exRNAdisease

Entry Detail

General Information

Database ID:

exR0196180

RNA Name:

hsa-miR-100-5p

RNA Type:

miRNA

Chromosome:

chr11

Starnd:

-

Coordinate:

Start Site(bp):

122152275

End Site(bp):

122152296

External Links:

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 Symbol	Chromosome	Start Site(bp)	End Site(bp)	Strand
CAPNS1	chr19	36139953	36150353	+
MEF2D	chr1	156463727	156500779	-

miRNA targets:

NA

circRNA targets:

NA

lncRNA targets:

lncRNA Symbol	Chromosome	Start Site(bp)	End Site(bp)	Strand
AC051619.7	chr15	45200325	45200632	-
LINC00943	chr12	126723412	126749027	+

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