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Article

Co-Expression Analysis of microRNAs and Proteins in Brain of Alzheimer’s Disease Patients

1
Institute of Inflammation and Ageing, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
2
Surgical Reconstruction and Microbiology Research Centre, National Institute for Health Research, Queen Elizabeth Hospital, Birmingham B15 2TH, UK
3
Department of Emergency Medicine, American University of Beirut Medical Center, Beirut 1107 2020, Lebanon
4
Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98165 Messina, Italy
5
Program for Neurotrauma, Neuroproteomics & Biomarkers Research, Departments of Emergency Medicine, Psychiatry, Neuroscience and Chemistry, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Cells 2022, 11(1), 163; https://doi.org/10.3390/cells11010163
Received: 26 November 2021 / Revised: 29 December 2021 / Accepted: 1 January 2022 / Published: 4 January 2022

Abstract

:
Alzheimer’s disease (AD) is the most common form of dementia globally; however, the aetiology of AD remains elusive hindering the development of effective therapeutics. MicroRNAs (miRNAs) are regulators of gene expression and have been of growing interest in recent studies in many pathologies including AD not only for their use as biomarkers but also for their implications in the therapeutic field. In this study, miRNA and protein profiles were obtained from brain tissues of different stage (Braak III-IV and Braak V-VI) of AD patients and compared to matched controls. The aim of the study was to identify in the late stage of AD, the key dysregulated pathways that may contribute to pathogenesis and then to evaluate whether any of these pathways could be detected in the early phase of AD, opening new opportunity for early treatment that could stop or delay the pathology. Six common pathways were found regulated by miRNAs and proteins in the late stage of AD, with one of them (Rap1 signalling) activated since the early phase. MiRNAs and proteins were also compared to explore an inverse trend of expression which could lead to the identification of new therapeutic targets. These results suggest that specific miRNA changes could represent molecular fingerprint of neurodegenerative processes and potential therapeutic targets for early intervention.

1. Introduction

Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia, leading to severe disability and early death, and with devastating consequences for families, health-care systems, and society [1]. It affects approximately between 40 and 50 million globally, but the number of cases is expected to triple by 2050 owing to population growth and ageing [2,3]. Most AD cases are considered sporadic, and the cause is unclear; however, a notable risk factor is carrying the APOE4 allele of the Apolipoprotein E gene [4]. Fewer than 1% of AD cases are known to be familial, caused by inheritance of rare autosomal dominant mutations [5] of amyloid precursor protein (APP) gene, presenilin 1 (PSEN1), and presenilin 2 (PSEN2), where PSEN1 and PSEN2 are involved in the processing of APP to amyloid-beta (Aβ) [6].
Diagnosis of Alzheimer’s disease is based on a history of cognitive decline combined with neurological and laboratory analysis, including the measurement of cerebrospinal fluid (CSF) biomarkers of Aβ and tau tangles and neuroimaging scans to identify brain atrophy and anatomical changes [7]. The histological hallmarks of AD pathology are Aβ-plaques and tau tangles accompanied by neuron loss in the brain. The pathology follows a specific pattern of brain regions in which plaques and tangles are found and mapped according to Braak staging [8].
Although the FDA recently approved the first Alzheimer’s medication clearing out amyloid plaques, this intervention is still controversial, and to date, no treatment has shown a clear/undoubted patient clinical benefit. This may be a consequence of the late-stage diagnosis of patients as well as the fact that the numerous studied compounds are unable to modify the disease process [9]. Therefore, there is an urgent need to discover early mechanistic biomarkers/endpoints as well as new therapeutic targets.
MicroRNAs (miRNAs, miRs), a small non-coding RNA class of post-transcriptional regulators, are particularly attractive molecules due to their ability to regulate multiple genes. Technology has now been developed to isolate, quantify, and profile miRNAs in diseases and is being combined with approaches to deliver miRNA therapeutics effectively without an immunogenic response [10,11,12,13,14,15,16,17]. Whilst these developments are promising, there are currently no miRNA therapeutics available; however, there is a remarkable effort in place, with many entering clinical trials with the earliest implication for cancer [18]. Consequently, miRNA as a therapy presents a promising solution for multifactorial diseases, such as AD, which do not yet have successful disease-modifying treatments; however, further research is required to elucidate the potential role of miRNA in this complex pathology. Previous studies in AD patients have highlighted changes in the expression of miRNAs that target AD-related mRNA and proteins. Among them, miR-101 and miR-16 have been shown to target APP mRNA [19,20]. MiR-124 was downregulated in the AD model SAMP8 mice, leading to enhanced expression of the splicing regulator PTBP1 and aberrant APP splicing [21]. Several miRNAs are involved in the amyloid cascade through the regulation of beta-secretase 1 (BACE1), including miR-107, miR-29a/b-1, miR-29c, miR-188-3p, miR-339-5p, miR-195, miR-186, and miR-124 in animal and human studies [22,23,24,25,26,27,28]. Other miRNAs that target the catalytic subunits of γ-secretase have also been shown to be deregulated in AD. Furthermore, Tau protein, the main constituent of neurofibrillary tangles, has been shown to be regulated by miR-132 and miR-34a [29].
In this study, we present an integrated analysis of miRNA and protein expression from the brains of AD patients. The primary outcome was to elucidate the pathological mechanism that can lead to neuronal death in confirmed and late AD cases. This was reached through the expression analyses of miRNAs and proteins and their related pathways in controls and AD (Braak V-VI stage) patients. The secondary outcome had the aim to investigate whether any of these pathological mechanisms or pathways detected in the late stage of AD were also present at the early stage of AD (Braak III-IV) and consequently whether any of the molecules differentially expressed can be used as early indicators of the pathology or as potential targets to stop or delay the progress of the pathology.

2. Materials and Methods

2.1. Sample Population

Post-mortem temporal cortex brain tissues were used in this study, which had been taken within 72 h of death and obtained from the South-West Dementia Brain Bank (SWDBB, Bristol, UK) and stored at −80 °C. Samples were stratified by Braak tangle stages I-VI. Stages I and II were used as the control group (n = 5), stages III-IV were used for the early stage of AD group (n = 5), and stages V and VI were used for the late-stage of AD group (n = 5). The groups were sex- and age-matched to remove these variables as a potential confounding factor.

2.2. MicroRNA Analysis

2.2.1. RNA Extraction and Analysis

Temporal cortex samples were homogenised for 30 s using a TissueRuptor II (Qiagen, Hilden, Germany) in RNase-free PBS (ThermoFisher Scientific, Waltham, MA, USA). RNA was extracted from the homogenised samples using the miRNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The RNA concentration was measured on a nanophotometer (IMPLEN, Westlake Village, CA, USA).
An Agilent 2100 Bioanalyzer (Santa Clara, CA, USA) was used to detect the size distribution of total RNA as well as determine the quality of the RNA. A total of 100 ng of RNA was extracted and converted to cDNA using the miRCURY LNA kit (Qiagen, Hilden, Germany). cDNA was mixed with a SYBR master-mix (miRCURY SYBR Green Master Mix; Qiagen, Hilden, Germany). This was added to the miRCURY LNA miRNA miRNome Panel I–II using the automated pipettor the QIAgility (Qiagen, Hilden, Germany). These plates were run on the Quantstudio 5 qPCR machine (ThermoFisher Scientific, Waltham, MA, USA) as instructed by protocol provided with the panels. In total 751miRNAs (miRCURY LNA miRNA miRNome PCR Panels, https://geneglobe.qiagen.com/us/product-groups/mircury-lna-mirna-mirnome-pcr-panels, accessed on 6 October 2019 were tested using the miRNome plates for each of the samples. Overall, 705 miRNAs showed Cq values, and fold changes were calculated using the ΔΔCq method. hsa-miR-99a-5p and hsa-miR-361-5p were selected as the most stable pair of miRNAs across the samples (stability value = 0.05) by NormFinder software (https://moma.dk/normfinder-software, accessed on 1 December 2019 and therefore used as the housekeeping genes.

2.2.2. Statistical Analysis

Fold change values were tested for normality and found to not be normally distributed using the Shapiro–Wilk test. Mann–Whitney U tests was used to test differences in miRNA expression between two groups. To check for variability across different groups, a heat map analysis was performed.

2.2.3. MiRNA Targets and Pathways Analysis

Diana tools mirPath v.3 (http://snf-515788.vm.okeanos.grnet.gr/, accessed on 20 June 2021) was used for the downstream analysis. The DIANA-microT-CDS algorithm with a set threshold of 0.8 was used to select mRNA targets on the gene union tool. The final p-value was corrected with FDR. A p-value of less than 0.05 was considered significant. KEGG (Kyoto Encyclopedia of Genes and Genomes, Kyoto, Japan) pathways analysis was also performed.

