0.This package has been enhanced considerably compared with its original version published 9 years ago.The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases.It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization.Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists.We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
文摘Objective:To identify potential drug targets for metastasis colorectal cancer(CRC)patients with low mutational burden by examining differences in immune-related gene expression.Methods:For this study,623 samples were collected from The Cancer Genome Atlas(TCGA)database,comprising tumor mutational burden(TMB),RNA sequencing(RNA-Seq),and clinical data.Differential gene expression analysis,Gene Ontology(GO),and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis of the identified genes were conducted using the R package.Additionally,a comparative analysis of immune infiltrating cell composition in metastatic and non-metastatic groups was performed.Hub genes,exhibiting high levels of interaction,were selected using the Search Tool for the Retrieval of Interacting Genes/Proteins(STRING)database.The Drug Gene Interaction Database(DGIdb)was then utilized to estimate drugs targeting the identified hub genes.Results:The transcriptome data of 326 colorectal cancer patients with low TMB were analyzed,comprising 58 patients with metastasis and 268 patients without metastasis.Among the differential expression in 1,111 genes for patients with metastasis compared to those without metastasis,733 genes were upregulated,and 378 genes were downregulated.KEGG and GO enrichment analysis indicated significant differences in gene expression in CRC metastatic patients with low TMB compared to non-metastasis patients with low TMB.Enriched pathways included humoral immune response,immunoglobulin production,and regulation of AMPA receptor activity.Two genes related to interleukin-12 were identified through secondary enrichment for immune-related genes.Analysis of tumor-infiltrating immune cell data revealed significant differences in memory-activated T cell CD4 and T cell CD8.Conclusions:This analysis of RNA sequencing data and immune-filtrating cell data revealed significant differences between metastatic colorectal cancer patients with low TMB and their non-metastatic counterparts.These distinctions suggest the possibility o
基金National Key Research and Development Program of China,No.2021YFC2701704the National Clinical Medical Research Center for Geriatric Diseases,"Multicenter RCT"Research Project,No.NCRCG-PLAGH-20230010the Military Logistics Independent Research Project,No.2022HQZZ06.
文摘BACKGROUND Multiple myeloma(MM)is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow.The translocation,(t)(4;14),results in high-risk MM with limited treatment alternatives.Thus,there is an urgent need for identification and validation of potential treatments for this MM subtype.Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.AIM To elucidate the molecular basis and search for potential effective drugs of t(4;14)MM subtype by employing a comprehensive approach.METHODS The transcriptional signature of t(4;14)MM was sourced from the Gene Expression Omnibus.Two datasets,GSE16558 and GSE116294,which included 17 and 15 t(4;14)MM bone marrow samples,and five and four normal bone marrow samples,respectively.After the differentially expressed genes were identified,the Cytohubba tool was used to screen for hub genes.Then,the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis.Using the STRING database and Cytoscape,protein–protein interaction networks and core targets were identified.Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis,respectively.RESULTS In this study,a total of 258 differentially expressed genes with enriched functions in cancer pathways,namely cytokine receptor interactions,nuclear factor(NF)-κB signaling pathway,lipid metabolism,atherosclerosis,and Hippo signaling pathway,were identified.Ten hub genes(cd45,vcam1,ccl3,cd56,app,cd48,btk,ccr2,cybb,and cxcl12)were identified.Nine drugs,including ivermectin,deforolimus,and isoliquiritigenin,were predicted by the Connectivity Map database to have potential therapeutic effects on t(4;14)MM.In molecular docking,ivermectin showed strong binding affinity to all 10 identified targets,especially cd45 and cybb.Iver
基金This work was supported by the National Center for Tumor Diseases(NCT)Molecular Precision Oncology Program and the NCT Donations against Cancer Program,Germany.
文摘Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest.However,it may produce a long list of significant terms with highly redundant information that is difficult to summarize.Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.We propose a new method named binary cut for clustering similarity matrices of functional terms.Through comprehensive benchmarks on both simulated and real-world datasets,we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups.We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut,while similarity matrices based on gene overlap showed less consistent patterns.We implemented the binary cut algorithm in the R package simplifyEnrichment,which additionally provides functionalities for visualizing,summarizing,and comparing the clustering.The simplifyEnrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.
文摘[目的]运用网络药理学方法分析白术-白芍药对治疗肝纤维化的有效活性成分,预测其作用靶点,并分析其可能的作用机制,为肝脾同治理论提供网络药理学依据。[方法]利用中药系统药理学数据库与分析平台(Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,TCMSP)筛选白术-白芍药对的活性成分及其对应靶点,结合人类基因组注释数据库(Human Genome Annotation Database,GeneCards)、人类孟德尔遗传在线(Online Mendelian Inheritance in Man,OMIM)数据库筛选治疗肝纤维化的潜在作用靶点。利用网站Venny 2.1.0绘制韦恩图,然后通过Cytoscape 3.7.2软件和String数据库构建成分-靶点-疾病网络图和靶点间的蛋白互作(protein-protein interaction,PPI)网络,通过Metoscape平台对靶点进行基因本体(gene ontology,GO)富集分析和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路与基因功能富集分析。[结果]经过筛选,共获得白术-白芍的20个有效药物成分,与肝纤维化相关的关键化学成分9个,药对-肝纤维化的共同靶点53个,PPI关键靶点11个,蛋白激酶B1(protein kinase B1,AKT1)、白细胞介素-6(interleukin-6,IL-6)、丝裂原激活蛋白激酶8(mitogen activated protein kinase 8,MAPK8)、肿瘤坏死因子(tumor necrosis factor,TNF)是白术-白芍作用的关键靶点。GO富集分析结果包括生物过程(biological process,BP)条目20条、细胞组分(celluar component,CC)11条、分子功能(molecular function,MF)18条;KEGG富集分析发现相关信号通路18条,涉及糖尿病并发症中的晚期糖基化终末产物-糖基化终末产物受体(advanced glycosylation end product-receptor of advanced glycosylation end product,AGE-RAGE)信号通路、癌症信号通路、流体剪切应力和动脉粥样硬化通路、核因子-κB(nuclear factor-κB,NF-κB)信号通路、铂剂耐药通路、大肠癌通路、小细胞肺癌通路、肝细胞癌通路等。[结论]白
