Visible node/edge number:
Node colors:
Node labels:

show label of nodes with degree >=

Node sizes:
Edge colors:
Edge widths factor:
Layout:

gravity >=

edgeLength =

GRN in network:

Notice: IE browser need to manually refresh (F5) this page after resetting the network.

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-140-5p DNPEP -0.76 0 0.47 0 miRTarBase -0.16 0 NA
2 hsa-miR-140-5p SOX2 -0.76 0 1.63 0 miRTarBase -0.26 0.02297 23060440; 25426255 We found that the stem cell self-renewal regulator SOX2 is a novel target of miR-140 and that this miR-140/SOX2 pathway critically regulates breast tumor-initiating cell survival providing a new link between ERĪ± signaling and breast cancer stem cell maintenance;miR-140 plays an important tumor suppressive role in the Wnt SOX2 and SOX9 stem cell regulator pathways
NumGOOverlapSizeP ValueAdj. P Value
1 CIRCULATORY SYSTEM PROCESS 10 366 6.424e-06 0.009963
2 ORGANONITROGEN COMPOUND CATABOLIC PROCESS 10 343 3.625e-06 0.009963
3 PEPTIDE CATABOLIC PROCESS 4 25 4.715e-06 0.009963
NumGOOverlapSizeP ValueAdj. P Value
1 AMINOPEPTIDASE ACTIVITY 10 43 3.875e-15 3.6e-12
2 EXOPEPTIDASE ACTIVITY 11 106 1.813e-12 8.421e-10
3 METALLOAMINOPEPTIDASE ACTIVITY 4 15 5.268e-07 0.0001631
4 METALLOPEPTIDASE ACTIVITY 8 188 2.293e-06 0.0005327
5 PROTEIN KINASE ACTIVITY 13 640 6.331e-06 0.001176
6 TRANSFERASE ACTIVITY TRANSFERRING PHOSPHORUS CONTAINING GROUPS 16 992 9.308e-06 0.001441
7 KINASE ACTIVITY 14 842 2.586e-05 0.003432
8 PROTEIN SERINE THREONINE KINASE ACTIVITY 10 445 3.46e-05 0.004018
9 PEPTIDASE ACTIVITY 12 663 4.592e-05 0.00474
10 METALLOEXOPEPTIDASE ACTIVITY 4 53 9.903e-05 0.0092
NumGOOverlapSizeP ValueAdj. P Value

Over-represented Pathway

NumPathwayPathviewOverlapSizeP ValueAdj. P Value
1 mTOR_signaling_pathway_hsa04150 7 151 5.793e-06 0.0003012
2 MAPK_signaling_pathway_hsa04010 7 295 0.0003977 0.007531
3 Signaling_pathways_regulating_pluripotency_of_stem_cells_hsa04550 5 139 0.0004345 0.007531
4 FoxO_signaling_pathway_hsa04068 4 132 0.003111 0.04044
5 Autophagy_other_hsa04136 2 32 0.009294 0.09666
6 Oocyte_meiosis_hsa04114 3 124 0.01903 0.1425
7 Autophagy_animal_hsa04140 3 128 0.02068 0.1425
8 PI3K_Akt_signaling_pathway_hsa04151 5 352 0.02231 0.1425
9 Apelin_signaling_pathway_hsa04371 3 137 0.02467 0.1425
10 Cellular_senescence_hsa04218 3 160 0.03661 0.1904
11 Peroxisome_hsa04146 2 83 0.05496 0.2598
12 Rap1_signaling_pathway_hsa04015 3 206 0.06777 0.2773
13 Regulation_of_actin_cytoskeleton_hsa04810 3 208 0.06933 0.2773
14 TNF_signaling_pathway_hsa04668 2 108 0.08684 0.3096
15 Ras_signaling_pathway_hsa04014 3 232 0.08931 0.3096
16 Phagosome_hsa04145 2 152 0.1522 0.4415
17 Hippo_signaling_pathway_hsa04390 2 154 0.1554 0.4415
18 Jak_STAT_signaling_pathway_hsa04630 2 162 0.1682 0.4415
19 cGMP_PKG_signaling_pathway_hsa04022 2 163 0.1698 0.4415
20 Calcium_signaling_pathway_hsa04020 2 182 0.2009 0.4749
21 cAMP_signaling_pathway_hsa04024 2 198 0.2276 0.4913
22 Focal_adhesion_hsa04510 2 199 0.2292 0.4913
23 Endocytosis_hsa04144 2 244 0.305 0.5579

Quest ID: ae921f019cf93c0a31eed4dcaf95c2d8