2.3. Protein Analysis

2.3.1. Protein Extraction and Analysis

Proteins were extracted with scioExtract buffer (Sciomics, Neckargemünd, Germany) using the extraction SOPs. The bulk protein concentration was determined by BCA assay. The reference samples and the samples were labelled for two hours with scioDye 1 and scioDye 2, respectively. After that the reaction was stopped, the buffer exchanged to PBS and samples stored at −20 °C until use.
Samples were analysed in a dual-colour approach using a reference-based design on scioDiscover antibody microarrays (Sciomics, Neckargemünd, Germany) targeting 1351 different proteins with 1821 antibodies in four replicates. The arrays were blocked with scioBlock (Sciomics, Neckargemünd, Germany) on a Hybstation 4800 (Tecan, Männedorf, Switzerland), and afterwards the samples were incubated competitively with the reference sample using a dual-colour approach. After incubation for three hours, the slides were thoroughly washed with 1× PBSTT, rinsed with 0.1× PBS and water, and finally dried with nitrogen.

2.3.2. Data Acquisition and Statistical Analysis

Slide scanning was conducted using a Powerscanner (Tecan, Männedorf, Switzerland) with constant instrument power and PMT settings. Spot segmentation was performed with GenePix Pro 6.0 (Molecular Devices, Union City, CA, USA). Data were analysed using the linear models for microarray data (LIMMA) package of R-Bioconductor. For the normalisation, a specialised invariant Lowess method was applied. For analysis of the samples, a one-factorial linear model was fitted with LIMMA resulting in a two-sided t-test or F-test based on moderated statistics. All presented p-values were adjusted for multiple testing by controlling the false discovery rate according to Benjamini and Hochberg. Proteins were defined as deferential for ׀logFCj׀ > 0.5 and an adjusted p-value < 0.05. Proteins differentially expressed between groups are presented as log-fold changes (logFC) calculated for the basis 2. When samples versus controls are compared, a logFC = 1 means that the sample group had on average a 21 = 2-fold higher signal than the control group. logFC = −1 stands for 2−1 = 1/2 of the signal in the sample as compared to the control group.

2.3.3. Pathway Analysis

Proteins were defined as significantly differential for |log2FC| ≥ 0.5 and a simultaneous adjusted p-value ≤ 0.05. All differential proteins were subjected to STRING (search tool for the retrieval of interacting genes/proteins) analysis for the visualization of protein networks, pathway analysis (based on KEGG and Reactome databases), as well as biological function classification analysis (based on the gene ontology (GO) database). KEGG pathways only are shown in this paper.

2.4. Pathway Studio

As part of our pathway analysis and gene ontology representation, we utilized the Elsevier’s PathwayStudio v. 10 (https://www.elsevier.com/solutions/pathway-studio-biological-research, accessed on 20 June 2021). This software handles high throughput “omics” data that can be interrogated for functional analysis and interactome assessment. For this purpose, differential proteins and altered miRNAs were evaluated using the propriety PathwayStudio ResNet database gene analysis. “Subnetwork Enrichment Analysis” (SNEA) algorithm builds functional pathways where Fisher’s statistical test is used to associate one differential hit (gene/protein/microRNA, etc.) to a specific molecular function, pathway, or biological process with the option of localizing these in cellular compartments. The association is categorized into protein modification targets, regulation targets binding partners, and expression targets.

3. Results

3.1. Demographic Characteristics

The demographic characteristics are reported in Table 1. For each group, 3F and 2M samples were selected. The average age ± standard deviation for the control group was 85.4 ± 9.2; 82 ± 4.5 and 84.4 ± 6.8 for Braak III-IV and Braak V-VI respectively. The average post-mortem delay ± standard deviation for control group was 49.7 ± 13.00; 35.8 ± 20.18 and 43.25 ± 17.68 for Braak III-IV and Braak V-VI respectively. History of diagnosis, Braak tangle stage, and CERAD (Consortium to establish a registry for Alzheimer’s disease) are also reported in Table 1.

3.2. MicroRNA Analysis

Fifty-four differentially expressed miRNAs (DE-miRNAs) were found, of which 14 were substantially different between control and Braak stage III-IV and 44 between control and Braak stage V-VI (Table 2). A further analysis comparing Braak stage III-IV versus Braak stage V-VI is available in (Supplementary Materials Table S1).
These changes can also be visualised using a hierarchal clustered heatmap (Figure 1). There is a clear shift towards blue in the Braak stage V-VI, whereas the control group is red, with the Braak stage III-IV sharing differences with both sides airing closer to the control group. Overall, this visual representation demonstrates that the majority of differentially expressed miRNAs are downregulated as the disease progresses.

3.3. Protein Analysis

Upon extraction, one sample (Braak stage III-IV) featured a slightly red colour indicating either a higher blood content or some impurities in the extraction process. In addition, this sample was tagged in the quality control analysis due to lower overall signal intensities. Therefore, this sample was excluded from the biostatistical analysis.
Fold changes and statistically significant proteins are listed in Table 3.
Comparing the AD subtypes with controls, 73 differential proteins were identified for Braak stage III-IV and 119 for Braak stage V-VI. In addition, 18 proteins were differential for both AD subtypes.
A further comparison (Braak stage III-IV vs. Braak stage V-VI) is available in (Supplementary Materials Table S2).
Relative expression levels for differential proteins identified across all comparisons with both ׀logFC׀ > 0.5 and adj. p-value < 0.001 are summarised in heat map Figure 2.

3.4. MiRNA Target and Pathways Analysis

MiRNA targets of DE-miRNA were selected using DIANA tools as described above. The predicted target genes for DE-miRNAs were compared against differentially expressed proteins (DE-proteins) found in the brain samples. Inverse relationships between expression of miRNA and target proteins were checked and showed one case in Braak stage III-IV and 34 cases in Braak stage V-VI (Table 4).
Pathways analyses obtained for both DE-miRNAs and DE-proteins were compared and resulted in one common pathway (Rap1 signalling pathway) in Braak stage III-IV (Figure 3) and six pathways (ECM-receptor interaction, MAPK signalling pathway, Ras signalling pathway, PI3K-Akt signalling pathway, FoxO signalling pathway, and Rap1 signalling pathway) in Braak stage V-VI (Figure 4).
Pathways analyses obtained for both DE-miRNAs and DE-proteins in Braak stage III-IV.showed one common pathway (Rap1 signalling pathway).
Pathways analyses obtained for both DE-miRNAs and DE-proteins in Braak stage V-VI. showed six common pathways (ECM-receptor interaction, MAPK signalling pathway, Ras signalling pathway, PI3K-Akt signalling pathway, FoxO signalling pathway, and Rap1 signalling pathway).
In Rap1 signalling pathway of Braak stage III-IV, no inverse relation was found between miRNA targets and proteins analysed in this study. In the six common pathways identified in Braak stage V-VI, the inverse relations between DE-miRNAs and DE-proteins are reported in Table 5.
Some of these inverse relationships were verified by using ELISA kits (all details are presented in Supplementary Materials).

3.5. Pathway Studio

Pathway studio was interrogated to analyse DE-miRNAs and DE-proteins at early and late stage of AD. Figure 5 panel A shows the pathological processes triggered by Braak III-IV DE-miRNA. The most relevant AD pathways, namely lipotoxicity, apoptosis, and senescence, are shown in panel B of the same figure. The senescence pathway showed to be regulated by miR-204, one of the most significantly expressed miRs of early AD. In panel C and D, the pathological processes of miRNA targets and the main AD Braak V-VI pathway-related ones, including cell death, cell ageing, senescence, and apoptosis are shown, respectively.
DE-proteins pathways are represented in Figure 6 and confirmed the ECM and cell-cell adhesion among the main processes regulated during AD development.

