Abstract
Background
Blood 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.
Methods
Studies 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.
Results
A 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.
Conclusion
This 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.
Supplementary Information
The online version contains supplementary material available at
10.1186/s13195-021-00862-z.
Keywords: Alzheimer’s disease, Meta-analysis, MicroRNAs, ABMDA,
Biomarkers, Neuroinflammation, Neuronal cell cycle re-entry
Background
Alzheimer’s disease (AD) is subcellularly characterized by the presence
of extracellular amyloid-beta (Aβ) plaque deposition and intracellular
neurofibrillary tangles of hyperphosphorylated tau proteins [[33]1].
The aberrant protein aggregates are accompanied by activation of
neuroinflammation, and loss of synaptic functions [[34]2]. In the
progression of AD, irreversible loss of neurons and synaptic functions
gradually develops over decades before the manifestation of cognitive
symptoms [[35]3]. Because the root causes of pathological Aβ
accumulation and hyperphosphorylated tau proteins are not clear, drug
development for AD often fails and current AD treatments alleviate
symptoms only. The failure of most clinical trials in AD has been
partially attributed to the lack of sensitive biomarkers to identify
potential AD [[36]4], which can identify and enroll patients at the
early stage of AD, as it may be too late to rescue the dysfunction
present in advanced stages of the disease.
The National Institute of Neurological and Communicative Disorders and
Stroke and the Alzheimer’s Disease and Related Disorders Association
(NINCDS-ADRDA) and the National Institute on Aging and Alzheimer’s
Association (NIA-AA) proposed that the diagnosis of AD should be
dependent on biomarkers rather than solely dependent on clinical
symptoms [[37]5, [38]6]. Aberrant levels of Aβ and tau proteins in
cerebrospinal fluid (CSF) and blood have been evaluated as biomarkers
for AD diagnosis [[39]7, [40]8], specifically, increased levels of
total and hyperphosphorylated tau proteins, and decreased levels of Aβ
in CSF. CSF levels of total and hyperphosphorylated tau proteins are
correlated with neurofibrillary tangle load, and CSF levels of Aβ are
inversely correlated with amyloid load [[41]9]. However, a review
concluded that these CSF biomarkers are more useful for ruling out AD,
than for indicating a definite diagnosis [[42]10]. To avoid the side
effects of invasive CSF sampling, such as positional headache, Fei et
al. [[43]11] have proposed the use of peripheral blood Aβ detection,
such as the ratio of Aβ42/Aβ40. However, another review has reported
that lower plasma Aβ42/Aβ40 ratios might not be associated with
increased AD risk [[44]12]. The plasma phosphorylated tau 217 is
proposed to distinguish AD from non-AD neurodegenerative individuals,
since its level increases more steeply in non-demented individuals with
amyloid positivity than those without amyloid positivity [[45]13]. The
limitations to use Aβ or tau proteins as biomarkers in blood might be
due to several reasons. First, Aβ in both CSF and blood tends to
self-aggregate [[46]14], masking epitopes for detection and reducing
the correlation with AD. Second, the entrance of Aβ and tau proteins
into body fluid is hindered by the extent of blood–brain barrier (BBB)
leakage [[47]15]. Thus, there is a need to identify other potential
non-invasive biomarkers to aid the diagnosis of AD. An ideal AD
biomarker would allow for mass screening to identify patients at high
risk of developing AD in the presymptomatic stage with adequate
reliability.
MicroRNAs (miRNAs) are a class of single-stranded non-coding RNAs that
are 18–25 nucleotides in length and bind to the 3′ untranslated region
of target mRNAs to modify the target mRNAs’ expression in a
post-transcriptional manner [[48]16]. Each miRNA simultaneously targets
hundreds of mRNAs, and over 2500 mature miRNAs have been recorded in
the latest version of the miRBase database [[49]17]. Out of all
recorded miRNAs, 300 have been associated with neurodegenerative
diseases and 131 miRNAs are specific for AD [[50]18]. Downregulated
expressions of miR-29a/b-1, miR-29c, and miR-339-5p have been reported
to upregulate the expression of BACE1 in AD brain, thereby increasing
Aβ production [[51]19–[52]21]. The expression of miR-15/107 in cerebral
cortical gray matter is correlated with amyloid plaque density
[[53]22]. The AD-dysregulated miRNAs in brain have been associated with
neuroinflammation and cell cycle regulation [[54]23, [55]24] and may be
released into peripheral blood through the BBB [[56]25] and transported
by lipoproteins in circulation for stability [[57]26]. These stable
AD-dysregulated miRNAs in blood may reflect the composition of
dysregulated miRNAs in brain, suggesting that they may be potential
biomarkers of AD. The dysregulated miRNAs were found in plasma and
serum of AD patients [[58]27, [59]28], e.g., the expression of miR-125b
in serum of AD patients is correlated with Mini-Mental State
Examination (MMSE) scores [[60]29]. Compared with biomarkers in brain
or CSF, blood circulating biomarkers are preferable for their higher
accessibility. However, before establishing any miRNAs as AD
biomarkers, it is necessary to first evaluate their reliability and
consistency among different studies of differentially expressed miRNAs
(DEmiRNAs) in AD patients, and their biological significance to
understand their roles in the pathogenesis of AD.
Thus far, the development of blood circulating DEmiRNAs as AD
biomarkers has been hindered by inconsistent and unreliable studies.
For example, Denk et al. [[61]30] and Wu et al. [[62]31] conducted
similar studies using quantitative real-time polymerase chain reaction
(qRT-PCR) to quantify the expression of miRNAs in serum, and found 22
and 9 DEmiRNAs, respectively. Only one DEmiRNA, miR-146a-5p, was common
between the two studies. Further, the studies reported the same
miR-146a-5p to have opposite directions of dysregulation in AD. This
inconsistency can be attributed to differences in the AD patients
recruited for the trials, including the presence of other disease
conditions that might influence the levels of biological molecules in
blood, and the stages of disease progression [[63]32, [64]33]. A
meta-analysis of these inconsistent DEmiRNA results may resolve
discrepancies and enhance generalizability of the results. Takousis et
al. [[65]34] conducted a meta-analysis to identify DEmiRNAs in brain,
blood, and CSF from expression profiling studies of AD. They applied
Stouffer’s method to integrate the P values of every DEmiRNA from each
independent study and found 32 statistically significant DEmiRNAs in
blood. However, Stouffer’s method does not include information about
dysregulation states of DEmiRNAs and its use of P values is outdated in
terms of the methodology of meta-analysis; thus, the results were
probably biased by the number of studies reporting the same DEmiRNAs.
Hu et al. and Zhang et al. [[66]35, [67]36] also conducted
meta-analyses independently and reported that the sensitivity (86% and
80%, respectively) and specificity (87% and 83%, respectively) of
blood-based miRNAs are comparable with those of
fluorodeoxyglucose-positron emission tomography (FDG-PET; sensitivity:
91%, specificity: 86%) [[68]37] for AD diagnosis. The results of these
meta-analyses demonstrate that some blood circulating DEmiRNAs may be
useful for AD diagnosis, but there has not been extensive
identification of DEmiRNAs that may serve as AD biomarkers.
Structural and functional patterns of similar DEmiRNAs can be
identified by machine learning, which has been applied to the
identification of AD biomarkers. Studies using different machine
learning techniques have identified different DEmiRNAs for AD diagnosis
with up to 89% accuracy [[69]38–[70]40]. Until recently, machine
learning mainly focused on fine tuning the set of DEmiRNAs identified
from differential miRNA expression profiling studies to obtain a
smaller panel of miRNAs for AD diagnosis. As an emerging trend, machine
learning is being used to predict potential DEmiRNAs as AD biomarkers
based on DEmiRNAs identified from differential expression profiling
studies. For example, Zhao et al. [[71]41] used the adaptive boosting
for miRNA disease association (ABMDA) ensemble learning method to
identify miRNAs that are associated with a disease. ABMDA is a
supervised learning approach with validation against the Human MicroRNA
Disease Database (HMDD) [[72]42]. Its clustering algorithm is based on
k-means distance, and its boosting technique combines classifiers by
their corresponding weights to form a stronger classifier. The ABMDA
ensemble learning method relies upon the initial set of DEmiRNAs to
predict additional DEmiRNAs. In the present study, we used
meta-analysis to enhance the reliability of the initial DEmiRNAs and
augment the prediction performance of the ABMDA ensemble learning
method.
This study aimed to identify blood circulating DEmiRNAs as potential AD
biomarkers by augmenting the DEmiRNAs identified by meta-analysis with
the ABMDA ensemble learning method. The meta-analysis was conducted
under the guidance of the PRISMA statement [[73]43], with the
adjustment in the risk of bias assessment.
