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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.

miRNA-gene regulations

(Download full result)

Num microRNA           Gene miRNA log2FC miRNA pvalue Gene log2FC Gene pvalue Interaction Correlation beta Correlation P-value PMID Reported in cancer studies
1 hsa-miR-100-5p AP1AR -1.48 2.0E-5 0.08 0.58996 MirTarget; miRNATAP -0.12 0 NA
2 hsa-miR-100-5p CCNG1 -1.48 2.0E-5 -0.63 0.0004 miRNAWalker2 validate -0.11 2.0E-5 NA
3 hsa-miR-100-5p DPY19L4 -1.48 2.0E-5 0.45 0.00434 miRNAWalker2 validate -0.12 0 NA
4 hsa-miR-100-5p FGFR3 -1.48 2.0E-5 1.16 0.08962 miRNAWalker2 validate; miRTarBase; MirTarget; miRNATAP -0.39 3.0E-5 23778527; 25344675; 26604796; 26018508 Hypoxia regulates FGFR3 expression via HIF 1α and miR 100 and contributes to cell survival in non muscle invasive bladder cancer; We have previously investigated the role of microRNAs in bladder cancer and have shown that FGFR3 is a target of miR-100; In this study we investigated the effects of hypoxia on miR-100 and FGFR3 expression and the link between miR-100 and FGFR3 in hypoxia; Bladder cancer cell lines were exposed to normoxic or hypoxic conditions and examined for the expression of FGFR3 by quantitative PCR qPCR and western blotting and miR-100 by qPCR; The effect of FGFR3 and miR-100 on cell viability in two-dimensional 2-D and three-dimensional 3-D was examined by transfecting siRNA or mimic-100 respectively; Increased FGFR3 was also in part dependent on miR-100 levels which decreased in hypoxia; Hypoxia in part via suppression of miR-100 induces FGFR3 expression in bladder cancer both of which have an important role in maintaining cell viability under conditions of stress;MicroRNA 100 regulates pancreatic cancer cells growth and sensitivity to chemotherapy through targeting FGFR3; The predicted target of miR-100 fibroblast growth factor receptor 3 FGFR3 was downregulated by siRNA to examine its effect on pancreatic cancer cells; Luciferase essay showed FGFR3 was direct target of miR-100; FGFR3 was significantly downregulated by overexpressing miR-100 in pancreatic cancer cells and knocking down FGFR3 by siRNA exerted similar effect as miR-100; Our study demonstrated that miR-100 played an important role in pancreatic cancer development possibly through targeting FGFR3;Overexpression of miR 100 inhibits cell proliferation migration and chemosensitivity in human glioblastoma through FGFR3; Expression of fibroblast growth factor receptor 3 FGFR3 the bioinformatically predicted target of miR-100 was examined by Western blot in glioblastoma; FGFR3 was directly regulated by miR-100 in glioblastoma; Ectopically overexpressing FGFR3 was able to ameliorate the anticancer effects of upregulation of miR-100 on glioblastoma growth migration and chemosensitivity; Overexpressing miR-100 had anticancer effects on glioblastoma likely through regulation of FGFR3;Overexpression of miR 100 inhibits growth of osteosarcoma through FGFR3; Here we reported significantly higher levels of fibroblast growth factor receptor 3 FGFR3 and significantly lower levels of miR-100 in the OS specimen compared to those in the paired normal bone tissues; Bioinformatics analysis and luciferase reporter assay suggest that miR-100 binds to the 3'UTR of FGFR3 mRNA to prevent its translation; Taken together our data demonstrate that miR-100 may inhibit the growth of OS through FGFR3
5 hsa-miR-100-5p FZD5 -1.48 2.0E-5 0.92 0.00022 MirTarget; miRNATAP -0.14 6.0E-5 NA
6 hsa-miR-100-5p H3F3A -1.48 2.0E-5 0.66 2.0E-5 miRNAWalker2 validate -0.14 0 NA
7 hsa-miR-100-5p ID1 -1.48 2.0E-5 -0.11 0.79783 miRNAWalker2 validate -0.39 0 NA
8 hsa-miR-100-5p MAPK6 -1.48 2.0E-5 -0.19 0.3655 miRNAWalker2 validate -0.12 4.0E-5 NA
9 hsa-miR-100-5p MED12 -1.48 2.0E-5 0.12 0.37828 miRNAWalker2 validate -0.1 0 NA
10 hsa-miR-100-5p MTMR3 -1.48 2.0E-5 -0.16 0.27855 MirTarget; miRNATAP -0.12 0 26130569 Antagonism of miR-100 in SK-BR-3 cells increased the expression of MTMR3 a target gene of miR-100 which resulted in the activation of p27 and eventually led to G2/M cell-cycle arrest and apoptosis
11 hsa-miR-100-5p N4BP2 -1.48 2.0E-5 -0.57 0.09042 miRNAWalker2 validate -0.12 0.01407 NA
12 hsa-miR-100-5p NSF -1.48 2.0E-5 0.2 0.16247 miRNAWalker2 validate -0.11 0 NA
13 hsa-miR-100-5p PATZ1 -1.48 2.0E-5 0.34 0.12729 miRNAWalker2 validate -0.19 0 NA
14 hsa-miR-100-5p RAVER2 -1.48 2.0E-5 -1.09 0.00026 MirTarget; miRNATAP -0.15 0.00036 NA
15 hsa-miR-100-5p RPL36A -1.48 2.0E-5 1.1 4.0E-5 miRNAWalker2 validate -0.14 0.00014 NA
16 hsa-miR-100-5p RRM2 -1.48 2.0E-5 3.09 0 miRNAWalker2 validate -0.12 0.00689 NA
17 hsa-miR-100-5p SEPSECS -1.48 2.0E-5 0.14 0.35256 miRNAWalker2 validate -0.14 0 NA
18 hsa-miR-100-5p SRD5A3 -1.48 2.0E-5 0.73 0.00071 miRNAWalker2 validate -0.14 0 NA
19 hsa-miR-100-5p UBN2 -1.48 2.0E-5 0.26 0.1232 miRNAWalker2 validate -0.16 0 NA
20 hsa-miR-100-5p ZADH2 -1.48 2.0E-5 -0.09 0.52147 MirTarget -0.13 0 NA
NumGOOverlapSizeP ValueAdj. P Value
NumGOOverlapSizeP ValueAdj. P Value
NumGOOverlapSizeP ValueAdj. P Value

Over-represented Pathway

NumPathwayPathviewOverlapSizeP ValueAdj. P Value
1 Signaling_pathways_regulating_pluripotency_of_stem_cells_hsa04550 3 139 0.0003434 0.01786
2 p53_signaling_pathway_hsa04115 2 68 0.00208 0.05409
3 Hippo_signaling_pathway_hsa04390 2 154 0.01022 0.1771
4 Rap1_signaling_pathway_hsa04015 2 206 0.01776 0.2308

Quest ID: 633d43c3539ee1ede7a73ab976023459