4. Discussion

Alzheimer’s disease is a complex, multifactorial disease with no available treatment due to the lack of understanding of causative factors of the disease. It is also difficult to diagnose AD early in the disease process because of the late onset nature of symptoms and the lack of clinical clarity between AD and other neurodegenerative and neurocognitive diseases. Understanding the mechanisms underlying the initiation of neurodegeneration is critical, and the early stages of disease may present a valuable therapeutic window before irreversible brain damage has occurred. Therefore, in this paper, we had two main objectives; the first one was to identify the molecular mechanisms and the cellular processes developed in the late and confirmed cases of AD. We achieved this outcome profiling microRNAs and proteins in the brains of AD Braak V-VI patients. The microRNAs and proteins found significantly and differentially expressed were then used for advanced bioinformatic analyses to determine their relative pathways. In addition, an inverse relation between miRs and protein targets was also investigated. The inverse association with protein targets has a greater likelihood of directly targeting those proteins, making them suitable for miRNA therapy.
The second objective was to perform the same analyses as above using brain tissue of AD patients at an early stage (Braak III-IV) and comparing them to controls. This second outcome was to evaluate if any of the mechanisms detected in the late phase of AD could be earlier identified, therefore opening a window for potential early interventions.
MiRNAs were a phenomenon discovered in 1993, and ever since, there has been much interest in their ability to regulate gene expression within diseases [30]. More recently, they have been studied as biomarker candidates of many pathologies, including AD, in blood and CSF [31,32]. A comparison between blood/CSF miRNAs and brain DE-miRNAs of this research is available in (Supplementary Materials Table S3). Results matched 14 DE-miRNAs between biofluids and brain tissue, offering an alternative way of non-invasively monitoring any change or advancement of AD pathology.
In this study, we identified 14 DE-miRNAs and 73 DE-proteins between the control and early stage of AD samples. An inverse expression was seen in the upregulation of miR-204-5p and its potential downregulated target SELE (selection E), a protein responsible for accumulation of leukocytes at the sites of inflammation. MiR-204 was also depicted by our analysis using pathway studio as key regulator of senescence (Figure 5, panel B). Other studies showed miR-204-5p to be one of the most abundantly expressed in AD [33] and upregulated in sporadic Parkinson’s disease patients, leading to apoptosis of dopaminergic cells in the brain [34]. However, none was able to prove that SELE was a miR-204-5p target.
Bioinformatic analysis revealed 58 mutual KEGG pathways between Braak III-IV DE-miRNAs and DE-proteins and one common pathway, the R1 signalling pathway. Rap1 is a small GTPase that controls diverse processes, such as cell adhesion, cell-cell junction formation, and cell polarity, by regulating the function of integrins and other adhesion molecules [35,36]. Previous studies showed that inhibition of Rap1 interactions reduces Ca2+ dysfunction and improves neuronal survival [37].
Cell adhesion and extracellular matrix (ECM) degradation are two pathways found regulated by DE-proteins of early AD using pathway studio. The same pathways were also found in DE-miRNAs and DE-proteins of the late stage of AD.
In the late stage of AD, we reported 44 DE-miRNAs compared to control, of which four were in common with the early stage of AD, and 119 DE-proteins, of which 18 were in common with the early stage. By identifying miRNAs and target proteins with an inverse expression, we were also able to select 34 potential connections that may play a role in AD pathology (Table 4). The associated KEGG pathways also helped to understand the possible cellular effects of each deregulated miRNA. Forty-two pathways were found in proteins and 69 in microRNAs and six pathways were in common: ECM-receptor interaction, MAPK signalling pathway, Ras signalling pathway, PI3K-Akt signalling pathway, FoxO signalling pathway, and Rap1 signalling pathway.
The first pathway, the ECM-receptor interaction, also confirmed by the pathway studio analysis of DE-proteins, is related to cellular integrity and consists of proteoglycans/glycosaminoglycans (PGs/GAGs), proteins, proteinases, and cytokines, which surround cells and facilitate intercellular communication [38]. In CNS, fibrous proteins (collagen, elastin) and adhesive glycoproteins (laminin, fibronectin) were responsible for the strength and elasticity of the ECM [39,40]. ECM also regulates intercellular communication and coordinate processes, such as cell migration, proliferation, differentiation, and apoptosis as well as tissue morphogenesis and homeostasis [41,42], and aberrant expression of the ECM components in the brain can lead to neuro-development disorders, psychiatric dysregulation, and neuro-degenerative diseases [43,44]. An upregulation of collagen IV, laminin, and fibronectin in the brains of AD and a co-localization with Senile Plaques was also proven [45].
In our study, we found several inverse expressions between miRs and proteins with a function in the ECM pathway. Among them, we found hsa-miR-671-5p and Thrombospondin 1 (THBS1), an adhesive glycoprotein that mediates cell-to-cell and cell-to-matrix interactions. This protein can bind to fibrinogen, fibronectin, laminin, type V collagen, and integrins alpha-V/beta-1, hsa-miR-132-3p, and hsa-miR-212-3p, inversely related to CD44, which was previously found upregulated in severe Alzheimer’s disease [46]. hsa-miR-30e-5p and Integrin beta-1 (ITGB1), a receptor for collagen, is another interaction, and it was suggested to undergo plasticity through interactions with ECM proteins, modulating ion channels, intracellular Ca2+ and protein kinases signalling, and reorganization of cytoskeletal filaments [47]. ECM and integrin aberrations are likely to contribute to imbalanced synaptic function in epilepsy, Alzheimer’s disease, mental deficiency, schizophrenia, and other brain conditions [48]. hsa-miR-1-3p, hsa-miR-425-3p, and hsa-miR-1237-3p are all downregulated, and all show the upregulated fibronectin (FN1) as potential target. Fibronectin is the adhesive protein that connects cells with collagen fibres in the ECM, binds to cell-surface integrin regulating cytoskeleton reorganization, and facilitates cell migration [48,49,50,51,52,53].
In addition, several studies reported that the upregulation of fibronectin enhanced the adherence of microglial cells and lead to APP secretion [54,55] and Aβ aggregation in AD pathogenesis.
The other five pathways found to be compromised in the late stage of AD are MAPK, FoxO, Rap1, Ras, and PI3K-Akt signalling pathways. These are intracellular signalling pathways and are required for signal relay, signal amplification, and integration of stimuli in the cell, leading to proliferation, differentiation, apoptosis, and inflammation.
The MAPK signalling pathway is key for signal transduction between neurons and is involved in the maintenance of synaptic plasticity, which is reduced with progression of AD [56]. In particular, p38 MAPK is stimulated by phosphorylated tau, normal dephosphorylated tau, and Aβ oligomers, leading to inflammatory signalling and activation of microglia in AD and inhibition of p38 MAPK pathways, successfully reducing Aβ and memory loss in AD mice [56,57,58,59,60] and suggesting this MAPK signalling as an important therapeutic target.
FoxO signalling pathway is active in many parts of the brain, including the hippocampus, amygdala, and nucleus accumbens. It functions to determine cell fate and survival, leading to apoptosis, cell proliferation, and differentiation. Dysregulation of FoxOs can lead to increased apoptosis and oxidative stress [61,62]. Some studies also suggest that FoxO proteins may be a downstream target of APP, leading to an APP intracellular domain induced death in neurones, with APP as a transcriptional coactivator of FoxO proteins [63]. Other studies speculated that FoxO proteins may also be upstream regulators of APP [64].
The phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway seems to be particularly important for mediating neuronal survival [65], playing a role in learning and memory as well [66,67]. It is also involved in the pathogenesis of acute cerebrovascular disease, neurodegeneration diseases, epilepsies, and AD [68,69]. The epidermal growth factor receptor (EGFR) was one of the inverse expressed targets in this pathway, and several studies demonstrated that EGFR inhibitors improve pathological and behavioural conditions in AD [70,71]. Moreover, EGFR expression is up-regulated in reactive astrocytes, and its hyper-activation leads to astrogliosis [72,73]. The neuroprotective effect of EGFR inhibition was demonstrated in ALS also [71]. EGFR is regulated by miR-1-3p, and this interaction was already found in cancer [74].
Finally, research groups also reported an increased Ras expression in cells with upregulated APP expression, which could lead to deregulation of the cell cycle [75].

Limitations

All samples used in this study were collected within 72 h of death and then frozen. This long interval of time could have affected the miRNA abundance due to the degradation of the miRNAs. The sample size for all groups (n = 5 per group) was small, and the control and AD groups decreased to n = 4 for the protein microarray. Due to the exploratory nature and the small sample size of the study, differences between groups in miRNAs are presented without adjustments for multiple comparisons; thus, further studies with larger numbers of samples will be required to assess the reproducibility of these findings.
Moreover, we were unable to test for specific proteins in the microarray because of the Sciomics customised platform (1351 proteins only) for this analysis. As result, not all specific targets of the differential miRNAs have been analysed, and the blind analysis of proteins limited the comparison to miRNA expression in this study; therefore, it is important to look at specific protein targets in the future.
Finally, of the 2300 known miRNAs in the human brain, only 751 were analysed for differential expression in this study. This will be an important avenue for future investigation.

5. Conclusions

In conclusion, our results demonstrate that miRNAs and proteins are differentially expressed in the early and late stages of Alzheimer’s disease. The correlation between their expression must be explored further to understand how these miRNAs regulate gene expression in disease progression. In addition, mutual signalling pathways between the differential miRNAs and proteins were investigated, showing only one common pathway (the Rap1 signalling) between the early and late stage of AD.
While the differentially expressed miRNAs and mutual signalling pathways that were found in this study are in line with previous work, new interactions between miRNA and protein targets were also identified. These novel findings must be investigated further to gain a deeper understanding of their roles in AD and their potential use as therapeutic targets. The results of this can be taken as a basis for future, larger-scale studies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells11010163/s1, Table S1: DE-miRNAs of Braak stage III-IV compared to Braak stage V-VI. Table S2: DE-proteins of Braak stage III-IV compared to Braak stage V-VI. Figure S1: FN1 and EFGR protein concentrations after ELISA quantification. Table S3: DE-miRNAs of AD brain tissues identifidied in this study compared to DE-miRNAs of different biofluids identified in literature.

Author Contributions

Conceptualization, C.N.W., S.M., F.K., A.B. and V.D.P.; methodology, C.N.W., G.B. and V.D.P.; validation, K.M.Y., F.K., S.M. and V.D.P.; formal analysis, E.A., D.T., M.A.H., A.N. and F.K.; investigation, C.N.W., G.B. and K.M.Y.; resources, C.N.W. and G.B.; data curation, C.N.W., G.B., K.M.Y. and V.D.P.; writing original draft preparation, C.N.W. and V.D.P.; writing—review and editing, all. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by ARUK small Research Grant and by the University of Birmingham (UK).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the NHS Rec generic approvals of Research Tissue Bank (18/SW/0029) on the 17 May 2019.