Methods
The present meta-analysis was mainly conducted using R and Python
packages and is illustrated in Fig. [74]1. If an included study
contained several comparisons, the DEmiRNAs from each comparison were
collected independently. The sample sizes of control and AD groups, and
DEmiRNAs and their dysregulated states from each independent comparison
were extracted. The names of all reported DEmiRNAs were standardized
based on the miRBase database [[75]17] before the meta-analysis. A
meta-analysis for the DEmiRNAs was performed when more than one
comparison reported the same DEmiRNA. Statistically significant
DEmiRNAs from the meta-analysis were collected as positive miRNA–AD
associations for the ABMDA ensemble learning method to further increase
the number of positive miRNA–AD associations. The ABMDA-identified
miRNAs with scores higher than the predefined criteria, together with
the DEmiRNAs identified by the meta-analysis, were treated as potential
AD biomarkers in blood. The biological significance of the identified
DEmiRNAs was studied by a biological pathway enrichment analysis and
network analysis of their corresponding target genes. The combination
of meta-analysis and ABMDA ensemble learning might be beneficial to
resolve the existing inconsistencies and perform systematical
predictions based on reliable results.
Fig. 1.
[76]Fig. 1
[77]Open in a new tab
Overall study design. The italic font and normal font on the right-hand
side of the arrows represent the R package and python-based algorithm,
respectively. The name of each database is shown on the left-hand side
of the arrow
Differential miRNA expression studies selection
Differential miRNA expression studies were collected from PubMed from
inception until January 10, 2020, from ScienceDirect and Web of Science
between 1993 and 2019. The search strategy is shown below:
For PubMed, (“Alzheimer’s disease” [MESH Terms] OR “Alzheimer’s
disease” [All Fields] OR “alzheimer*” [MESH Terms] OR “alzheimer*” [All
Fields]) AND (“microRNA*” [MESH Terms] OR “microRNA*” [All Fields] OR
“miRNA*” [MESH Terms] OR “miRNA*” [All Fields])
For ScienceDirect and Web of Science, (microRNA OR miRNA) AND Alzheimer
After the search, two authors (SCY and XNL) screened all titles,
abstracts, and full texts independently according to the eligibility
criteria. Disagreements between the authors were resolved by discussion
with the other authors.
The case-control studies reporting differential miRNA expression in
blood from AD and healthy participants were included. The included
studies had to report the differential expression profiling methods,
sample sizes of the disease and control groups, and statistical
significance for each DEmiRNA. Studies were excluded if they (1) were
not AD research (i.e., focused on other dementia disease conditions);
(2) reported irrelevant effects on differential miRNA expression
profiles (i.e., demonstrated treatment effects on the differential
miRNA expression); (3) did not report the blood DEmiRNAs for AD and
healthy participants (i.e., were not controlled studies, or recruited
AD participants suffering from other diseases); (4) did not report
DEmiRNAs and their corresponding dysregulation states (i.e., up or
down) as outcomes; or (5) did not obtain DEmiRNAs by a validation
method (i.e., qRT-PCR).
Meta-analysis
For each study, the following data were extracted for the
meta-analysis: (1) comparison ID (PubMed ID with blood elements or
disease stages); (2) blood elements; (3) differential miRNA expression
profiling method; (4) number of AD cases; (5) number of control cases;
and (6) DEmiRNAs and their dysregulated states. For studies that
reported both screening and validation results of differential miRNA
expression, only the results from validation methods were collected.
The names of extracted DEmiRNAs were standardized using the
miRNAmeConverter [[78]44] package for R software with the miRBase
database [[79]17]. For each DEmiRNA that was reported by more than one
comparison, a meta-analysis was performed using the metafor [[80]45]
package for R software. The meta-analysis was conducted for each
qualified DEmiRNA, independently, under a random-effects model. For
each qualified DEmiRNA from independent comparisons, that was reported
as binary dysregulation (i.e., upregulation or downregulation) in AD
group compared with healthy control group. The effect sizes of binary
data were calculated as log[e] odds ratios (logORs) based on the number
of dysregulation events in both disease and control samples. The
heterogeneity of each DEmiRNA was reported as tau square (τ^2) based on
the restricted maximum-likelihood estimator and I^2 statistics. The
outcomes were logORs with a 95% confidence interval (CI), P values,
τ^2, and I^2. For each DEmiRNA in the ith study, the effect size (θ[i]
) based on the numbers of dysregulation events in both AD and control
samples was calculated according to the formula, log(
[MATH:
Ai∗DiBi∗Ci :MATH]
), where A[i] and B[i] (C[i] and D[i]) represent the number of
upregulated and downregulated cases in the disease (control) group,
respectively. Then the overall effect was computed according to
formula,
[MATH:
∑Wiθi∑Wi :MATH]
, where W[i] is the weight and is equal to 1/(v[i] + τ^2), where v[i]
is the sample variance. A larger sample size has more weight on the
overall effect size. The P values were adjusted by false discovery rate
(FDR), and DEmiRNAs with FDR-adjusted P values less than 0.05 were
regarded as statistically significant. Statistically significant
DEmiRNAs with logORs above or below 0 were considered upregulated or
downregulated, respectively, in AD compared with healthy controls.
Subgroup analysis was conducted based on the DEmiRNAs collected from
the included differential miRNA expression studies. The DEmiRNAs were
split into four different subgroups based on the blood sample sources,
i.e., whole blood, plasma, serum, and peripheral blood mononuclear cell
(PBMC).
ABMDA identification of DEmiRNAs
ABMDA ensemble learning was used to identify potential miRNA–disease
associations. The original miRNA–disease associations with significant
dysregulation in the “circulation_biomarker_diagnosis” category were
extracted from HMDD, in which the associations are experimentally
verified. DEmiRNAs identified in the meta-analysis with adjusted P
values less than 0.05 were input into the ABMDA ensemble learning
method as positive miRNA–AD associations to further increase the number
of positive miRNA–AD associations. In addition to initial miRNA–disease
associations, ABMDA ensemble learning also requires disease–disease
similarity and miRNA–miRNA functional similarity. The disease–disease
similarity was determined using the DOSE [[81]46] package for R
software. The miRNA–miRNA functional similarity was retrieved from the
database MISIM [[82]47]. The ABMDA-identified DEmiRNAs were sorted by
their prediction scores, and the DEmiRNAs with top prediction scores
were collected until the first predicted DEmiRNA that was reported
statistically insignificant in the meta-analysis. The names of
predicted DEmiRNAs were standardized using the miRNAmeConverter package
for R software with the miRBase database [[83]17]. The ABMDA-identified
DEmiRNAs with the top prediction scores, together with the
meta-analysis DEmiRNAs were considered potential AD biomarkers.
Biological significance of DEmiRNAs from meta-analysis and ABMDA by
independent and synergic biological enrichment analysis
Each miRNA regulates a large number of genes to exert a profound
influence on genetic expression in specific cellular functions, and the
primary function of each miRNA can be understood by identifying its
target genes. The target genes of the DEmiRNAs from the meta-analysis
and ABMDA identification were obtained using the multiMiR [[84]48]
package for R software based on the database miRTarBase [[85]49]. The
target genes of two DEmiRNA categories were collected separately to
conduct two independent enrichment analyses and were also combined to
conduct a synergic enrichment analysis. The biological enrichment
analysis was conducted using the clusterProfiler [[86]50] package for R
software based on the Kyoto Encyclopedia of Genes and Genomes (KEGG)
[[87]51]. The pathways with FDR-adjusted P values less than 0.05 were
considered statistically significant.
Collaborative biological function of DEmiRNAs from meta-analysis and ABMDA by
network analysis
The target gene semantic similarity measurements of the DEmiRNAs
identified from the meta-analysis and ABMDA were computed using the
DOSE package for R. The target genes with semantic similarity over 0.95
were treated as common target genes in the two DEmiRNA categories. The
DEmiRNAs and common target genes of the two DEmiRNA categories were
used to construct a DEmiRNA–gene network based on the data from STRING
(version 11) [[88]52] to investigate the collaborative function of the
DEmiRNAs identified from the meta-analysis and ABMDA. Only interactions
with the highest confidence (0.9) were kept from the STRING.
Risk of bias
The risk of bias for each included study was evaluated according to the
Minimum Information for Publication of Quantitative Real-time PCR
Experiments (MIQE) [[89]53]. This guideline is designed to assess the
quality of qRT-PCR data and describes the minimum information required
to ensure that experimental results can be comprehensively interpreted
and independently verified. In the present study, the guideline was
used to assess the quality of the expression profiling analysis of the
included studies, including experimental design, sample annotation,
experimental procedure, data processing pipeline, and result
presentation. The evaluation was performed by two authors (SCY and
XNL), independently. Disagreements between the authors were resolved by
discussion with the other authors. Items with low risk were counted
+ 1, suggesting high reproducibility; items with unclear risk were
counted 0, suggesting ambiguous reproducibility; and items with high
risk were counted − 1, suggesting low reproducibility.
