Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures
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{"title"=>"Inferring the perturbed microRNA regulatory networks in cancer using hierarchical gene co-expression signatures", "type"=>"journal", "authors"=>[{"first_name"=>"Jin", "last_name"=>"Gu", "scopus_author_id"=>"56405709400"}, {"first_name"=>"Zhenyu", "last_name"=>"Xuan", "scopus_author_id"=>"7005595028"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84894271849", "issn"=>"19326203", "pmid"=>"24278370", "doi"=>"10.1371/journal.pone.0081032", "pui"=>"372425450", "sgr"=>"84894271849"}, "id"=>"912bcae5-1e07-36b9-adbd-c4d8838e2e92", "abstract"=>"MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks. However, due to the complexity of cancer transcriptome, current methods often encounter low sensitivity and report few onco-miR candidates. Here, we developed a new method, named miRHiC (enrichment analysis of miRNA targets in Hierarchical gene Co-expression signatures), to infer the perturbed miRNA regulatory networks by using the hierarchical co-expression signatures in large-scale cancer gene expression datasets. The method can infer onco-miR candidates and their target networks which are only linked to sub-clusters of the differentially expressed genes at fine scales of the co-expression hierarchy. On two real datasets of lung cancer and hepatocellular cancer, miRHiC uncovered several known onco-miRs and their target genes (such as miR-26, miR-29, miR-124, miR-125 and miR-200) and also identified many new candidates (such as miR-149, which is inferred in both types of cancers). Using hierarchical gene co-expression signatures, miRHiC can greatly increase the sensitivity for inferring the perturbed miRNA regulatory networks in cancer. All Perl scripts of miRHiC and the detailed documents are freely available on the web at http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/.", "link"=>"http://www.mendeley.com/research/inferring-perturbed-microrna-regulatory-networks-cancer-using-hierarchical-gene-coexpression-signatu", "reader_count"=>16, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>7, "Student > Ph. D. Student"=>3, "Student > Master"=>1, "Professor"=>3, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>7, "Student > Ph. D. Student"=>3, "Student > Master"=>1, "Professor"=>3, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>3, "Medicine and Dentistry"=>3, "Agricultural and Biological Sciences"=>7, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"Japan"=>1, "Egypt"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1289497"], "description"=>"<p>In the first step, the differentially expressed genes were clustered as hierarchical gene co-expression signatures; then, the most significant enrichment of the miRNA target gene set was found across the hierarchical signatures; and finally, a permutation test was used to estimate the empirical p-value of the enrichment.</p>", "links"=>[], "tags"=>["flowchart"], "article_id"=>857852, "categories"=>["Biological Sciences"], "users"=>["Jin Gu", "Zhenyu Xuan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0081032.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_flowchart_of_miRHiC_/857852", "title"=>"The flowchart of miRHiC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-20 03:03:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1289499"], "description"=>"<p>The histogram of the empirical p-values of the control miRNA target gene sets.</p>", "links"=>[], "tags"=>["histogram", "empirical", "p-values", "mirna"], "article_id"=>857854, "categories"=>["Biological Sciences"], "users"=>["Jin Gu", "Zhenyu Xuan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0081032.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_histogram_of_the_empirical_p_values_of_the_control_miRNA_target_gene_sets_/857854", "title"=>"The histogram of the empirical p-values of the control miRNA target gene sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-20 03:03:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1289502"], "description"=>"<p>A) is for lung cancer and B) for hepatocellular cancer. The circle nodes represent the gene co-expression signatures (ClusterID:Size). The diamond nodes represent the inferred onco-miRs. The numbers on the edges represent the sizes of the miRNA target genes overlapped with the corresponding gene co-expression signatures.</p>", "links"=>[], "tags"=>["perturbed", "mirna", "networks", "types", "cancers", "inferred"], "article_id"=>857857, "categories"=>["Biological Sciences"], "users"=>["Jin Gu", "Zhenyu Xuan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0081032.