Data Availability Statement

The datasets used or analysed during the current study available from the corresponding author on reasonable request.

Acknowledgments

We would like to thank the South-West Dementia Brain Bank (SWDBB) for providing brain tissue for this study. The SWDBB is part of the Brains for Dementia Research programme, jointly funded by Alzheimer’s Research UK and Alzheimer’s Society and is supported by BRACE (Bristol Research into Alzheimer’s and Care of the Elderly) and the Medical Research Council.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Heatmap showing the expression of 54 differentially regulated microRNAs in Alzheimer’s disease. Fifty-four microRNAs were found using Mann–Whitney statistical analysis across the 2 different comparisons, Control vs. Braak stage III-IV and Control vs. Braak stage V-VI. A p-value of less than 0.05 was considered significant. Hierarchal clustering is present on the left with colours relevant to their group. This was made using heatmap.2 in the R programming language with complete linkage, and Euclidean distance used to compute.
Figure 1. Heatmap showing the expression of 54 differentially regulated microRNAs in Alzheimer’s disease. Fifty-four microRNAs were found using Mann–Whitney statistical analysis across the 2 different comparisons, Control vs. Braak stage III-IV and Control vs. Braak stage V-VI. A p-value of less than 0.05 was considered significant. Hierarchal clustering is present on the left with colours relevant to their group. This was made using heatmap.2 in the R programming language with complete linkage, and Euclidean distance used to compute.
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Figure 2. Heatmap showing the expression of 174 differentially regulated proteins in Alzheimer’s disease across the 2 compariosns Control vs. Braak stage IiI–IV and Control vs. Braak stage V-VI. A p-value of less than 0.05 was considered significant. Hierarchal clustering is present on the left with colours relevant to their group. This was made using heatmap.2 in the R programming language with complete linkage, and Euclidean distance used to compute.
Figure 2. Heatmap showing the expression of 174 differentially regulated proteins in Alzheimer’s disease across the 2 compariosns Control vs. Braak stage IiI–IV and Control vs. Braak stage V-VI. A p-value of less than 0.05 was considered significant. Hierarchal clustering is present on the left with colours relevant to their group. This was made using heatmap.2 in the R programming language with complete linkage, and Euclidean distance used to compute.
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Figure 3. Braak stage III-IV pathways analyses.
Figure 3. Braak stage III-IV pathways analyses.
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Figure 4. Braak stage V-VI pathways analyses.
Figure 4. Braak stage V-VI pathways analyses.
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Figure 5. DE-miRNA pathway studio analyses in Braak III-IV and Braak V-VI. The pathological processes triggered by Braak III-IV DE-miRNA (panel A) and Braak V-VI DE-miRNA (panel C). The related pathways of Braak III-IV DE-miRNA (panel B) and Braak V-VI DE-miRNA (panel D).
Figure 5. DE-miRNA pathway studio analyses in Braak III-IV and Braak V-VI. The pathological processes triggered by Braak III-IV DE-miRNA (panel A) and Braak V-VI DE-miRNA (panel C). The related pathways of Braak III-IV DE-miRNA (panel B) and Braak V-VI DE-miRNA (panel D).
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Figure 6. DE-protein pathway studio analyses in Braak III-IV and Braak V-VI. AD pathways generated by DE-proteins in Braak III-IV (panel A) and Braak V-VI DE-miRNA (panel B).
Figure 6. DE-protein pathway studio analyses in Braak III-IV and Braak V-VI. AD pathways generated by DE-proteins in Braak III-IV (panel A) and Braak V-VI DE-miRNA (panel B).
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Table 1. Demographics of samples received from South-West Dementia Brain Bank. CAA= cerebral amyloid angiopathy.
Table 1. Demographics of samples received from South-West Dementia Brain Bank. CAA= cerebral amyloid angiopathy.
GroupsAgeSexPost-Mortem DelayHist DiagnosisBraak Tangle StageCERAD Diag. Group
Controls77M42No AD, mild to moderate CAA1No AD
74F39.5Control, no significant abnormalities1No AD
92M56.5Control, moderate CAA1No AD
90F67.25Single microinfarct in occipital cortex fine to use as control1No AD
94F43.25Normal-looking brain1No AD
Early stage79F70AD3
90F21AD4
80M24AD probable, moderate CAA4Probable AD
80F26AD probable, moderate CAA4Probable AD
81M38AD definite4Definite AD
Late stage88F68AD definite, moderately severe CAA5Definite AD
93F31.75AD definite5Definite AD
78M49.5AD mod severe6Definite AD
86F45.25AD definite (severe)6Definite AD
77M21.75AD definite, moderate CAA6Definite AD
Table 2. DE-miRNA in Braak stage III-IV and Braak stage V-VI compared to controls (Braak stage I-II). Fold changes (FD), standard deviation (SD), and p-values ≤ 0.05 are reported.
Table 2. DE-miRNA in Braak stage III-IV and Braak stage V-VI compared to controls (Braak stage I-II). Fold changes (FD), standard deviation (SD), and p-values ≤ 0.05 are reported.
MiRNAsCONTR