Results
Differential miRNA expression studies included in the analysis
The selection process of the differential miRNA expression studies is
shown in Fig. [90]2. A total of 7841 studies were initially identified
from PubMed, ScienceDirect, and Web of Science. After a systematic
search, 47 studies met the eligibility criteria and were included in
the present study [[91]23, [92]27–[93]31, [94]39, [95]40,
[96]54–[97]92]. The characteristics of the included studies are shown
in Table [98]1. The studies mainly focused on four blood elements:
serum, plasma, whole blood, and peripheral blood mononuclear cells.
Serum (n = 19) was the most extensively studied, followed by plasma (n
= 16).
Fig. 2.
[99]Fig. 2
[100]Open in a new tab
Flow diagram of the differential miRNA expression study selection,
including the identification, screening, eligibility, and inclusion
stages
Table 1.
Characteristics of the included differential miRNA expression studies
PubMed ID Comparison ID Blood elements Differential miRNA expression
profiling methods (screening method/validation method) Number of
control case Number of AD case Number of upregulated miRNA Number of
downregulated miRNA
31857133 31857133_Ser Serum qRT-PCR 93 108 1
31811079 31811079_Ser Serum qRT-PCR 51 32 9 3
31849573 31849573_Pla Plasma microRNA microarray/qRT-PCR 31 16 2
31809862 31809862_WB Whole blood qRT-PCR 214 145 1 1
31766231 31766231_Pla Plasma microRNA microarray/qRT-PCR 29 23 1
31691877 31691877_Ser Serum qRT-PCR 30 30 4
31592314 31592314_Pla Plasma RNA deep-sequencing/qRT-PCR 11 10 6
31572518 31572518_Ser Serum qRT-PCR 98 105 1
31420923 31420923_Pla Plasma qRT-PCR 120 120 2
31092279 31092279_Pla Plasma qRT-PCR 14 56 3
30914454 30914454_Pla Plasma qRT-PCR 385 385 5
30328325 30328325_Pla Plasma qRT-PCR 20 20 1
29966198 29966198_Pla Plasma qRT-PCR 10 10 2
29746584 29746584_Ser Serum qRT-PCR 38 47 14 8
29635818 29635818_Pla Plasma qRT-PCR 20 20 1
29606187 29606187_Ser Serum qRT-PCR 107 228 2 1
29036829 29036829_WB Whole blood microRNA microarray/qRT-PCR 17 21 3
29036818 29036818_Ser_Mild Serum RNA deep-sequencing/qRT-PCR 86 31 1 5
29036818_Ser_Mod Serum RNA deep-sequencing/qRT-PCR 86 52 3 5
29036818_Ser_Sev Serum RNA deep-sequencing/qRT-PCR 86 38 4 5
28934394 28934394_Ser Serum microRNA microarray/qRT-PCR 18 11 1
28849039 28849039_Ser_Mil Serum microRNA microarray/qRT-PCR 30 30 1
28849039_Ser_Mod Serum microRNA microarray/qRT-PCR 30 30 1
28626163 28626163_Ser Serum RNA deep-sequencing/qRT-PCR 40 45 5 4
28179587 28179587_Pla_AD2 Plasma qRT-PCR 9 13 4 3
28179587_Pla_MCI-AD2 Plasma qRT-PCR 9 8 4
28137310 28137310_Ser Serum RNA deep-sequencing/qRT-PCR 22 36 1
27545218 27545218_Ser Serum qRT-PCR 40 48 1
27501295 27501295_WB Whole blood qRT-PCR 109 172 4
27446280 27446280_WB Whole blood qRT-PCR 25 25 1
27277332 27277332_WB Whole blood qRT-PCR 30 30 1
27239545 27239545_PBMC Peripheral blood mononuclear cells qRT-PCR 36 36
3
27027823 27027823_Ser Serum qRT-PCR 62 84 1 3
26973465 26973465_Pla Plasma microRNA microarray/qRT-PCR 40 40 3
26497032 26497032_PBMC Peripheral blood mononuclear cells microRNA
microarray/qRT-PCR 41 45 2 1
26078483 26078483_Ser Serum RNA deep-sequencing/qRT-PCR 75 79 4
25955795 25955795_WB Whole blood qRT-PCR 30 30 1
25742200 25742200_Pla Plasma qRT-PCR 81 97 1
25667669 25667669_Ser Serum qRT-PCR 42 26 1
25152461 25152461_Ser Serum microRNA microarray/qRT-PCR 30 38 2
24827165 24827165_Pla Plasma qRT-PCR 7 7 1
24827165_Ser Serum qRT-PCR 7 7 1
24577456 24577456_Ser Serum RNA deep-sequencing/qRT-PCR 155 158 6
24550773 24550773_Pla Plasma qRT-PCR 27 25 1
24550773_PBMC Peripheral blood mononuclear cells qRT-PCR 27 25 1
24157723 24157723_Pla Plasma qRT-PCR 10 10 2
24139697 24139697_Ser Serum qRT-PCR 155 105 1 2
24064186 24064186_PBMC_Mon Monocytes qRT-PCR 37 34 1
24064186_PBMC_Lym Lymphocytes qRT-PCR 37 34 1
23922807 23922807_Pla_C2 Plasma microRNA microarray/qRT-PCR 17 20 6
23895045 23895045_WB Whole blood RNA deep-sequencing/qRT-PCR 21 94 5 7
23435408 23435408_PBMC Peripheral blood mononuclear cells qRT-PCR 25 28
1
22155483 22155483_Ser Serum qRT-PCR 7 7 5
19936094 19936094_PBMC Peripheral blood mononuclear cells microRNA
microarray/qRT-PCR 5 5 2
[101]Open in a new tab
Identification of DEmiRNAs from meta-analysis
After DEmiRNA name standardization, there were 115 DEmiRNAs reported in
54 independent comparisons of 47 differential expression studies that
compared AD blood samples with healthy blood samples. Eight-eight
DEmiRNAs were found in one blood element, 25 DEmiRNAs were found in two
blood elements, two DEmiRNAs were found in three blood elements, and no
DEmiRNAs were found in all four blood elements. The most frequently
reported DEmiRNAs in AD blood were miR-146a-5p and miR-26a-5p, which
were dysregulated in five independent comparisons (Table [102]2). Of
the 115 DEmiRNAs, 43 (37.4%) were reported in at least two independent
comparisons; dysregulation of 18 of them was reported consistently in
the same direction, whereas dysregulation of 25 of them was reported in
different directions. Based on the currently available data, none of
the inconsistent DEmiRNA results were resolved in this meta-analysis.
In the meta-analysis of 43 dysregulated miRNAs, 18 DEmiRNAs were found
to be statistically significant (Table [103]3; Additional file [104]1);
7 and 11 DEmiRNAs were upregulated and downregulated, respectively.
Among the 18 DEmiRNAs identified in this study, 6 of them (let-7d-5p,
miR-107, miR-128-3p, miR-191-5p, miR-29c-3p, and miR-93-5p) were also
found statistically significant in a previous meta-analysis [[105]34].
The discrepancy could be due to that we only included the literature
with qRT-PCR as validation results, and we treated comparisons
independently even that those comparisons were from the same
literature. Meanwhile, 13 DEmiRNAs of our meta-analytical results
(let-7d-5p, miR-106b-3p, miR-107, miR-126-5p, miR-148b-5p, miR-181c-3p,
miR-191-5p, miR-200a-3p, miR-22-3p, miR-483-5p, miR-486-5p, miR-502-3p,
and miR-93-5p), were reported contributing the AD diagnostic values in
sensitivity and specificity in Hu et al. and Zhang et al. [[106]35,
[107]36]. The most significantly downregulated DEmiRNA was miR-107,
which was identified in four independent comparisons. MiR-106b-39 was
the most significantly upregulated DEmiRNA among four independent
comparisons. Downregulation of miR-107 has been reported to increase
BACE1 expression [[108]93] and influence cell cycle protein expression
[[109]94]. The dysregulation of miR-106b-3p is negatively correlated
with MMSE score [[110]66] and modulates Aβ metabolism [[111]95]. Most
DEmiRNAs identified in the meta-analysis were associated with mediating
Aβ generation, tau protein phosphorylation, and neuronal functions
maintenance.
Table 2.