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_perturbed_miRNA_regulatory_networks_in_the_two_types_of_cancers_inferred_by_miRHiC_/857857", "title"=>"The perturbed miRNA regulatory networks in the two types of cancers inferred by miRHiC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-20 03:03:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1289504"], "description"=>"<p>The average log-transformed fold changes of the shared target genes are also shown in the below table.</p>", "links"=>[], "tags"=>["perturbed", "mir-149", "sub-networks", "luc"], "article_id"=>857859, "categories"=>["Biological Sciences"], "users"=>["Jin Gu", "Zhenyu Xuan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0081032.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_perturbed_miR_149_sub_networks_shared_by_LUC_and_HCC_/857859", "title"=>"The perturbed miR-149 sub-networks shared by LUC and HCC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-20 03:03:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1289505"], "description"=>"a<p>The q-value is due to empirical p-value <0.0001.</p>b<p>(BS) labels mean that the miRNAs are inferred in more than 50% bootstrapping experiments with q-value <0.1.</p>", "links"=>[], "tags"=>["onco-mirs", "inferred", "mirhic", "q-value"], "article_id"=>857860, "categories"=>["Biological Sciences"], "users"=>["Jin Gu", "Zhenyu Xuan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0081032.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_onco_miRs_inferred_by_miRHiC_with_q_value_lt_0_1_/857860", "title"=>"The onco-miRs inferred by miRHiC with q-value <0.1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-20 03:03:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1289506"], "description"=>"a<p>The selected targets with high TargetScan scores and literature evidences (reported in at least 5 PubMed abstracts with key words “liver cancer” or “lung cancer”).</p>b<p>These functional terms (and the corresponding FDRs) are the selected top enriched GO terms of the signature annotated by DAVID web tool.</p>", "links"=>[], "tags"=>["genes", "functions", "inferred", "onco-mir", "perturbed"], "article_id"=>857861, "categories"=>["Biological Sciences"], "users"=>["Jin Gu", "Zhenyu Xuan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0081032.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_selected_target_genes_and_the_related_signature_functions_of_the_inferred_onco_miR_perturbed_target_networks_/857861", "title"=>"The selected target genes and the related signature functions of the inferred onco-miR perturbed target networks.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-20 03:03:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1289510"], "description"=>"<div><p>MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks. However, due to the complexity of cancer transcriptome, current methods often encounter low sensitivity and report few onco-miR candidates. Here, we developed a new method, named miRHiC (enrichment analysis of miRNA targets in Hierarchical gene Co-expression signatures), to infer the perturbed miRNA regulatory networks by using the hierarchical co-expression signatures in large-scale cancer gene expression datasets. The method can infer onco-miR candidates and their target networks which are only linked to sub-clusters of the differentially expressed genes at fine scales of the co-expression hierarchy. On two real datasets of lung cancer and hepatocellular cancer, miRHiC uncovered several known onco-miRs and their target genes (such as miR-26, miR-29, miR-124, miR-125 and miR-200) and also identified many new candidates (such as miR-149, which is inferred in both types of cancers). Using hierarchical gene co-expression signatures, miRHiC can greatly increase the sensitivity for inferring the perturbed miRNA regulatory networks in cancer. All Perl scripts of miRHiC and the detailed documents are freely available on the web at <a href=\"http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/\" target=\"_blank\">http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/</a>.</p></div>", "links"=>[], "tags"=>["perturbed", "microrna", "networks", "cancer", "hierarchical", "co-expression"], "article_id"=>857865, "categories"=>["Biological Sciences"], "users"=>["Jin Gu", "Zhenyu Xuan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0081032"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Inferring_the_Perturbed_microRNA_Regulatory_Networks_in_Cancer_Using_Hierarchical_Gene_Co_Expression_Signatures_/857865", "title"=>"Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-20 03:03:10"}

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Relative Metric

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