FC ± SD
Braak III-IV
FC ± SD
CONTR vs. Braak III-IV
p-Value
Braak V-VI
FC ± SD
CONTR vs. Braak V-VI
p-Value
hsa-miR-221-3p1.01 ± 0.150.76 ± 0.110.03
hsa-miR-431-5p1.01 ± 0.170.63 ± 0.230.030.55 ± 0.140.01
hsa-miR-494-3p1.01 ± 0.200.65 ± 0.200.03
hsa-miR-671-5p1.02 ± 0.210.66 ± 0.170.030.52 ± 0.150.01
hsa-miR-222-3p1.01 ± 0.150.76 ± 0.080.03
hsa-miR-542-5p1.06 ± 0.370.56 ± 0.130.030.39 ± 0.180.01
hsa-miR-642a-5p1.10 ± 0.503.79 ± 2.870.03
hsa-miR-548a-5p1.03 ± 0.240.40 ± 0.270.01
hsa-miR-204-5p1.04 ± 0.301.50 ± 0.120.011.66 ± 0.370.01
hsa-miR-520d-5p1.22 ± 0.830.45 ± 0.150.03
has-miR-651-5p1.05 ± 0.370.53 ± 0.170.03
has-miR-301b-3p1.02 ± 0.220.63 ± 0.200.03
hsa-miR-6361.04 ± 0.310.37 ± 0.130.01
hsa-miR-548a-3p1.05 ± 0.360.47 ± 0.320.03
hsa-miR-140-5p1.00 ± 0.07 0.81 ± 0.090.01
hsa-miR-210-3p1.19 ± 0.93 0.54 ± 0.160.03
hsa-miR-185-5p1.09 ± 0.59 0.64 ± 0.050.01
hsa-miR-425-5p1.03 ± 0.33 0.61 ± 0.100.01
hsa-miR-503-5p1.13 ± 0.64 0.37 ± 0.190.01
hsa-miR-30b-3p1.02 ± 0.22 0.60 ± 0.120.01
hsa-miR-545-3p1.00 ± 0.11 0.68 ± 0.190.03
hsa-miR-6651.09 ± 0.53 0.40 ± 0.120.01
hsa-miR-132-3p1.05 ± 0.33 0.47 ± 0.230.03
hsa-miR-22-5p1.01 ± 0.13 0.64 ± 0.300.01
hsa-miR-30e-5p1.00 ± 0.12 0.76 ± 0.090.01
hsa-miR-32-5p1.00 ± 0.10 0.67 ± 0.290.01
hsa-miR-324-3p1.00 ± 0.07 0.81 ± 0.230.03
hsa-miR-877-5p1.01 ± 0.20 0.67 ± 0.130.03
hsa-let-7i-5p1.04 ± 0.35 0.64 ± 0.200.01
hsa-miR-6021.06 ± 0.43 0.57 ± 0.200.03
hsa-miR-148b-3p1.00 ± 0.02 0.77 ± 0.220.01
hsa-miR-34c-3p1.48 ± 1.76 0.39 ± 0.180.01
hsa-miR-6221.12 ± 0.48 0.38 ± 0.270.01
hsa-miR-185-3p1.10 ± 0.62 0.43 ± 0.150.01
hsa-miR-766-3p1.00 ± 0.12 0.78 ± 0.120.03
hsa-miR-3261.02 ± 0.21 0.63 ± 0.290.03
hsa-miR-147a1.05 ± 0.37 0.45 ± 0.270.03
hsa-miR-146b-5p1.84 ± 2.72 0.41 ± 0.090.01
hsa-miR-1-3p1.01 ± 0.18 0.69 ± 0.110.03
hsa-miR-425-3p1.01 ± 0.18 0.66 ± 0.070.01
hsa-miR-450a-5p1.07 ± 0.38 0.48 ± 0.250.03
hsa-miR-106b-5p1.02 ± 0.26 0.72 ± 0.110.01
hsa-miR-212-3p1.06 ± 0.38 0.36 ± 0.180.01
hsa-miR-4841.06 ± 0.47 0.66 ± 0.090.01
hsa-miR-219a-1-3p1.04 ± 0.35 0.48 ± 0.150.01
hsa-miR-12441.05 ± 0.36 0.48 ± 0.140.01
hsa-miR-12051.04 ± 0.31 0.60 ± 0.290.03
hsa-miR-146b-3p1.78 ± 2.56 0.42 ± 0.080.01
hsa-miR-9201.09 ± 0.48 0.20 ± 0.110.01
hsa-miR-1237-3p1.10 ± 0.52 0.47 ± 0.130.03
hsa-miR-671-3p1.016 ± 0.20 0.67 ± 0.110.01
hsa-miR-582-3p1.03 ± 0.29 0.61 ± 0.070.01
hsa-miR-15391.12 ± 0.61 0.36 ± 0.170.01
hsa-miR-30c-1-3p1.02 ± 0.21 0.74 ± 0.090.01
Table 3. DE-proteins in Braak stage III-IV and Braak stage V-VI compared to controls (Braak stage I-II). Fold changes (LogFC), standard deviation, and adj. p-values ≤ 0.05 are reported.
Table 3. DE-proteins in Braak stage III-IV and Braak stage V-VI compared to controls (Braak stage I-II). Fold changes (LogFC), standard deviation, and adj. p-values ≤ 0.05 are reported.
ProteinHGNC NumberBraak Stage III-IV vs. CONTR logFCadj. p-ValueBraak Stage V-VI vs. CONTR logFCadj. p-Value
G3PGAPDH1.40.00740.970.044
DPB1HLA-DPB11.230.00612.051.9 × 10−6
METRLMETRNL1.20.0121.310.0038
CD9CD91.150.0057
IL34IL341.070.02
GLPBGYPB1.030.0061
FGFP1FGFBP10.920.043
HPTHP0.80.048
GPNMBGPNMB0.790.0190.950.0025
CD81CD810.740.00290.710.0017
PLK1PLK10.650.0330.610.042
CNTN2CNTN20.620.022
SDC4SDC40.620.00440.850.000014
CATL1CTSL0.610.00025
PARK7PARK70.60.0017
CATDCTSD0.550.00026
PGCAACAN0.550.0210.550.016
LEG3LGALS30.540.018
HSPB1HSPB10.510.0330.770.00051
UROKPLAU−0.540.032
CR2CR2−0.560.0331.070.041
TR11BTNFRSF11B−0.590.032
TNFBLTA−0.590.017
FGF9FGF9−0.60.018
MEP1AMEP1A−0.60.011
CAH9CA9−0.610.0061
EGLNENG−0.610.02
FABPLFABP1−0.610.011
KLOTBKLB−0.610.019
IL17CIL17C−0.620.021
MLKLMLKL−0.630.0052
CEAM5CEACAM5−0.630.018
ITA2B (CD41a)ITGA2B−0.640.02
IFNL3IFNL3−0.640.0079
P53TP53−0.650.015
PTN1PTPN1−0.660.0074
SPA9SERPINA9−0.670.011
NECT4NECTIN4−0.680.011
IL2RBIL2RB−0.680.036
CD22CD22−0.680.019
DAFCD55−0.690.0061
ULBP3ULBP3−0.690.0061
NGALLCN2−0.690.0059
THYGTG−0.690.0095
CD72CD72−0.70.0085
VTDBGC−0.720.0044
HLA-DR −0.720.00591.530.0058
IFNGIFNG−0.720.036
KLK3KLK3−0.730.0061
EGLNENG−0.740.0029
TIE2TEK−0.740.013
CDCP1CDCP1−0.750.005
LAG3LAG3−0.760.0066
BCAMBCAM−0.790.00035
METMET−0.820.019−1.070.001
CNTFCNTF−0.830.0014
PD1L2PDCD1LG2−0.840.0032
IFNGIFNG−0.870.0061
SIGL9SIGLEC9−0.910.011
COIA1 (endostatin)COL18A1−0.940.00610.690.041
CXCL9CXCL9−0.980.048
PROK1PROK1−1.010.0074−0.760.042
PARP1PARP1−1.010.025−1.190.0052
LYAM2SELE−1.050.012
IL2RAIL2RA−1.110.011−1.20.0037
CCL26CCL26−1.120.044
CD53CD53−1.290.0081−0.980.043
POSTNPOSTN−1.330.011
TNF11TNFSF11−1.40.0047
IL34_MOUSEIl34−1.530.00014
CCL28CCL28−1.570.000032
CTGFCTGF−1.830.000055
TNR11TNFRSF11A−1.90.00063
CADH5CDH5 3.250.00051
GLUCGCG 2.364.8 × 10−6
NUB1NUB1 1.980.00083
SP1SP1 1.890.000024
CD44CD44 1.620.000037
ICAM1ICAM1 1.580.000013
I22R2IL22RA2 1.560.011
ICAM1ICAM1 1.490.000081
BLNKBLNK 1.452.7 × 10−6
FUT8FUT8 1.430.042
MKMDK 1.430.00065
JAK1JAK1 1.370.0055
ESR1ESR1 1.371.9 × 10−6
IL13_MOUSEIl13 1.320.0005
MPIP3CDC25C 1.280.022
IGKCIGKC 1.280.000017
CHK2CHEK2 1.20.046
CSF1RCSF1R 1.170.0023
KI2L2KIR2DL2 1.130.0038
CYBPCACYBP 1.090.0017
GNLYGNLY 1.070.0037
CADH1CDH1 1.070.035
BGH3TGFBI 1.050.0032
VGFR2KDR 1.040.015
PRAF3ARL6IP5 0.990.011
UPARPLAUR 0.920.0094
MYBAMYBL1 0.920.012
A2APSERPINF2 0.90.036
SRGEFSERGEF 0.830.021
ITB1ITGB1 0.835.4 × 10−7
CTNB1CTNNB1 0.820.047
FOLR3FOLR3 0.790.022
CSF3CSF3 0.780.036
FCG2BFCGR2B 0.760.00019
TSP1THBS1 0.750.013
CD15 0.740.0012
TNR6FAS 0.730.013
PIRPIR 0.710.038
ITALITGAL 0.690.00027
SLIPNUGGC 0.690.025
TFF3 0.661.1 × 10−6
TFF3 0.661.9 × 10−6
EGFREGFR 0.660.0024
20S ImmunoproteasomePSMB8 0.650.005
CD166ALCAM 0.650.0055
FINCFN1 0.640.014
FURINFURIN 0.580.045
IFNL2IFNL2 0.570.00018
CFAHCFH 0.560.0094
BGLRGUSB 0.550.0022
IGLC1IGLC1 0.540.015
CD81CD81 0.530.0023
CATACAT 0.520.0019
TNR5CD40 0.520.005
TFR1TFRC −0.510.002
RBTN2LMO2 −0.520.015
TFR1TFRC −0.530.00057
IL15IL15 −0.540.043
CCL27CCL27 −0.550.01
IL15IL15 −0.560.049
CCD50CCDC50 −0.560.000014
CALB1CALB1 −0.580.049
BTLABTLA −0.580.034
BUB1BUB1 −0.590.014
HMGN2HMGN2 −0.590.047
2A5DPPP2R5D −0.60.011
IL22IL22 −0.610.03
STAT3STAT3 −0.650.031
FAF1FAF1 −0.650.039
SAMPAPCS −0.660.03
CCR7CCR7 −0.720.022
PRIOPRNP −0.740.0094
CD97CD97 −0.790.001
HBEGFHBEGF −0.80.003
NEK2NEK2 −0.820.0056
TFR1TFRC −0.820.0052
ANGL3ANGPTL3 −0.830.013
AKT3AKT3 −0.850.0089
PAPOAPAPOLA −0.890.046
IL12AIL12A −0.910.0071
RA51CRAD51C −10.0017
ASTRAGRAMD1A −10.0063
BIRC3BIRC3 −1.010.02
RARR2RARRES2 −1.010.00042
PSN1PSEN1 −1.090.028
CD99CD99 −1.090.026
BARD1BARD1 −1.10.031
LIMD1LIMD1 −1.170.00019
CP3A5CYP3A5 −1.230.036
SDHBSDHB −1.240.017
CCL21CCL21 −1.30.013
HGFHGF −1.310.017
PRIOPRNP −1.390.0018
SAA1SAA1 −1.40.000006
TSP2THBS2 −1.460.016
Q6ZNL3FLJ00280 −1.560.038
CFAD_MOUSECfd −1.580.02
FGF23FGF23 −1.71.2 × 10−6
ARVCARVCF −1.850.012
CCR7CCR7 −1.940.032
CFLARCFLAR −2.270.001
Table 4. Number of predicted targets for DE-miRNA in Braak III-IV and Braak V-VI and matched DE-proteins with an inverse relation.
Table 4. Number of predicted targets for DE-miRNA in Braak III-IV and Braak V-VI and matched DE-proteins with an inverse relation.
MiRNAsNumber of TargetsMatched Protein Targets with Inverse Relation in Braak III-IVMatched Protein Targets with Inverse Relation in Braak V-VI
hsa-miR-221-3p302
hsa-miR-431-5p341
hsa-miR-494-3p1002
hsa-miR-671-5p414 THBS1, SP1
hsa-miR-222-3p221
hsa-miR-542-5p16
hsa-miR-642a-5p53
hsa-miR-548a-3p26
hsa-miR-204-5p228SELE
hsa-miR-520d-5p1538
hsa-miR-651-5p103
hsa-miR-301b-3p548
hsa-miR-636148
hsa-miR-548a-3p1504
hsa-miR-140-5p300 GPNMB
hsa-miR-210-3p8
hsa-miR-185-5p802 ACAN, TGFB1
hsa-miR-425-5p316
hsa-miR-503-5p276
hsa-miR-30b-3p553 ACAN, CDH1
hsa-miR-545-3p235
hsa-miR-665391 HDR
hsa-miR-132-3p626 CD44
hsa-miR-22-5p575
hsa-miR-30e-5p1207 ITGB1, CAT
hsa-miR-32-5p843
hsa-miR-324-3p91
hsa-miR-877-5p151
hsa-let-7i-5p646 IL13, FAS
hsa-miR-60223 METRNL
hsa-miR-148b-3p554 ALCAM
hsa-miR-34c-3p276 SERPINF2, FURIN
hsa-miR-622263 HLA-DPB1
hsa-miR-185-3p263 HLA-DPB1
hsa-miR-766-3p607 CHEK2
hsa-miR-326349 ACAN
hsa-miR-147a404 NUGGC
hsa-miR-146b-5p682 MYBL1
hsa-miR-1-3p634 EGFR, FN1
hsa-miR-425-3p11 FN1
hsa-miR-450a-5p24
hsa-miR-106b-5p1254 ESR1, FURIN, JAK1
hsa-miR-212-3p597 CD44
hsa-miR-484516
hsa-miR-219a-1-3p142
hsa-miR-1244233
hsa-miR-12051011 CSF3
hsa-miR-146b-3p270H
hsa-miR-920823 BLNK
hsa-miR-1237-3p170 FNI
hsa-miR-671-3p29
hsa-miR-582-3p340
hsa-miR-15396
hsa-miR-30c-1-3p431 ACAN
Table 5. Common pathways identified in DE-miRNAs and DE-proteins in Braak stage V-VI. For each of the pathways, the involved microRNAs and the correspondent predicted targets are listed. The protein targets identified in this study with an inverse relation are in bold.
Table 5. Common pathways identified in DE-miRNAs and DE-proteins in Braak stage V-VI. For each of the pathways, the involved microRNAs and the correspondent predicted targets are listed. The protein targets identified in this study with an inverse relation are in bold.
Common KEGG Pathways in Braak V-VIMicroRNAsTargets in the Pathways and Inverse Relation
ECM-receptor interactionhsa-miR-30b-3pCOL27A1, DAG1, COL11A2, VWF
hsa-miR-671-5pTHBS1, CD47
hsa-let-7i-5pCOL27A1, COL3A1, COL1A1, COL1A2, COL1A2, ITGA7, COL4A6, COL5A2, COL4A1
hsa-miR-140-5pDAG1, TNN
hsa-miR-766-3pLAMB4, COL24A1, DAG1
hsa-miR-484LAMB3, DAG1
hsa-miR-920ITGA3, DAG1
hsa-miR-665COL24A1, COL6A6, COL3A1, TNR
hsa-miR-326COL24A1, ITGA5, CD47
hsa-miR-132-3pITGA9, COL4A4, COL11A1, COL5A2, CD44
hsa-miR-30e-5pITGB1, ITGA9, ITGA8, ITGA4, ITGA6, ITGB3
hsa-miR-148b-3pLAMB2, ITGB8, ITGA9, ITGA5, ITGA11, COL2A1, COL4A1, LAMA4
hsa-miR-212-3pITGA9, COL4A4, COL11A1, COL5A2, CD44
hsa-miR-431-5pITGA1, LAMC2
hsa-miR-1-3pITGB6, COL4A3, FN1, LAMC2
hsa-miR-185-5pSV2B, ITGA1
hsa-miR-1237-3pSV2B, ITGB6, COL6A6, THBS3, COL6A3, VWF, FN1, TNR
hsa-miR-324-3pITGB5, COL2A1
hsa-miR-622ITGB5, COL2A1
hsa-miR-106b-5pITGB8
hsa-miR-1244ITGB8, ITGAV
hsa-miR-324-3pITGB5, COL2A1
hsa-miR-622ITGB5, COL2A1
hsa-miR-106b-5pITGB8
hsa-miR-1244ITGB8, ITGAV
hsa-miR-32-5pITGA8, COL27A1, ITGB6, ITGA5, ITGAV, COL5A1, COL1A2, COL11A1, ITGA6
hsa-miR-204-5pCOL6A6, COL5A3
hsa-miR-425-5pTNR
hsa-miR-147aSV2A, ITGA11, TNR
hsa-miR-582-3pCD47
hsa-miR-22-5pITGA5
hsa-miR-425-3pFN1
hsa-miR-503-5pCOL4A1
MAPK signalling pathwayhsa-miR-503-5pCACNA1I, RAPGEF2, FGF2, AKT3, FGF7
hsa-miR-665TGFBR1, MAPK8IP2, CACNG7, NLK, FGF2, MAX, AKT3, CACNA1D
hsa-miR-132-3pMAP3K3, CRK, KRAS, NLK, RASA1, ZAK, SOS1, TGFB2, AKT3, MECOM, DUSP9, MAPK1
hsa-miR-212-3pMAP3K3, CRK, KRAS, NLK, RASA1, ZAK, SOS1, TGFB2, AKT3, MECOM, DUSP9, MAPK1
hsa-let-7i-5pTGFBR1, MAP4K2, NRAS, ELK4, MAP4K3, MAP4K4, MAP3K1, TP53, FGF11, CASP3, RASGRP1, MAPK8, FLNA, NGF, FAS, MEF2C, DUSP1
hsa-miR-34c-3pMRAS, PAK2, BDNF, MAP2K6, PPP3CB, RASGRP1, PPM1A, SOS1, MAP3K2, MEF2C, GRB2
hsa-miR-22-5pPRKCA, ELK4, RAP1A, RAF1, MAP4K3, RASA1, SOS1, CACNA2D4, NF1, MAP2K1, MAP3K2
hsa-miR-147aCACNA1A, FLNC, FGF14, CRKL, MAP4K3, TAOK1, CACNA1I, FGF11, NLK, PDGFB, CACNA2D2, HSPA8, CACNG2, PRKCB, MAP3K2, DUSP9, FGFR2, MAPK10
hsa-miR-1-3pATF2, RAP1A, MAP4K3, EGFR, MAP3K1, BDNF, IKBKB, RASA1, RAPGEF2, SOS1, MAX, NFATC3, RAP1B, PRKACB, PDGFA
hsa-miR-1205NTRK2, CACNA1A, MAP4K2, DUSP6, PLA2G4F, RASGRP2, IKBKB, JMJD7-PLA2G4B, CACNA1E, MAPT, NF1, MKNK1, RASGRP3, MEF2C, MAPK1, PDGFRB
hsa-miR-30e-5pRASA2, PPP3R1, MAP4K4, FGF20, MAP3K13, KRAS, TAOK1, PPP3CA, RASA1, CASP3, PPP3CB, RAPGEF2, MAPK8, SOS1, MAP3K2, RASGRP3, CACNB2, NFATC3, MAP3K7, RAP1B, MAP3K5, PDGFA, RPS6KA2
hsa-miR-30b-3pTAB1, MAPK8IP2, MAP3K13, PAK1, FGF11, DUSP10, PPP5D1, ZAK, ARRB2, CACNB2, MAPK1, ELK1
hsa-miR-326CACNG8, ELK4, RAF1, RPS6KA1, MRAS, PAK1, IKBKB, FGF9, FGF18, PTPN5, FGFR2, FGF1, ELK1
hsa-miR-920LAMTOR3, MAP3K2
hsa-miR-431-5pNTRK2, MAP3K13, TAOK1, RAPGEF2, CACNA1E
hsa-miR-671-5pNTRK2, NFKB1, STK4, MAP3K4, MAPKAPK3, MAPK1
hsa-miR-106b-5pTAOK3, BRAF, NTRK2, PDGFRA, DUSP2, RASA2, ELK4, CRK, MAPK7, RASGRF2, PPP3R1, MAP3K1, RRAS2, TAOK1, DUSP10, RASA1, MAPK8, ZAK, SOS1, STK3, HSPA8, NF1, MAP3K2, MKNK2, MAPK1, TGFBR2, MAP3K5, RPS6KA2
hsa-miR-30c-1-3pNTRK2, STK4, MAP3K13, PAK1, MAP2K6, FGF11, SOS1, ARRB1
hsa-miR-545-3pCRK, MAP3K1, TAOK1, PPP3CA, CACNA1B, PDGFB, FLNA, MAXPTPN5, MEF2C
hsa-miR-148b-3pSOS2, NRAS, GADD45A, MRAS, MAP3K4, MAP3K12, SOS1, MAX, TGFB2, DUSP1
hsa-miR-146b-5pBRAF, PDGFRA, HSPA1A, NRAS, TRAF6
hsa-miR-766-3pCACNA1G, RASGRP2, MAP3K4, BDNF, IKBKB, NLK, PRKCG, DUSP7, TNFRSF1A, RPS6KA4
hsa-miR-425-5pMAPK8, ZAK, SOS1, FGF9, PRKCB, STMN1, MEF2C, MAP3K5
hsa-miR-219a-1-3pFASLG
hsa-miR-1244CACNB4, RAPGEF2, MAPK8, MAP3K2, RAP1B
hsa-miR-582-3pCACNB4, MAP3K4, TAOK1, RAPGEF2
hsa-miR-622ATF2, RASA1, MAP3K12, MAPKAPK3, NF1, MECOM, STMN1
hsa-miR-185-3pPRKCA, CACNA1G, FLNC, MAP3K3, FGF17, MAPK13, RPS6KA5, PAK1, CACNA1I, FLNB, FLNA, MAPK1, CACNB3
hsa-miR-484RPS6KA1, RASGRP2, MAP3K11, FLNB, FLNA, FGF8, FGF1, MAPKAPK2, PDGFA
hsa-miR-671-3pMAP4K1, RASGRF1
hsa-miR-185-5pPAK2, CACNG7, MAPK14, MAP2K6, CACNG4, CDC42, PRKCB, RELA, NFATC3
hsa-miR-146b-3pRAF1, PRKCB, RPS6KA3, IL1A, PLA2G4C
hsa-miR-204-5pFGF20, TAOK1, SOS1, DUSP3
hsa-miR-1237-3pTAOK3, CACNG8, TGFBR1, PTPRR, TAB2, MAPK11, CACNA1E, STK3, FGF1, CACNB3
hsa-miR-324-3pTAB1, ARRB1
hsa-miR-542-5pIKBKB
hsa-miR-140-5pPDGFRA, GNG12, NLK, CACNB1, FGF9
hsa-miR-32-5pBRAF, RAP1A, CACNA1I, NLK, DUSP10, ZAK, MAP3K2, CACNA2D1, DUSP5, MAP2K4, RAP1B, DUSP1
hsa-miR-877-5pTAB2, MAP2K4
hsa-miR-602TAOK3, RASGRP2
hsa-miR-450a-5pCACNA1F, DUSP10
Ras signalling pathwayhsa-miR-1-3pMET, RAP1A, ETS1, EGFR, GNB1, CALM2, IKBKB, RAB5A, RASA1, SOS1, IGF1, RAP1B, PRKACB, PDGFA
hsa-miR-204-5KSR2, SHC1, FGF20, SOS1
hsa-miR-425-5pMAPK8, SOS1, FGF9, PRKCB, GF1, ABL2, GRIN2B
hsa-miR-132-3pGNB1, KRAS, RASA1, SOS1, EXOC2, AKT3, PIK3CA, MAPK1, GRIN2B
hsa-miR-22-5pPRKCA, GNG11, PIK3CB, RAP1A, RAF1, RASA1, SOS1, NF1, MAP2K1, GRIN2B
hsa-miR-30e-5pRASA2, EFNA3, IGF1R, FGF20, KRAS, GNG10, RASA1, PIK3CD, PLA2G12A, MAPK8, BRAP, FLT1, SOS1, PDGFC, RASAL2, RASGRP3, RGL1, ABL2, GRIN2A, RAP1B, ABL1, PLA2G2C, PDGFA
hsa-miR-148b-3pRASAL1, SOS2, NRAS, ETS1, MRAS, PIK3R3, FLT1, SOS1, KITLG, IGF1, CSF1, TEK, GRIN2B
hsa-miR-34c-3pPAK2, MRAS, RASGRP1, REL, SOS1, GAB1, GRB2
hsa-miR-106b-5pPDGFRA, RASA2, RASGRF2, PAK7, TIAM1, PAK3, RRAS2, RASA1, MAPK8, REL, FLT1, SOS1, NF1, GAB1, SHC4, PDGFD, RGL1, MAPK1, ABL2, RAB5B, RAPGEF5, GNB5, GRIN2B
hsa-miR-212-3pGNB1, KRAS, RASA1, SOS1, EXOC2, AKT3, PIK3CA, MAPK1, GRIN2B
hsa-miR-30c-1-3pKSR2, STK4, PAK1, FGF11, SOS1, GRIN2A, RAB5B, GRIN2B
hsa-miR-665KSR2, RALA, FGF2, AKT3, KDR, GRIN2B
hsa-miR-185-5pKSR2, KSR1, PAK2, PAK7, GNB3, CDC42, PAK6, PRKCB, PAK6, RELA, VEGFA, ABL2, GRIN2B
hsa-miR-1205KSR2, MET, PIK3R2, PIK3R5, PLA2G4F, RASGRP2, IKBKB, JMJD7-PLA2G4B, PTPN11, NF1, PAK6, RASGRP3, RASSF1, MAPK1, PLA2G2C, PDGFRB, GRIN2B
hsa-miR-484KSR2, LAT, CALM1, RASGRP2, PAK3, PIK3CD, PDGFD, FGF8, GRIN1, FGF1, CSF1, PDGFA
hsa-miR-185-3pPRKCA, GNG13, FGF17, PLA2G6, PAK1, PAK6, SYNGAP1, MAPK1
hsa-miR-30b-3pKSR2, EFNA5, PAK1, PLA2G2D, FGF11, PAK4, PLD2, SYNGAP1, GAB2, RALB, MAPK1, RAB5B, ELK1, GRIN2B
hsa-miR-766-3pPIK3R5, ETS1, RASGRP2, IKBKB, PRKCG, PLD2, GRIN2B
hsa-miR-326GNGT1, RAF1, MRAS, PAK1, IKBKB, FGF9, PAK4, FGF18, RASAL2, FGFR2, FGF1, CSF1, ELK1, GRIN2B
hsa-miR-147aFGF14, CALM1, TIAM1, FGF11, PLCG1, PDGFB, PRKCB, PDGFC, FGFR2, MAPK10, GRIN2B
hsa-miR-146b-5pPDGFRA, NRAS, REL, ABL2, GRIN2B
hsa-miR-1237-3pKSR2, PAK7, GNG10, PLA2G12A, PIK3CA, RASAL2, HTR7, FGF1, GRIN2B
hsa-miR-503-5pIGF1R, PIK3R1, INSR, FGF2, AKT3, VEGFA, FGF7
hsa-miR-324-3pKSR2
hsa-miR-146b-3pKSR2, PAK7, RAF1, PAK3, MLLT4, PRKCB, PLA2G4C
hsa-miR-920KSR2, GAB1, SYNGAP1, CSF1
hsa-miR-1244RHOA, MAPK8, GAB1, HGF, RAP1B
hsa-let-7i-5pNRAS, IGF1R, FGF11, PLA2G3, RASGRP1, MAPK8, INSR, NGF, IGF1, PAK6, ABL2
hsa-miR-542-5pIKBKB
hsa-miR-32-5pGNG11, PIK3CB, RAP1A, PIK3R3, PIK3CA, RGL1, RAP1B
hsa-miR-219a-1-3pFASLG, IGF1
hsa-miR-671-5pNFKB1, STK4, SHC3, KIT, SYNGAP1, RAB5B, MAPK10
hsa-miR-671-3pRASGRF1
hsa-miR-582-3pPLD1, RHOA, PTPN11, ANGPT1
hsa-miR-545-3pPLA2G12A, PDGFB, PDGFD