DEmiRNAs reported by the included differential miRNA expression
studies. The bold DEmiRNAs were reported by at least two independent
comparisons and were qualified for the subsequent meta-analysis
Comparison ID MiRNAs Dysregulated direction P value
31857133_Ser miR-193a-3p Down < 0.001
31811079_Ser miR-346 Up 0.0013
miR-345-5p Up 0.0239
miR-122-3p Up 0.0001
miR-1291 Up 0.0052
miR-640 Up 0.0004
miR-650 Up 0.0035
miR-1285-3p Up 0.0032
miR-1299 Up 0.0003
miR-1267 Up 0.0055
miR-208b-3p Down 0.0006
miR-206 Down 0.0004
31849573_Pla miR-132-3p Down 0.0333
miR-212-3p Down 0.001
31809862_WB miR-532-5p Up 4.8 × 10E−30
miR-1468-5p Down 6.2 × 10E−12
31766231_Pla miR-206 Up < 0.025
31691877_Ser miR-22-5p Up ≤ 0.005
miR-23a-3p Up ≤ 0.05
miR-29a-3p Up ≤ 0.05
miR-125b-5p Up ≤ 0.005
31592314_Pla miR-451a Down < 0.0005
miR-21-5p Down < 0.005
miR-23a-3p Down < 0.005
let-7i-5p Down < 0.05
miR-126-3p Down < 0.005
miR-151a-3p Down < 0.05
31572518_Ser miR-133b Down < 0.001
31420923_Pla miR-103a-3p Down < 0.001
miR-107 Down < 0.001
31092279_Pla miR-92a-3p Up 0.0442
miR-181c-5p Up 0.0024
miR-210-3p Up 0.0006
30914454_Pla miR-101-3p Down < 0.001
miR-153-3p Down < 0.001
miR-144-3p Down < 0.001
miR-381-3p Down < 0.001
miR-383-5p Down < 0.001
30328325_Pla miR-128-3p Up < 0.05
29966198_Pla miR-146a-5p Up < 0.05
miR-933 Up < 0.05
29746584_Ser miR-103a-3p Up < 0.05
miR-142-3p Up < 0.05
miR-20a-5p Up < 0.05
miR-29b-3p Up < 0.05
let-7b-5p Up < 0.05
let-7 g-5p Up < 0.05
miR-106a-5p Up < 0.05
miR-106b-5p Up < 0.05
miR-18b-5p Up < 0.05
miR-223-3p Up < 0.05
miR-26a-5p Up < 0.05
miR-26b-5p Up < 0.05
miR-301a-3p Up < 0.05
miR-30b-5p Up < 0.05
miR-132-3p Down < 0.05
miR-146a-5p Down < 0.05
miR-15a-5p Down < 0.05
miR-22-3p Down < 0.05
miR-320a-3p Down < 0.05
miR-320b Down < 0.05
miR-92a-3p Down < 0.05
miR-1246 Down < 0.05
29635818_Pla miR-1908-5p Up < 0.05
29606187_Ser miR-135a-5p Up < 0.05
miR-193b-3p Down < 0.01
miR-384 Up < 0.05
29036829_WB miR-144-5p Down 0.03
miR-374a-5p Down 0.034
miR-221-3p Down 0.042
29036818_Ser_Mild miR-106b-3p Up < 0.001
miR-26a-5p Down < 0.001
miR-181c-3p Down < 0.001
miR-126-5p Down < 0.001
miR-22-3p Down < 0.001
miR-148b-5p Down < 0.001
29036818_Ser_Mod miR-106b-3p Up < 0.001
miR-1246 Up < 0.001
miR-26a-5p Down < 0.001
miR-181c-3p Down < 0.001
miR-126-5p Down < 0.001
miR-22-3p Down < 0.001
miR-148b-5p Down < 0.001
29036818_Ser_Sev miR-106b-3p Up < 0.001
miR-1246 Up < 0.001
miR-660-5p Up < 0.001
miR-26a-5p Down 0.007
miR-181c-3p Down < 0.001
miR-126-5p Down < 0.001
miR-22-3p Down < 0.001
miR-148b-5p Down < 0.001
28934394_Ser miR-455-3p Up 0.007
28849039_Ser_Mil miR-222-3p Down < 0.05
28849039_Ser_Mod miR-222-3p Down < 0.05
28626163_Ser miR-146a-5p Up < 0.05
miR-106b-3p Up < 0.05
miR-195-5p Up < 0.05
miR-20b-5p Up < 0.05
miR-497-5p Up < 0.05
miR-29c-3p Down < 0.05
miR-93-5p Down < 0.05
miR-19b-3p Down < 0.05
miR-125b-3p Down < 0.05
28179587_Pla_AD2 miR-486-5p Up < 0.001
miR-483-5p Up < 0.0001
miR-502-3p Up < 0.0001
miR-200a-3p Up < 0.01
miR-151a-5p Down < 0.001
miR-30b-5p Down < 0.01
miR-103a-3p Down < 0.01
28179587_Pla_MCI-AD2 miR-486-5p Up < 0.001
miR-483-5p Up < 0.001
miR-502-3p Up < 0.01
miR-200a-3p Up < 0.05
28137310_Ser miR-501-3p Down 0.002
27545218_Ser miR-613 Up < 0.01
27501295_WB miR-9-5p Down 0.001
miR-106a-5p Down 0.001
miR-106b-5p Down 0.008
miR-107 Down 0.001
27446280_WB miR-135b-5p Down < 0.01
27277332_WB miR-206 Up < 0.001
27239545_PBMC miR-27b-3p Up < 0.05
miR-128-3p Up < 0.05
miR-155-5p Up < 0.05
27027823_Ser miR-125b-5p Down < 0.001
miR-223-3p Down < 0.001
miR-29 Down < 0.01
miR-519 Up < 0.001
26973465_Pla miR-10b-5p Down 0.022
miR-29a-3p Down 0.041
miR-130b-3p Down 0.002
26497032_PBMC miR-425-5p Up < 0.001
miR-339-5p Up 0.003
miR-639 Down 0.04
26078483_Ser miR-31-5p Down < 0.0001
miR-93-5p Down < 0.0001
miR-143-3p Down < 0.0001
miR-146a-5p Down < 0.0001
25955795_WB miR-29c-3p Down 0.0001
25742200_Pla miR-107 Down < 0.001
25667669_Ser miR-210-3p Down < 0.01
25152461_Ser miR-135a-5p Down < 0.05
miR-200b-3p Down < 0.05
24827165_Pla miR-384 Down < 0.05
24827165_Ser miR-384 Down < 0.05
24577456_Ser miR-98-5p Down 2.67 × 10E−4
miR-885-5p Down 2.8 × 10E−4
miR-483-3p Down 1.0 × 10E−4
miR-342-3p Down 9.19 × 10E−16
miR-191-5p Down 1.54 × 10E−9
let-7d-5p Down 1.2 × 10E−6
24550773_Pla miR-34c-5p Up < 0.01
24550773_PBMC miR-34c-5p Up < 0.01
24157723_Pla miR-34a-5p Down < 0.05
miR-146a-5p Down < 0.05
24139697_Ser miR-125b-5p Down < 0.0001
miR-181c-5p Down < 0.0001
miR-9-5p Up 0.0045
24064186_PBMC_Mon miR-128-3p Up < 0.05
24064186_PBMC_Lym miR-128-3p Up < 0.05
23922807_Pla_C2 let-7d-5p Down 0.0001
let-7 g-5p Down 0.001
miR-15b-5p Down 0.001
miR-142-3p Down 0.0001
miR-191-5p Down 0.002
miR-545-3p Down 0.03
23895045_WB miR-151a-3p Up < 0.05
let-7d-3p Up < 0.05
miR-5010-3p Up < 0.05
let-7f-5p Down < 0.05
miR-1285-5p Down < 0.05
miR-107 Down < 0.05
miR-103a-3p Down < 0.05
miR-26b-5p Down < 0.05
miR-26a-5p Down < 0.05
miR-532-5p Down < 0.05
23435408_PBMC miR-29b-3p Down 0.002
22155483_Ser miR-137-3p Down < 0.05
miR-181c-5p Down < 0.05
miR-9-5p Down < 0.05
miR-29a-3p Down < 0.05
miR-29b-3p Down < 0.05
19936094_PBMC miR-34a-5p Up < 0.05
miR-181b-5p Up < 0.05
[112]Open in a new tab
Table 3.
