Entry Detail



General Information

Database ID:exR0196404
RNA Name:hsa-miR-147b-3p
RNA Type:miRNA
Chromosome:chr15
Starnd:+
Coordinate:
Start Site(bp):45433098End Site(bp):45433118
External Links:hsa-miR-147b-3p



Disease Information

Disease Name:
Disease Category:
MeSH ID:
Type:
Alias:



Expression Detail

GEO ID:GSE120584
Description:Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data
Experimental Design:Disease vs Control
Case Disease Type:Alzheimer's Disease
Case Disease SubType:NA
Case Sample:Alzheimer's Disease
Control Sample:Normal
Number of Case:1021
Number of Control:288
Number of Samples:1309





Regulatory Relationship

mRNA targets:
Gene SymbolChromosomeStart Site(bp)End Site(bp)Strand
ADCY9
chr16
3953387
4116442
-
FASN
chr17
82078338
82098294
-
FGFRL1
chr4
1009936
1026898
+
IGF2
chr11
2129112
2141238
-
MGRN1
chr16
4616493
4690974
+
MNT
chr17
2384073
2401104
-
PRDX2
chr19
12796820
12801800
-
SDC4
chr20
45325288
45348424
-
TNPO3
chr7
128954180
129055173
-
miRNA targets:NA
circRNA targets:NA
lncRNA targets:NA
Display:



Experiment Detail

GEO ID:GSE120584
Sample Source:Blood
Source Fraction:Serum
Platform:GPL21263
Method:Microarray
Num of detected RNA Type:1
Num of detected RNAs of this Type:2532
Sample treatment protocol:NA
RNA Extract protocol:Total RNA was extracted each from 300uL serum samples using 3D-Gene® RNA extraction reagent from liquid sample kit (Toray Industries, Inc.).
RNA library preparation protocol:miRNA was labeled using 3D-Gene® miRNA Labeling kit in accordance with the manufacturer's instructions.



Reference

PMID:30820472
Title:Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data
Author:Shigemizu D, Akiyama S, Asanomi Y, Boroevich KA, Sharma A, Tsunoda T, Matsukuma K, Ichikawa M, Sudo H, Takizawa S, Sakurai T, Ozaki K, Ochiya T, Niida S
Journal:Commun Biol. 2019 Feb 25;2:77.
Description:we investigated potential miRNA biomarkers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia