Evidence map›Paper›PMID 34243793›Full record

SynthesisAlzheimer's research & therapy2021

Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer's disease by meta-analysis and adaptive boosting ensemble learning.

Sze Chung Yuen, Xiaonan Liang, Hongmei Zhu, Yongliang Jia, Siu-Wai Leung

Open access · goldAbstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Alzheimer's research & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
3.6field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.

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  11. circRNA fromInternational journal of molecular sciences · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Sze Chung Yuen *State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, 999078, Macao, China.
Xiaonan Liang *State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, 999078, Macao, China.
Hongmei ZhuState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, 999078, Macao, China.
Yongliang JiaState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, 999078, Macao, China.
Siu-Wai LeungShenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China. siuwai.leung@ed.ac.uk.
University of Macau · MOShenzhen Academy of Robotics · CNZhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBlood circulating microRNAs that are specific for Alzheimer's disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis. To enrich the pool of DEmiRNAs for potential AD biomarkers, we used a machine learning method called adaptive boosting for miRNA disease association (ABMDA) to identify eligible candidates that share similar characteristics with the DEmiRNAs identified from meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method.

methodsStudies on DEmiRNAs and their dysregulation states were corroborated with one another by meta-analysis based on a random-effects model. DEmiRNAs identified by meta-analysis were collected as positive examples of miRNA-AD pairs for ABMDA ensemble learning. ABMDA identified similar DEmiRNAs according to a set of predefined criteria. The biological significance of all resulting DEmiRNAs was determined by their target genes according to pathway enrichment analyses. The target genes common to both meta-analysis- and ABMDA-identified DEmiRNAs were collected to construct a network to investigate their biological functions.

resultsA systematic database search found 7841 studies for an extensive meta-analysis, covering 54 independent comparisons of 47 differential miRNA expression studies, and identified 18 reliable DEmiRNAs. ABMDA ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs. These 28 DEmiRNAs and their dysregulated pathways were related to neuroinflammation. The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs. In the biological network constructed from 1865 common target genes of the identified DEmiRNAs, the multiple core ubiquitin-proteasome system, that is involved in neuroinflammation and CCR, was highly connected.

conclusionThis study identified 28 DEmiRNAs as potential AD biomarkers in blood, by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs identified by meta-analysis and ABMDA were significantly related to neuroinflammation, and the ABMDA-identified DEmiRNAs were related to neuronal CCR.

Indexed as

Alzheimer DiseaseMicroRNAsBiomarkersComputational BiologyHumansMachine LearningBiomarkersMicroRNAsABMDAAlzheimer’s diseaseBiomarkersMeta-analysisMicroRNAsNeuroinflammationNeuronal cell cycle re-entry

Identifiers

PMID34243793
PMCPMC8272278
OpenAlexW3180860900

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.