This regulatory network was inferred from the input dataset. The miRNAs and mRNAs are
presented as round and rectangle nodes respectively. The numerical value popped up upon mouse over the gene node is the log2 transformed fold-change of the gene expression between the two groups. All of the nodes are clickable, and the detailed information of the miRNAs/mRNAs and related cancer pathway will be displayed in another window. The edges between nodes are supported by both interactions (predicted or experimentally verified) and correlations learnt from cancer dataset. The numerical value popped up upon mouse over the edge is the correlation beat value (effect size) between the two nodes. The experimental evidences of the edges reported in previous cancer studies are highlighted by red/orange color. All of these information can be accessed by the "mouse-over" action. This network shows a full map of the miRNA-mRNA regulation of the input gene list(s), and the hub miRNAs (with the high network degree/betweenness centrality) would be the potential cancer drivers or tumor suppressors. The full result table can be accessed in the "Regulations" tab.
"miRNACancerMAP" is also a network visualization tool for users to draw their regulatory network by personal customization. Users can set the complexity of the network by limiting the number of nodes or edges. And the color of the nodes can be defined by different categories of the mRNAs and miRNAs, such as Gene-Ontology, pathway, and expression status. Users can also select to use network degree or network betweenness centrality to define the node size. And edges can be black or colored by the correlation. Purple edge means negative correlation (mostly found between miRNA and mRNA), and blue edge means positive correlation (found in PPI or miRNA-miRNA sponge effect). We can also add the protein-protein interactions (PPI) into the network. This result will show the cluster of genes regulated by some specific miRNAs. Additionally, miRNA-miRNA edges can be added by the "miRNA sponge" button, presenting some clusters of miRNAs that have the interactions via sponge effect.
|Num||microRNA||Gene||miRNA log2FC||miRNA pvalue||Gene log2FC||Gene pvalue||Interaction||Correlation beta||Correlation P-value||PMID||Reported in cancer studies|
|7||hsa-miR-143-3p||DNMT3A||-2.58||0||0.85||0||miRNAWalker2 validate; miRTarBase; miRNATAP||-0.11||0||19638978; 24218337||Using in silico predictions DNA methyltranferase 3A DNMT3A was defined as a potential target of miR-143; Restoration of the miR-143 expression in colon cell lines decreased tumour cell growth and soft-agar colony formation and downregulated the DNMT3A expression in both mRNA and protein levels; DNMT3A was shown to be a direct target of miR-143 by luciferase reporter assay; Furthermore the miR-143 expression was observed to be inversely correlated with DNMT3A mRNA and protein expression in CRC tissues; Our findings suggest that miR-143 regulates DNMT3A in CRC;Ectopic expression of miR-143 inhibited proliferation and soft agar colony formation of breast cancer cells and also downregulated DNA methyltransferase 3A DNMT3A expression on both mRNA and protein levels; DNMT3A was demonstrated to be a direct target of miR-143 by luciferase reporter assay; Furthermore miR-143 expression was observed to be inversely correlated with DNMT3A mRNA and protein expression in breast cancer tissues; Our findings suggest that miR-143 regulates DNMT3A in breast cancer cells|
|15||hsa-miR-143-3p||FHIT||-2.58||0||-0.65||0.11656||miRNAWalker2 validate; miRTarBase; MirTarget||-0.14||0.01858||NA|
|18||hsa-miR-143-3p||HK2||-2.58||0||0.98||0.00025||miRNAWalker2 validate; miRTarBase||-0.16||2.0E-5||25322940; 27574783; 23376635; 22691140; 26269764; 24033605||Furthermore knockdown of ATG2B and hexokinase 2 a key enzyme in glycolysis and another target of miR-143 co-ordinated to inhibit the proliferation of H1299 cells;We previously identified hexokinase 2 the major glycolytic enzyme as a target gene of miR-143 in TNBC;We show that miR-143 is significantly down-regulated in glioma tissues and glioblastoma stem-like cells GSLCs while miR-143 over-expression inhibits glycolysis by directly targeting hexokinase 2 and promotes differentiation of GSLCs;MicroRNA 143 down regulates Hexokinase 2 in colon cancer cells; Here we report the identification of Hexokinase 2 HK2 as a direct target of miR-143; We have identified and validated HK2 as a miR-143 target; Furthermore our results indicate that miR-143 mediated down-regulation of HK2 affects glucose metabolism in colon cancer cells;MicroRNA 143 acts as a tumor suppressor by targeting hexokinase 2 in human prostate cancer; Furthermore we identified hexokinase 2 HK2 a metabolic enzyme that executes the first step of aerobic glycolysis as a target of miR-143 in prostate cancer; Knockdown of HK2 recapitulated the effects of miR-143 and accompanied with decreased glucose metabolism;Luciferase reporter assays showed that both miR-143 and miR-145 directly regulated HK2|
|21||hsa-miR-143-3p||HRAS||-2.58||0||0.62||0.00176||miRNAWalker2 validate; miRTarBase||-0.16||0||21276449||The Evi1 microRNA 143 K Ras axis in colon cancer|
|25||hsa-miR-143-3p||MYO6||-2.58||0||0.45||0.03399||miRNAWalker2 validate; miRTarBase; MirTarget; miRNATAP||-0.11||0.00033||20353999||A computational search indicated the 3'-untranslated region UTR of the mRNA for myosin VI MYO6 as a potential target for both miR-143 and miR-145 the expression of which was reduced in the tumor tissues; In luciferase reporter analysis we find a significant negative regulatory effect on the MYO6 3'UTR by both miR-143 and miR-145; Mutation of the potential binding sites for miR-143 and miR-145 in the MYO6 3'UTR resulted in a loss of responsiveness to the corresponding miRNA; Our data indicate that miR-143 and miR-145 are involved in the regulation of MYO6 expression and possibly in the development of prostate cancer|
|Num||Pathway||Pathview||Overlap||Size||P Value||Adj. P Value|