Identification of co-expressed genes associated with MLL rearrangement in pediatric acute lymphoblastic leukemia
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Abstract Rearrangements involving the mixed lineage leukemia (MLL) gene are common adverse prognostic factors of pediatric acute lymphoblastic leukemia (ALL). Even allogeneic hematopoietic stem cell transplantation does not improve the outcome of ALL cases with some types of MLL rearrangements. The aim of the present study was to identify the co-expressed genes that related to MLL rearrangement (MLL-r) and elucidate the potential mechanisms of how MLL-r and their partner genes lead to leukemogenesis. Gene co-expression networks were constructed using the gene expression data and sample traits of 204 pretreated pediatric ALL patients, and co-expression modules significantly related to the MLL-r were screened out. Gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway analysis of the module genes were performed. Hub genes were identified and their expression levels were analyzed in samples with or without MLL-r and the results were validated by an independent investigation. Furthermore, the relationships between the hub genes and sample traits were analyzed. In total, 21 co-expression modules were identified. The green module was positively correlated with MLL-r. PROM1, LGALS1, CD44, FUT4 and HOXA10 were identified as hub genes, which were involved in focal adhesion, calcium-dependent phospholipid binding, connective tissue development and transcriptional misregulation in cancer. The expression levels of the five hub genes were significantly increased in MLL-r samples, and the results were further validated. PROM1, LGALS1, CD44 and HOXA10 were positively related to the leukocyte count. These findings might provide novel insight regarding the mechanisms and potential therapeutic targets for pediatric ALL with MLL-r.Keywords:
KEGG
KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
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The KEGG Orthology (KO) database is a widely used molecular function reference database which can be used to conduct functional annotation of most microorganisms. At present, there are many KEGG tools based on the KO entries for annotating functional orthologs. However, determining how to efficiently extract and sort the annotation results of KEGG still hinders the subsequent genome analysis. There is a lack of effective measures used to quickly extract and classify the gene sequences and species information of the KEGG annotations. Here, we present a supporting tool: KEGG_Extractor for species-specific genes extraction and classification, which can output the results through an iterative keyword matching algorithm. It can not only extract and classify the amino acid sequences, but also the nucleotide sequences, and it has proved to be fast and efficient for microbial analysis. Analysis of the ancient Wood Ljungdahl (WL) pathway through the KEGG_Extractor reveals that ~226 archaeal strains contained the WL pathway-related genes. Most of them were Methanococcus maripaludis, Methanosarcina mazei and members of the Methanobacterium, Thermococcus and Methanosarcina genus. Using the KEGG_Extractor, the ARWL database was constructed, which had a high accuracy and complement. This tool helps to link genes with the KEGG pathway and promote the reconstruction of molecular networks. Availability and implementation: KEGG_Extractor is freely available from the GitHub.
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The KEGG database and analysis tools (https://www.kegg.jp) have been developed mostly for understanding genes and genomes of cellular organisms. The KO (KEGG Orthology) dataset, which is a collection of functional orthologs, plays the role of linking genes in the genome to pathways and other molecular networks, enabling KEGG mapping to uncover hidden features in the genome. Although viruses were part of KEGG for some time, they were not fully integrated in the KEGG analysis tools, because the KO assignment rate is very low for virus genes. To supplement KOs a new dataset named virus ortholog clusters (VOCs) is computationally generated, covering 90% of viral proteins in KEGG. VOCs can be used, in place of KOs, for taxonomy mapping to uncover relationships of sequence similarity groups and taxonomic groups and for identifying conserved gene orders in virus genomes. Furthermore, selected VOCs are used to define tentative KOs for characterizing protein functions. Here an overview of KEGG tools is presented focusing on these extensions for viral protein analysis.
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Abstract Ours and other previous studies have shown that CYP4Z1 is specifically and highly expressed in breast cancer, and acts as a promoter for the stemness of breast cancer cells. Here, we explored whether targeting CYP4Z1 could attenuate the stemness of breast cancer cells using HET0016, which has been confirmed to be an inhibitor of CYP4Z1 by us and others. Using the transcriptome‐sequencing analysis, we found that HET0016 suppressed the expression of cancer stem cell (CSC) markers and stem cell functions. Additionally, HET0016 indeed reduced the stemness of breast cancer cells, as evident by the decrease of stemness marker expression, CD44 + /CD24 − subpopulation with stemness, mammary‐spheroid formation, and tumor‐initiating ability. Moreover, HET0016 suppressed the metastatic capability through in vitro and in vivo experiments. Furthermore, we confirmed that HET0016 suppressed CYP4Z1 activity, and HET0016‐induced inhibition on the stemness and metastasis of breast cancer cells was rescued by CYP4Z1 overexpression. Thus, our results demonstrate that HET0016 can attenuate the stemness of breast cancer cells through targeting CYP4Z1.