Statistically significant DEmiRNAs identified by the meta-analysis
MiRNAs Comparison ID Number of upregulated case in AD Number of
downregulated case in AD Number of control case Weight P value FDR
LogOR 95% CI τ^2 I^2
miR-107 27501295_WB 172 109 25.08% 3.74E−25 1.61E−23 − 10.40 [− 12.36 ,
− 8.43] 0.00 0.00%
25742200_Pla 97 81 25.03%
23895045_WB 94 21 24.82%
31420923_Pla 120 120 25.07%
miR-106b-3p 29036818_Ser_Mild 31 86 24.98% 1.05E−20 1.53E−19 9.38 [7.41
, 11.35] 0.00 0.00%
29036818_Ser_Mod 52 86 25.06%
29036818_Ser_Sev 38 86 25.01%
28626163_Ser 45 40 24.96%
miR-22-3p 29036818_Ser_Mild 31 86 24.98% 1.07E−20 1.53E−19 − 9.38 [−
11.34 , − 7.41] 0.00 0.00%
29036818_Ser_Mod 52 86 25.06%
29036818_Ser_Sev 38 86 25.01%
29746584_Ser 47 38 24.95%
miR-126-5p 29036818_Ser_Mild 31 86 33.28% 2.06E−16 1.48E−15 − 9.53 [−
11.81 , − 7.26] 0.00 0.00%
29036818_Ser_Mod 52 86 33.39%
29036818_Ser_Sev 38 86 33.33%
miR-148b-5p 29036818_Ser_Mild 31 86 33.28% 2.06E−16 1.48E−15 − 9.53 [−
11.81 , − 7.26] 0.00 0.00%
29036818_Ser_Mod 52 86 33.39%
29036818_Ser_Sev 38 86 33.33%
miR-181c-3p 29036818_Ser_Mild 31 86 33.28% 2.06E−16 1.48E−15 − 9.53 [−
11.81 , − 7.26] 0.00 0.00%
29036818_Ser_Mod 52 86 33.39%
29036818_Ser_Sev 38 86 33.33%
miR-128-3p 27239545_PBMC 30 30 25.02% 4.64E−16 2.85E−15 8.19 [6.21 ,
10.17] 0.00 0.00%
24064186_PBMC_Mon 34 37 25.08%
24064186_PBMC_Lym 34 37 25.08%
30328325_Pla 20 20 24.82%
miR-93-5p 26078483_Ser 79 75 50.13% 2.3E−11 1.24E−10 − 9.50 [− 12.28 ,
− 6.71] 0.00 0.00%
28626163_Ser 45 40 49.87%
miR-29c-3p 25955795_WB 30 30 49.88% 1.81E−09 8.65E−09 − 8.56 [− 11.36 ,
− 5.77] 0.00 0.00%
28626163_Ser 45 40 50.12%
miR-132-3p 29746584_Ser 47 38 50.28% 6.67E−09 2.87E−08 − 8.27 [− 11.07
, − 5.48] 0.00 0.00%
31849573_Pla 16 31 49.72%
miR-222-3p 28849039_Ser_Mil 30 30 50.00% 8.09E−09 3.16E−08 − 8.22 [−
11.02 , − 5.43] 0.00 0.00%
28849039_Ser_Mod 30 30 50.00%
miR-34c-5p 24550773_Pla 25 27 50.00% 2.67E−08 9.58E−08 7.94 [5.14 ,
10.74] 0.00 0.00%
24550773_PBMC 25 27 50.00%
let-7d-5p 23922807_Pla_C2 20 17 49.74% 8.9E−06 2.73E−05 − 9.39 [− 13.54
, − 5.25] 4.89 54.62%
24577456_Ser 158 155 50.26%
miR-191-5p 23922807_Pla_C2 20 17 49.74% 8.9E−06 2.73E−05 − 9.39 [−
13.54 , − 5.25] 4.89 54.62%
24577456_Ser 158 155 50.26%
miR-200a-3p 28179587_Pla_AD2 13 9 50.26% 3.37E−05 8.05E−05 6.01 [3.17 ,
8.85] 0.00 0.00%
28179587_Pla_MCI-AD2 8 9 49.74%
miR-483-5p 28179587_Pla_AD2 13 9 50.26% 3.37E−05 8.05E−05 6.01 [3.17 ,
8.85] 0.00 0.00%
28179587_Pla_MCI-AD2 8 9 49.74%
miR-486-5p 28179587_Pla_AD2 13 9 50.26% 3.37E−05 8.05E−05 6.01 [3.17 ,
8.85] 0.00 0.00%
28179587_Pla_MCI-AD2 8 9 49.74%
miR-502-3p 28179587_Pla_AD2 13 9 50.26% 3.37E−05 8.05E−05 6.01 [3.17 ,
8.85] 0.00 0.00%
28179587_Pla_MCI-AD2 8 9 49.74%
[113]Open in a new tab
For subgroup analysis, among 54 comparisons, 7, 17, 23, and 7
comparisons investigated the DEmiRNAs in whole blood, plasma, serum,
and PBMC, respectively. The statistically significant DEmiRNAs from the
subgroup meta-analysis are shown in Table [114]4. MiR-107 was
consistently found in two subgroups, whole blood and plasma. Two
miRNAs, miR-103a-3p in plasma and miR-181c-5p in serum, were found
statistically significant in the subgroup analysis, but not in the
meta-analysis. MiR-103a-3p is recently reported to be related to AD
progression via regulating NPAS3 expression [[115]96], while low level
of miR-181c-5p in serum is suggested to be an indicator for cerebral
vulnerability in AD [[116]97].
Table 4.
Statistically significant DEmiRNAs identified by the meta-analysis in
subgroup analysis
Blood elements miRNA Comparison ID Number of upregulated case in AD
Number of downregulated case in AD Number of control case Weight P
value FDR LogOR 95% CI τ^2 I^2
Whole blood miR-107 27501295_WB 172 109 50.26% 1.03E−12 2.06E−12 −
10.12 [− 12.91, − 7.34] 0.00 0.00%
23895045_WB 94 21 49.74%
Plasma miR-103a-3p 28179587_Pla_AD2 13 9 49.64% 0.000266 0.00031 − 8.62
[− 13.26, − 3.99] 7.09 63.36%
31420923_Pla 120 120 50.36%
miR-107 25742200_Pla 97 81 49.96% 5.26E−14 3.68E−13 − 10.67 [− 13.45, −
7.89] 0.00 0.00%
31420923_Pla 120 120 50.04%
miR-200a-3p 28179587_Pla_AD2 13 9 50.26% 3.37E−05 4.72E−05 6.01 [3.17,
8.85] 0.00 0.00%
28179587_Pla_MCI-AD2 8 9 49.74%
miR-483-5p 28179587_Pla_AD2 13 9 50.26% 3.37E−05 4.72E−05 6.01 [3.17,
8.85] 0.00 0.00%
28179587_Pla_MCI-AD2 8 9 49.74%
miR-486-5p 28179587_Pla_AD2 13 9 50.26% 3.37E−05 4.72E−05 6.01 [3.17,
8.85] 0.00 0.00%
28179587_Pla_MCI-AD2 8 9 49.74%
miR-502-3p 28179587_Pla_AD2 13 9 50.26% 3.37E−05 4.72E−05 6.01 [3.17,
8.85] 0.00 0.00%
28179587_Pla_MCI-AD2 8 9 49.74%
Serum miR-106b-3p 29036818_Ser_Mild 31 86 24.98% 1.05E−20 9.08E−20 9.38
[7.41, 11.35] 0.00 0.00%
29036818_Ser_Mod 52 86 25.06%
29036818_Ser_Sev 38 86 25.01%
28626163_Ser 45 40 24.96%
miR-126-5p 29036818_Ser_Mild 31 86 33.28% 2.06E−16 7.01E−16 − 9.53 [−
11.81, − 7.26] 0.00 0.00%
29036818_Ser_Mod 52 86 33.39%
29036818_Ser_Sev 38 86 33.33%
miR-148b-5p 29036818_Ser_Mild 31 86 33.28% 2.06E−16 7.01E−16 − 9.53 [−
11.81, − 7.26] 0.00 0.00%
29036818_Ser_Mod 52 86 33.39%
29036818_Ser_Sev 38 86 33.33%
miR-181c-3p 29036818_Ser_Mild 31 86 33.28% 2.06E−16 7.01E−16 − 9.53 [−
11.81, − 7.26] 0.00 0.00%
29036818_Ser_Mod 52 86 33.39%
29036818_Ser_Sev 38 86 33.33%
miR-181c-5p 24139697_Ser 105 155 50.39% 0.00354 0.008598 − 8.28 [−
13.84, − 2.71] 11.96 74.29%
22155483_Ser 7 7 49.61%
miR-22-3p 29036818_Ser_Mild 31 86 24.98% 1.07E−20 9.08E−20 − 9.38 [−
11.34, − 7.41] 0.00 0.00%
29036818_Ser_Mod 52 86 25.06%
29036818_Ser_Sev 38 86 25.01%
29746584_Ser 47 38 24.95%
miR-93-5p 26078483_Ser 79 75 50.13% 2.30E−11 6.53E−11 − 9.50 [− 12.28,
− 6.71] 0.00 0.00%
28626163_Ser 45 40 49.87%
miR-222-3p 28849039_Ser_Mil 30 30 50.00% 8.09E−09 2.08E−08 − 8.22 [−
11.02, − 5.43] 0.00 0.00%
28849039_Ser_Mod 30 30 50.00%
PBMC miR-128-3p 27239545_PBMC 30 30 33.28% 3.94E−13 3.94E−13 8.44
[6.16, 10.72] 0.00 0.00%
24064186_PBMC_Mon 34 37 33.36%
24064186_PBMC_Lym 34 37 33.36%
[117]Open in a new tab
Identification of potential biomarkers by the ABMDA ensemble learning method
A total of 1751 known miRNA–disease associations between 413 miRNAs and
227 diseases were obtained from the “circulation_biomarker_diagnosis”
category in HMDD, including 17 known miRNA–AD associations.
