Entry Detail



General Information

Database ID:exR0196257
RNA Name:hsa-miR-1247-5p
RNA Type:miRNA
Chromosome:chr14
Starnd:-
Coordinate:
Start Site(bp):101560362End Site(bp):101560383
External Links:hsa-miR-1247-5p



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
HNRNPUL1
chr19
41262496
41307787
+
ELK4
chr1
205597556
205632011
-
IER5
chr1
181088700
181092900
+
miRNA targets:NA
circRNA targets:NA
lncRNA targets:
lncRNA SymbolChromosomeStart Site(bp)End Site(bp)Strand
AC234582.1
chr1
155195004
155205495
+
NEAT1
chr11
65422774
65445540
+
SNHG22
chr18
49814023
49851059
+
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