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KEGG ( http://www.genome.jp/kegg/ ) is a database of biological systems that integrates genomic, chemical and systemic functional information. KEGG provides a reference knowledge base for linking genomes to life through the process of PATHWAY mapping, which is to map, for example, a genomic or transcriptomic content of genes to KEGG reference pathways to infer systemic behaviors of the cell or the organism. In addition, KEGG provides a reference knowledge base for linking genomes to the environment, such as for the analysis of drug-target relationships, through the process of BRITE mapping. KEGG BRITE is an ontology database representing functional hierarchies of various biological objects, including molecules, cells, organisms, diseases and drugs, as well as relationships among them. KEGG PATHWAY is now supplemented with a new global map of metabolic pathways, which is essentially a combined map of about 120 existing pathway maps. In addition, smaller pathway modules are defined and stored in KEGG MODULE that also contains other functional units and complexes. The KEGG resource is being expanded to suit the needs for practical applications. KEGG DRUG contains all approved drugs in the US and Japan, and KEGG DISEASE is a new database linking disease genes, pathways, drugs and diagnostic markers.
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Abstract Background The MetaCyc and KEGG projects have developed large metabolic pathway databases that are used for a variety of applications including genome analysis and metabolic engineering. We present a comparison of the compound, reaction, and pathway content of MetaCyc version 16.0 and a KEGG version downloaded on Feb-27-2012 to increase understanding of their relative sizes, their degree of overlap, and their scope. To assess their overlap, we must know the correspondences between compounds, reactions, and pathways in MetaCyc, and those in KEGG. We devoted significant effort to computational and manual matching of these entities, and we evaluated the accuracy of the correspondences. Results KEGG contains 179 module pathways versus 1,846 base pathways in MetaCyc; KEGG contains 237 map pathways versus 296 super pathways in MetaCyc. KEGG pathways contain 3.3 times as many reactions on average as do MetaCyc pathways, and the databases employ different conceptualizations of metabolic pathways. KEGG contains 8,692 reactions versus 10,262 for MetaCyc. 6,174 KEGG reactions are components of KEGG pathways versus 6,348 for MetaCyc. KEGG contains 16,586 compounds versus 11,991 for MetaCyc. 6,912 KEGG compounds act as substrates in KEGG reactions versus 8,891 for MetaCyc. MetaCyc contains a broader set of database attributes than does KEGG, such as relationships from a compound to enzymes that it regulates, identification of spontaneous reactions, and the expected taxonomic range of metabolic pathways. MetaCyc contains many pathways not found in KEGG, from plants, fungi, metazoa, and actinobacteria; KEGG contains pathways not found in MetaCyc, for xenobiotic degradation, glycan metabolism, and metabolism of terpenoids and polyketides. MetaCyc contains fewer unbalanced reactions, which facilitates metabolic modeling such as using flux-balance analysis. MetaCyc includes generic reactions that may be instantiated computationally. Conclusions KEGG contains significantly more compounds than does MetaCyc, whereas MetaCyc contains significantly more reactions and pathways than does KEGG, in particular KEGG modules are quite incomplete. The number of reactions occurring in pathways in the two DBs are quite similar.
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The KEGG-PATH approach, a kind of data mining through functional enrichment analysis of time-course experiments or those involving multiple treatments, can uncover the complex regulation mechanisms of KEGG pathways through the subdivision of total effect.
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KEGG (Kyoto Encyclopedia of Genes and Genomes) is a bioinformatics resource for understanding the functions and utilities of cells and organisms from both high-level and genomic perspectives. It is a self-sufficient, integrated resource consisting of genomic, chemical, and network information, with cross-references to numerous outside databases, containing a complete set of building blocks (genes and molecules) and wiring diagrams (interaction networks) to represent cellular functions. This unit describes protocols for using KEGG, KEGG PATHWAY, KEGG GENES, KEGG SSDB, KEGG EXPRESSION, and KEGG LIGAND. Protocols are also described for how to color maps, compare chemical compounds and glycan chains, analyze Ortholog Clusters, and visualize and analyze microarray data, among other procedures. All of these many protocols enable the user to take advantage of the full breadth of the functionality provided by KEGG.
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