Identification of miRNA–disease associations by meta-analysis increased
the number of positive miRNA–AD associations by 15 for the ABMDA
identification after removing duplicates. The eleventh miRNA from the
ABMDA results was determined to be statistically insignificant in the
meta-analysis. Therefore, the first 10 miRNAs with prediction scores
ranging from 9.45 to 7.88 were collected (Table [118]5; Additional file
[119]2). Most of these 10 miRNAs have been associated with multiple
types of cancer as diagnostic or prognostic biomarkers
[[120]98–[121]104]. Only one miRNA, miR-155, has been reported as a
biomarker in AD for mediating neuroinflammation [[122]105].
Table 5.
AD-related DEmiRNAs identified by ABMDA
Disease miRNAs Mature miRNAs Score Used as biomarkers in diseases
Alzheimer disease miR-339 miR-339-5p 9.45 Lung cancer
miR-128-2 8.92 Hepatocellular carcinoma
miR-203 miR-203a-3p 8.76
miR-495 miR-495-3p 8.75 Non-small cell lung cancer
miR-155 miR-155-5p 8.70 AD
let-7a-2 8.67 Lung cancer
miR-103a-2 8.10
miR-16-2 8.02 Breast cancer
let-7b let-7b-5p 7.93 Non-small cell lung cancer
miR-625 miR-625-5p 7.88 Malignant pleural mesothelioma
[123]Open in a new tab
MiR-339-5p is upregulated to alleviate neuroinflammation by inhibiting
HMGB1 [[124]106]. HMGB1 encodes high mobility group box 1 to produce
proinflammatory cytokines by binding to receptors for advanced
glycation end products (RAGE) [[125]107] and inhibiting IKK-β and
IKK-γ, which are key elements of NF-κB signaling [[126]108]. NF-κB
signaling can also be modulated by the identified DEmiRNAs through
multiple mechanisms. Both miR-203a-3p and let-7b-5p target IGF1R, which
encodes insulin-like growth factor 1 receptor, to alleviate tumor
necrosis factor (TNF)-induced activation of NF-κB [[127]109, [128]110].
MiR-155 and miR-625-5p decrease the expression of SHIP and AKT2, which
encode Src homology 2-containing inositol phosphatase and RAC-beta
serine/threonine-protein kinase, respectively, to attenuate
NF-κB-dependent inflammation [[129]111, [130]112]. Additionally,
miR-495 targets NOD1, which encodes nucleotide-binding oligomerization
domain-containing protein 1, to reduce high glucose-induced
inflammation in diabetic complications [[131]113], whereas its mature
form, miR-495-3p, has recently been reported to regulate inflammatory
molecules by targeting IL5RA [[132]114]. The mature forms of miR-128-2,
let-7a-2, miR-103a-2, and miR-16-2 have not been reported in the
miRBase database at the time of this study; however, their family
members exhibit inflammatory properties. MiR-128 and let-7a regulate
gene expression in response to oxidative stress [[133]115, [134]116].
MiR-16 and miR-103-3p target ADORA2A and SNRK, which encode the
adenosine A2a receptor and sucrose non-fermentable-related
serine/threonine-protein kinase, respectively, to attenuate
NF-κB-dependent inflammation [[135]117, [136]118]. Current literature
suggests that NF-κB might be the main downstream effector for the
DEmiRNAs identified by ABMDA in the present study.
Enrichment analysis
There were 3496 and 2938 target genes in total for 18 and 10 DEmiRNAs
from the meta-analysis and ABMDA results, respectively (Additional file
[137]3). These target genes were subjected to two independent
enrichment analyses based on KEGG to obtain the functional annotations
of the DEmiRNAs (Fig. [138]3A, B). Most pathways targeted by the
meta-analysis and ABMDA DEmiRNAs were commonly found in the two
independent enrichment analyses, even though the DEmiRNAs in the two
categories were not identical. This indicated that multiple DEmiRNAs
target either common or different mRNA transcripts that functionally
converge on the same pathways. Most dysregulated pathways in the
enrichment analysis were involved in AD development via modulation of
neuroinflammation, including the AGE-RAGE signaling pathway in diabetic
complications, cell cycle, cellular senescence, Hippo signaling
pathway, and FoxO signaling pathways. In addition, multiple cancerous
pathways were also implicated. Compared with the two independent
enrichment analyses, the synergic enrichment analysis unifying the two
categories of DEmiRNAs might provide better insight for AD development.
In the synergic enrichment analysis, the dysregulated pathways were
more statistically significant (Fig. [139]3C). Also, the pathways that
were found in only one independent enrichment analysis (such as the
Hippo signaling pathway from the meta-analysis and cell cycle from
ABMDA) were both statistically significant in the synergic enrichment
analysis. The synergic enrichment analysis suggested that the two
DEmiRNA categories interact functionally and complement each other.
Fig. 3.
[140]Fig. 3
[141]Open in a new tab
Biological pathway enrichment analysis of the target genes of DEmiRNAs
identified by A meta-analysis; B ABMDA; and C both the meta-analysis
and ABMDA. The x-axis represents the number of genes in each of the
KEGG pathway, the y-axis represents the name of each KEGG pathway, and
the color represents the FDR-adjusted statistical significance
In the synergic enrichment analysis, the AGE-RAGE signaling pathway in
diabetic complications was identified as the second most statistically
significant. The AGE-RAGE pathway is involved in diabetic microvascular
complications. Elevated levels of AGE and RAGE have also been reported
in AD patients, and increased RAGE activity has been detected in
patients with early AD symptoms [[142]119, [143]120]. RAGE also
interacts with Aβ oligomers to induce BBB leakage [[144]121] and
upregulates NF-κB, which induces neuroinflammation [[145]122]. Elevated
neuroinflammation increases the expression of secretases for Aβ
production [[146]123], reduces Aβ degradation in microglia [[147]124],
and induces the aberrant hyperphosphorylation of tau proteins
[[148]125]. Elevated levels of proinflammatory cytokines and Aβ
increase cell cycle-related kinases, such as PKA, CAMKII, Fyn, and
mTORC1, inducing neuronal cell cycle re-entry (CCR) [[149]126,
[150]127]. Aberrant CCR results in neuronal hyperploidy, which alters
neuronal circuit function and reduces synaptic activity [[151]128,
[152]129], ultimately inducing neuronal death [[153]130]. Aberrant CCR
is also induced by malfunction of PI3K/AKT/mTOR, a cell survival
pathway disrupted in both AD and cancer, though in opposite directions
[[154]131]. Further, aberrant CCR is a causative factor for the
majority of neuronal death in early AD development and might be a
potential biological mechanism to link AD and multiple cancerous
diseases. Aberrant Aβ accumulation and proinflammatory cytokines from
dying neurons further enhance neuroinflammation and oxidative stress,
inducing cellular senescence and dysregulating the Hippo and FoxO
signaling pathways. Cellular senescence is a permanent state of
cellular rest that is involved in the onset of AD [[155]132], and
neuroinflammation and Aβ-mediated toxicity have been reported to
upregulate senescence-regulated genes [[156]133–[157]135]. The Hippo
signaling pathway is a kinase cascade relevant for cellular
homeostasis, and is upregulated by Aβ-mediated neurotoxicity to enhance
neurodegeneration with JNK in AD [[158]136, [159]137]. The FoxO
signaling pathway is involved in the relationship between ROS, insulin
resistance, and AD pathology [[160]138, [161]139]. Under persistent
oxidative stress, the FoxO signaling pathway increases the
transcription of apoptotic proteins [[162]140].
Network
In total, 5222 target genes were identified for the DEmiRNAs from the
meta-analysis and ABMDA, and 1865 target genes with semantic similarity
over 0.95 were identified as common target genes in the two DEmiRNA
categories, suggesting an overlap of the two categories in biological
functions. The common target genes were used to retrieve the
corresponding protein–protein interactions according to STRING and
construct a network. The network comprised 1865 common target genes as
nodes, and 18750 edges among the common target genes. The DEmiRNAs
let-7b-5p and miR-155 identified by ABMDA shared the most common target
genes with the DEmiRNAs miR-93-5p and miR-128-3p identified by the
meta-analysis. The common target genes UBC, UBB, and RPS27A, which are
core members in the ubiquitin-proteasome system (UPS), exhibited the
highest connection degree in the network.
The UPS is imperative not just in Aβ clearance [[163]141], but also in
neuroinflammation and neuronal CCR. The physiological function of the
UPS can be adversely influenced by neuroinflammation. Under
neuroinflammation, J2 prostaglandins are generated from prostaglandin
D2, which is the most abundant prostaglandin in the brain [[164]142].
J2 prostaglandins enhance the expression levels of COX2 to transition
acute neuroinflammation to chronic neuroinflammation and oxidize the
UPS units to promote disassembly [[165]143]. The impaired UPS induces
ectopic expression of cell cycle-related genes and causes neuronal CCR,
as the metabolisms of cyclin and cyclin-dependent kinases are dependent
on the UPS. A recent study [[166]144] has reported that the
dysregulation of an E3 ubiquitin ligase, Itch, induces neuronal CCR in
response to Aβ. Aβ-induced JNK activation phosphorylates Itch to
promote the degradation of TAp73, which is important for protein
synthesis under oxidative stress, in neurons [[167]145].
Quality assessment of studies
The MIQE guideline was used to access the expression profiling analysis
of the included studies. The results of the quality assessments were
shown in Fig. [168]4. Among the 47 expression profiling studies, miR-39
and U6 RNAs were frequently used as internal normalization controls for
qRT-PCR. Around 85% of the included studies provided sufficient
information about data processing, including statistical analysis and
quantification methods. About 70% of the included studies provided
sufficient information about sample annotation, but 16 studies did not
provide details of the storage or extraction methods of serum or
plasma. Approximately 60% of the included studies did not provide the
number of replicates in the experimental design, and 77% did not
provide the quantification cycle value in the actual data processing.
The parameters of qRT-PCR methods were missing in 14 studies. For the
annotation of PCR, most studies provided the full details of reference
miRNAs for quantification, but primer information was missing in 19
studies.
Fig. 4.
[169]Fig. 4
[170]Open in a new tab
Overall quality assessment of the miRNA differential expression
profiling approach for 47 included studies. Green color represents a
low risk of bias, in which the authors clearly provided full details of
the methods. Yellow color represents an unclear risk of bias, in which
the authors provided methods without full details
Discussion
There is a need for blood circulating biomarkers that can be mass
screened accurately and conveniently to identify high risk individuals
of AD. The identification of DEmiRNAs as biomarkers from differential
miRNA expression studies has been successful in cancers [[171]146] and
is thought to have potential for AD. DEmiRNAs associated with AD
pathology, such as Aβ production and neuroinflammation, are potentially
important biomarkers because the presence of Aβ and proinflammatory
cytokines are considered to be key factors for predicting whether
patients with mild cognitive impairment are progressing to AD
[[172]147–[173]149]. In this study, we used a meta-analysis approach
based on differential miRNA expression studies from blood to identify
reliable miRNA–AD associations. The associations were subsequently used
for the prediction of potential AD biomarkers using the ABMDA ensemble
learning method. We identified 28 DEmiRNAs (18 and 10 from
meta-analysis and ABMDA, respectively) as potential AD biomarkers in
blood.
The DEmiRNAs identified with the meta-analysis involved in Aβ
metabolism, including APP expression, Aβ-production enzyme regulation,
and Aβ clearance, tau protein phosphorylation, and also contribute to
neuronal function during AD progression, including pathogenic
neuroinflammation, apoptosis, mitochondrial oxygen chain activity, and
neuronal microtubule maintenance. The meta-analytical results of
DEmiRNAs mediate Aβ synthesis via several targets, and some of them are
also involved in tau protein phosphorylation and neuronal functions.
MiR-107 is negatively correlated with BACE1 and ADAM10 expression and
is downregulated in the early stage of AD [[174]150, [175]151]. The
downregulation of miR-107 also dysregulates the expression of CDK5R1,
which is involved in neuronal survival [[176]94]. Downregulation of
miR-181c dysregulates the expression of SPTLC1 [[177]152], to increase
Aβ deposition, and increases pro-inflammatory cytokines [[178]153].
MiR-22-3p and miR-29c also regulate Aβ deposition via targeting MAPK14
and BACE1, respectively [[179]154, [180]155]. Besides Aβ synthesis, Aβ
clearance is also interfered by the meta-analytical results of
DEmiRNAs. MiR-128 and miR-93 are reported to be involved in Aβ
phagocytosis and UPS for Aβ clearance by targeting cathepsin and
NEDD4L, respectively [[181]87, [182]156]. For Aβ-induced toxicity,
upregulation of miR-34c enhances Aβ-induced synaptic failure to
suppress the memory formation by targeting SIRT1 and VAMP2 [[183]157,
[184]158]. MiR-200a-3p coregulates BACE1 and PRKACB to protect neurons
against Aβ-induced toxicity and tau protein hyperphosphorylation
[[185]159], while upregulation of miR-200a-3p also interferes the
function of mitochondrial oxidative chain [[186]69]. The
meta-analytical results of DEmiRNAs were also involved in the tau
protein phosphorylation. MiR-132 is consistently downregulated in
different brain area, and negatively correlated with Braak stage,
suggesting that it has an important role in cognitive capacity and
correlated with tau protein phosphorylation [[187]160, [188]161]. The
downregulation of miR-132 is reported to increase the expression of
ITPKB and SIRT1 for tau pathology and Aβ generation, respectively
[[189]162, [190]163]. Meanwhile, miR-132 is also involved in caspase
3-dependent apoptosis [[191]164]. MiR-483-5p and miR-106b target MAPT
and FYN for tau protein synthesis and phosphorylation, respectively
[[192]69, [193]165]. For neuronal function, miR-222 targets CDKN1B to
influence cell cycle and apoptosis [[194]68]; miR-191 targets TMOD2 and
REST to regulate axonal guidance and dendritic growth [[195]166]; and
both miR-486-5p and miR-502-3p are the regulators of dynactin for
neuronal function [[196]69]. The potential mechanisms of miR-126-5p,
miR-148-5p, and let-7d in AD are not well known. MiR-126-5p targets
JNK-interacting protein 2 to mediate inflammatory response in
infectious conditions [[197]167]. The expression of miR-148-5p is
reported to be positively correlated with MMSE score [[198]66], and
let-7d is reported to be involved in neuronal cell cycle by regulating
enzymatic signaling [[199]168]. Compared with other let-7 family
members, let-7d does not significantly trigger the release of TNF-α
from microglia [[200]169].
DEmiRNAs from ABMDA also relate to neuroinflammation mainly via
modulation of NF-κB. The DEmiRNAs identified in the present study are
compatible with the first two differential miRNA expression studies in
AD, which reported that the DEmiRNAs are associated with dysregulated
inflammation [[201]23, [202]170]. Independent enrichment analysis in
the present study indicated disrupted signaling of AGE-RAGE, cellular
senescence, Hippo, FoxO, and cell cycles, and these dysregulated
pathways were more statistically significant in the synergic enrichment
analysis. The dysregulated pathways were associated with
neuroinflammation, in which neuroinflammation increases the production
of Aβ by modulation of secretase, suggesting a biological significance
of the 28 DEmiRNAs in AD development. The similarity of biological
functions of the two DEmiRNA categories fits the ABMDA assumption that
the miRNAs associated with the same disease should be functionally
related. In the network, 1865 target genes were commonly found from the
two DEmiRNA categories, and the highly connected target genes involved
UBC, UBB, and RPS27A, which are involved in the neuroinflammatory
process and CCR through mediating the UPS.
Comparison of DEmiRNA results from the meta-analysis and ABMDA revealed
that the cell cycle pathway was identified as significant only with
ABMDA. The majority of neuronal death in AD is due to the dysregulated
cell cycle, in which the differentiated neurons in AD-affected brain
regions re-enter the cell cycle in the presymptomatic disease
[[203]171, [204]172]. The ectopic expression of developmentally
regulated genes in AD links AD pathophysiology with aberrant CCR and
correlates with cognitive decline [[205]173]. CCR markers are expressed
in Aβ-cultured neurons within hours, suggesting CCR is an initial event
in response to Aβ [[206]174]. These findings indicate that neuronal CCR
induces rapid neuronal loss after exposure to Aβ. Neuronal CCR in AD
results from Aβ-induced activation of multiple protein kinases at the
plasma membrane, and tau protein phosphorylation by these proteins. The
absence of β-secretases, or blockage of the Aβ receptor, inhibits CCR
[[207]175, [208]176]. Aβ incorporates into the lipid rafts of neuronal
membranes by Fyn-dependent kinase [[209]177] and disturbs the structure
of lipid rafts to activate PKA, CAMKII, Fyn, and mTORC1 to
phosphorylate tau proteins at S409, S416, Y18, and S262, respectively
[[210]126, [211]127]. The phosphorylated tau proteins subsequently
modify mTORC1 activity to induce CCR. Aβ also induces a rapid loss of
the insulin receptor in the brain and impairs insulin receptor
autophosphorylation to reduce brain insulin signaling [[212]178],
decreasing the inhibitory effects of mTORC1 at lysosomes in Aβ-induced
CCR [[213]179, [214]180]. CCR can also be initiated by JNK, which is
activated when Aβ binds to receptors, such as RAGE, or by Aβ-induced
TNF [[215]144, [216]181]. Once CCR is initiated, neurons do not
complete the cell cycle [[217]171, [218]174]. The hyperploidy of
neurons results in hypertrophy of neuronal cell bodies in AD-affected
regions [[219]182, [220]183]. The number of hyperploid neurons is
higher in preclinical AD individuals than in healthy individuals, and
initially increases followed by a gradual decrease in AD development
[[221]171]. The hyperploidy gradually decreases the synaptic inputs,
which correlates with reduced activity of PSD-95 [[222]129], a scaffold
protein for glutamatergic function, suggesting that CCR-induced
hyperploidy results in synaptic dysfunction. Further, loss of
chromosomal homeostasis induces neuronal death [[223]184], probably via
neurotrophins and activation of FOXO1. Neurotrophins, such as nerve
growth factor (NGF) and brain-derived neurotrophic factor (BDNF),
elicit prosurvival functions, whereas their precursors, proNGF and
proBDNF, elicit apoptotic functions. The levels of neurotrophins and
their precursors are downregulated and upregulated in AD, respectively
[[224]185, [225]186], shifting neurotrophic prosurvival functions to
apoptotic functions. Additionally, CCR induces dysregulation of cyclin
metabolism to activate the transcription factor, FOXO1. The cyclin
B-CDK1 complex, which is upregulated in AD, phosphorylates FOXO1
[[226]174] to induce the expression of apoptotic genes [[227]187].
Thus, CCR is a key mechanism to functionally connect Aβ and tau
proteins in the early phase of AD development and connects the
seemingly unrelated pathologies between AD and cancer, and between AD
and insulin signaling impairment [[228]188]. This convergence of
pathology points to the importance of CCR in AD development and
significance of miRNAs involved in CCR. Figure [229]5 summarizes the
mechanism of Aβ-mediated CCR in AD, as described above.
Fig. 5.
[230]Fig. 5
[231]Open in a new tab
Mechanism of action of neuronal CCR in AD
Although it remains unclear whether neuroinflammation is the primary
cause of AD development or a secondary event to other primary
pathologies, neuroinflammation is an imperative and early event for
Aβ-mediated toxicity and has been suggested to contribute as much as Aβ
in AD pathology [[232]189]. The level of neuroinflammation assessed by
microglial activation has been correlated with worse cognitive decline
in a PEG imaging study of AD participants [[233]190]. Neuroinflammation
is progressively increased during AD development, as indicated by
elevated levels of proinflammatory cytokines [[234]191].
Proinflammatory cytokines have been identified around amyloid plaques
[[235]192], suggesting a role of neuroinflammation in Aβ deposition.
Neuroinflammation impairs the microglial Aβ degradation ability
[[236]193]. Several genome-wide association studies have reported that
immune-related genes are involved in AD pathogenesis
[[237]194–[238]197], such as CD33, CR1, EPHA1, and MS4A6E/MS4A4E, which
regulate the immune system in response to Aβ and activate microglial Aβ
degradation. An epigenome-wide association study [[239]198]
demonstrated hypermethylation of ANK1 in both presymptomatic and AD
patients. ANK1 encodes Ankyrin 1 to maintain the actin cytoskeleton,
and the upregulation of ANK1 in AD microglia has been reported, which
supports the significance of the microglial response in AD development
[[240]199]. Passamonti et al. [[241]200] reported that cognitive
decline is mediated by microglial activation, which is linked to
neuroinflammation. The perpetuation of inflammation influences synaptic
plasticity and induces neuronal damage, resulting in neurodegeneration
[[242]201]. Synaptic loss results primarily from the increased levels
of proinflammatory cytokines and activated microglial cells [[243]202].
However, the importance of neuroinflammation in AD has been questioned
because of the failure of nonsteroidal anti-inflammatory drugs (NSAIDs)
for treating AD. An early AD clinical trial reported the failure of
both cyclooxygenase-nonselective (naproxen) and
cyclooxygenase-selective (celecoxib) NSAIDs in cognitive function
improvement [[244]203]. Another study [[245]204], which involved
long-term follow-up of AD participants who received naproxen or
celecoxib, reported that naproxen reduces AD incidence in asymptomatic
individuals. The trials suggest that the efficacy of NSAIDs is disease
stage dependent and that the choice of NSAID is a determining factor. A
COX nonselective NSAID, tarenflurbil, has shown some positive results
at a high dosage for individuals with mild symptoms in a post hoc
analysis of a phase II clinical trial [[246]205], but no cognitive
improvement was observed with the drug in a phase III clinical trial
[[247]206]. One possible reason for its failure is the low penetration
of the drug from plasma to CSF. A recent study indicated that
intranasal delivery of tarenflurbil can increase the drug concentration
in the brain [[248]207], but no clinical trials have yet been carried
out with this route of delivery.
The miRNA expression profile in blood is representative of
dysregulation in all tissues. The DEmiRNAs could be biased by the
presence of multiple comorbidities, although the extent of how specific
comorbidities influence DEmiRNAs in AD is not fully known. The miRNAs
encapsulated in membrane vesicles can penetrate the BBB [[249]208],
suggesting that DEmiRNAs induced by peripheral complications could also
contribute to AD pathology in the brain. Peripheral inflammation
interferes with immunological processes in the brain through the entry
of activated peripheral immune cells that exacerbate or initiate
neuroinflammation. A growing body of evidence supports that peripheral
inflammation is a driver of AD. For example, obesity and type 2
diabetes mellitus increase the risk of AD development through mediating
neuroinflammation [[250]209], and the gut microbial diversity in
obesity patients is associated with increased levels of proinflammatory
cytokines in blood [[251]210]. The AD pathogenetic factor APOE ε4
allele has been reported to impair macrophage efferocytosis, which
subsequently induces tissue inflammation and increases the circulating
levels of proinflammatory cytokines [[252]211, [253]212]. Elevated
levels of proinflammatory cytokines in the blood of AD patients have
been reported in meta-analyses [[254]213, [255]214], confirming the
presence of peripheral inflammation in AD. Peripheral inflammation is
positively associated with cognitive decline and could detrimentally
impact brain function [[256]215]; specifically, elevated systemic TNF
levels have been associated with increased conversion of mild cognitive
impairment to AD. Systemic TNFs bind to TNF receptors to alter BBB
integrity and allow entry of peripheral immune cells to the brain,
inducing neuroinflammation [[257]216, [258]217]. The entry of
peripheral immune cells disrupts synaptic plasticity to induce
neuroinflammation, increasing the risk of cognitive decline [[259]215,
[260]218]. Neuroinflammation also releases antigens to activate T cells
in CSF. The activated T cells enter the brain and differentiate into
effector T cells, which can produce cytokines [[261]219] or induce
apoptosis [[262]220]. Abnormalities in T cells have been reported in AD
patients compared with controls [[263]221].
Limitations
Current approaches to diagnose AD (e.g., FDG-PET and CSF-based Aβ and
tau protein levels) require sophisticated equipment or lumbar puncture,
which could be avoided with the use of blood-based biomarkers. The
strength of this study is the integration of meta-analysis and the
ABMDA ensemble learning method to identify potential AD biomarkers in
blood. However, there are some limitations. First, most of the included
miRNA expression studies only conducted qRT-PCR as a validation
approach to identify DEmiRNAs, and only 17 of the included studies
reported both screening and validation approaches. This means that the
present identification of DEmiRNAs was dependent on those selected by
researchers in previous studies, and not an objective manner. This
implies that the number of DEmiRNAs in AD is likely larger than the
number of DEmiRNAs identified in the present study. Second, although
most DEmiRNAs from the meta-analysis and ABMDA identification were
associated with neuroinflammation, their function was not
distinguishable between acute and chronic neuroinflammation. Acute
neuroinflammation is beneficial for microglial clearance of Aβ, whereas
chronic neuroinflammation results in neurodegeneration by mediating
functional and structural damage to neurons [[264]222, [265]223].
Third, there were 9 DEmiRNAs from the meta-analysis extracted from the
independent comparisons of only one study, suggesting that the
meta-analytical results might be biased by the unidentified correlation
within the recruited population. Forth, disease stages of AD patients
were not fully and consistently provided in most included differential
miRNA expression studies, resulting in the performance of subgroup
analysis based on disease stage was not currently applicable. Also, the
results of original studies might be influenced by the lack of
adjustment for the disease stages. Fifth, all the included differential
miRNA expression studies were non-longitudinal studies, suggesting that
the miRNAs extracted from these studies were not repeated observations
over periods of time.
Conclusion
This study identified 28 DEmiRNAs as potential AD biomarkers in blood
by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs
identified by both meta-analysis and ABMDA were related to
neuroinflammation, and those identified solely by ABMDA were related to
neuronal CCR.
Supplementary Information
[266]13195_2021_862_MOESM1_ESM.xlsx^ (18KB, xlsx)
Additional file 1. The meta-analysis results of DEmiRNAs.
[267]Additional file 2. The ABMDA results.^ (3.6MB, xlsx)
[268]13195_2021_862_MOESM3_ESM.xlsx^ (146.3KB, xlsx)
Additional file 3. The target genes of the DEmiRNAs identified by
meta-analysis and ABMDA.
Acknowledgements