hsa-miR-622RASA1, NF1
hsa-miR-431-5pRAB5B, RAPGEF5
hsa-miR-140-5pPDGFRA, GNG12, RALA, EFNA4, FGF9, BCL2L1
hsa-miR-877-5pPLD2
hsa-miR-602RASGRP2
PI3K-Akt signalling pathwayhsa-miR-30e-5pITGB1, ITGA9, ITGA8, EFNA3, PPP2R2B, IGF1R, FGF20, KRAS, IFNAR2, GNG10, DDIT4, PIK3CD, EIF4E, CCNE2, FLT1, SOS1, IL2RA, YWHAZ, IRS1, PDGFC, FOXO3, ITGA4, ITGA6, PPP2R1B, BCL2L11, PDGFA, ITGB3
hsa-miR-1-3pPRLR, MET, ATF2, PPP2R3A, CREB5, ITGB6, EGFR, GNB1, CDK6, YWHAQ, PPP2R5A, IKBKB, EIF4E, COL4A3, SOS1, YWHAZ, IGF1, FN1, LAMC2, PDGFA
hsa-miR-582-3pGSK3B, PPP2CA, CREB5, CDKN1B, YWHAQ, PTK2, YWHAZ, ANGPT1, FOXO3, PTEN
hsa-miR-34c-3pGSK3B, YWHAG, EIF4E, SOS1, GRB2
hsa-miR-22-5pPRKCA, MYB, GNG11, PIK3CB, ITGA5, RAF1, IL7R, PTK2, THEM4, SOS1, HSP90B1, PPP2R3C, PKN2, MAP2K1, SGK3
hsa-miR-665COL24A1, COL6A6, CDK6, COL3A1, FGF2, CASP9, AKT3, CDKN1A, TNR, PPP2R1B, KDR
hsa-miR-766-3pYWHAH, LAMB4, CDC37, COL24A1, PIK3R5, PPP2R5D, IKBKB
hsa-miR-326GNGT1, SYK, COL24A1, ITGA5, RAF1, PPP2R5D, IFNAR2, IKBKB, BRCA1, PPP2R5B, FGF9, FGF18, SGK2, GYS1, FGFR2, FGF1, CSF1
hsa-miR-1244ITGB8, LPAR3, ITGAV, HGF
hsa-miR-146b-3pRAF1, RHEB, RPS6KB2, LPAR5, CCNE1
hsa-miR-32-5pPHLPP2, TSC1, GNG11, PRKAA2, ITGA8, PIK3CB, COL27A1, ITGB6, ITGA5, PIK3AP1, GHR, ITGAV, DDIT4, PIK3R3, COL5A1, COL1A2, COL11A1, PIK3CA, ITGA6, PTEN, SGK3, BCL2L11
hsa-miR-148b-3pLAMB2, SOS2, ITGB8, ITGA9, NRAS, ITGA5, CDKN1B, YWHAB, ITGA11, COL2A1, PIK3R3, FLT1, SOS1, KITLG, PRKAA1, IGF1, PTEN, CSF1, TEK, BCL2L11, COL4A1, LAMA4
hsa-let-7i-5pPRLR, TSC1, MYB, NRAS, CCND2, COL27A1, IGF1R, COL3A1, RPS6KB2, TP53, GHR, FGF11, COL1A1, INSR, NGF, COL1A2, IGF1, ITGA7, COL4A6, CDKN1A, OSMR, COL5A2, COL4A1, IL6R
hsa-miR-671-3pRPS6KB2
hsa-miR-1205MET, PPP2CA, SYK, PIK3R2, PIK3R5, CCND2, MTCP1, IKBKB, TSC2, EIF4E, RPTOR, MAPK1, CSF3, BCL2L11, PDGFRB
hsa-miR-132-3pMYB, ITGA9, PRKAA2, CREB5, GNB1, PPP2R5C, KRAS, GHR, EIF4E, COL4A4, SOS1, AKT3, COL11A1, PIK3CA, FOXO3, PTEN, SGK3, MAPK1, COL5A2, C8orf44-SGK3
hsa-miR-212-3pMYB, ITGA9, PRKAA2, CREB5, GNB1, PPP2R5C, KRAS, GHR, EIF4E, COL4A4, SOS1, AKT3, COL11A1, PIK3CA, FOXO3, PTEN, SGK3, MAPK1, COL5A2, C8orf44-SGK3
hsa-miR-1237-3pPRKAA2, CDC37, CCND2, HSP90AA1, ITGB6, PPP2R5D, COL6A6, GNG10, IL4R, EIF4E, IL2RA, CHRM2, THBS3, COL6A3, PIK3CA, VWF, FN1, TNR, FGF1
hsa-miR-484CREB3L3, LAMB3, PPP2R5D, IFNAR1, CRTC2, PIK3CD, PDGFD, FGF8, FGF1, CSF1, PDGFA
hsa-miR-503-5pMYB, CCND2, IGF1R, GHR, EIF4E, PIK3R1, INSR, FGF2, AKT3, CCNE1, VEGFA, FGF7, CCND3, COL4A1
hsa-miR-147aCREB3L3, FGF14, BCL2, CDK6, FGF11, ITGA11, PDGFB, LPAR5, PDGFC, TNR, PDPK1, FGFR2, PPP2R1B
hsa-miR-106b-5pPHLPP2, RBL2, PDGFRA, ITGB8, PPP2CA, CREB5, MCL1, CCND1, EIF4E, PPP2R2A, F2R, FLT1, SOS1, CHRM2, EIF4E2, PDGFD, OSM, PKN2, CDKN1A, MAPK1, JAK1, GNB5, BCL2L11
hsa-miR-545-3pPRLR, PDGFB, IRS1, PDGFD, RPS6KB1
hsa-miR-204-5pCREB5, CCND2, FGF20, COL6A6, IL7R, CREB1, SOS1, COL5A3
hsa-miR-425-5pYWHAG, CREB1, SOS1, FGF9, G6PC2, IGF1, TNR, PTEN
hsa-miR-450a-5pCREB1
hsa-miR-140-5pPDGFRA, GNG12, EFNA4, FGF9, BCL2L1, CREB3L1, PKN2, GYS1, TNN
hsa-miR-30b-3pPHLPP2, GSK3B, COL27A1, EFNA5, FGF11, EIF4B, COL11A2, VWF, MAPK1
hsa-miR-185-3pPRKCA, GNG13, FGF17, CRTC2, SGK2, MAPK1
hsa-miR-185-5pPHLPP2, CDK2, CCND2, GNB3, IL7, ITGA1, CDK6, BRCA1, EIF4B, EIF4E2, RELA, VEGFA, PPP2R1B
hsa-miR-431-5pITGA1, LAMC2
hsa-miR-30c-1-3pPHLPP2, GSK3B, G6PC3, FGF11, SOS1
hsa-miR-920IL2RB, PCK2, ITGA3, CSF1
hsa-miR-671-5pNFKB1, THBS1, PPP2R5C, BRCA1, KIT, PRKAA1, SGK2
hsa-miR-622ATF2, CREB5, ITGB5, COL2A1, PTEN
hsa-miR-146b-5pPDGFRA, NRAS, COL4A3
hsa-miR-542-5pIKBKB
hsa-miR-219a-1-3pFASLG, IGF1, IL6R
hsa-miR-425-3pFN1
FoxO signalling pathwayhsa-miR-148b-3pSOS2, NRAS, CDKN1B, GADD45A, PIK3R3, S1PR1, SOS1, PRKAA1, IGF1, TGFB2, HOMER1, USP7, PTEN, BCL2L11
hsa-miR-671-5pSTK4, SMAD3, PRKAA1, SGK2, MAPK10
hsa-miR-503-5pCCND2, IGF1R, PIK3R1, INSR, AKT3
hsa-miR-665STAT3, TGFBR1, SETD7, NLK, AKT3, SOD2, CDKN1A
hsa-miR-132-3pSMAD2, PRKAA2, SIRT1, KRAS, NLK, SOS1, TGFB2, EP300, AKT3, SOD2, PIK3CA, FOXO3, PTEN, SGK3, MAPK1, C8orf44-SGK3
hsa-miR-212-3pSMAD2, PRKAA2, SIRT1, KRAS, NLK, SOS1, TGFB2, EP300, AKT3, SOD2, PIK3CA, FOXO3, PTEN, SGK3, MAPK1, C8orf44-SGK3
hsa-miR-1237-3pTGFBR1, FBXO32, SMAD2, PRKAA2, CCND2, MAPK11, BCL6, PIK3CA, USP7
hsa-miR-106b-5pBRAF, RBL2, STAT3, SLC2A4, HOMER2, CCND1, SMAD4, S1PR1, MAPK8, SOS1, SOD2, CDKN1A, MAPK1, CCNG2, TGFBR2, BCL2L11
hsa-miR-147aRAG1, CDKN2B, NLK, FOXG1, EP300, PDPK1, MAPK10
hsa-miR-34c-3pSMAD2, SOS1, HOMER1, PLK4, GRB2
hsa-miR-30c-1-3pSMAD2, STK4, G6PC3, SOS1, SOD2, HOMER1
hsa-miR-22-5pPIK3CB, RAF1, IL7R, SOS1, CSNK1E, MAP2K1, SGK3
hsa-miR-1244FBXO25, STAT3, MAPK8
hsa-miR-30e-5pIRS2, FBXO32, SMAD2, SIRT1, SETD7, CAT, IGF1R, KRAS, PIK3CD, SKP2, MAPK8, FOXG1, SOS1, IRS1, BCL6, FOXO3, ATG12, BCL2L11
hsa-miR-622SIRT1, PTEN
hsa-miR-32-5pIRS2, BRAF, RAG1, FBXO32, PRKAA2, PIK3CB, SETD7, KLF2, NLK, PIK3R3, PIK3CA, PTEN, SGK3, BCL2L11
hsa-miR-1205SLC2A4, SMAD2, BNIP3, PIK3R2, PIK3R5, CCND2, IKBKB, SMAD4, GRM1, BCL6, SOD2, MAPK1, BCL2L11
hsa-miR-425-5pMAPK8, GRM1, SOS1, G6PC2, IGF1, SOD, PTEN
hsa-let-7i-5pIRS2, TGFBR1, NRAS, CCND2, IGF1R, TNFSF10, HOMER2, MAPK8, INSR, IGF1, CDKN1A, IL10
hsa-miR-1-3pEGFR, IKBKB, SOS1, IGF1
hsa-miR-219a-1-3pSMAD2, FASLG, IGF1
hsa-miR-766-3pFBXO32, PIK3R5, IKBKB, NLK, SKP2
hsa-miR-582-3pCDKN1B, FOXO3, PTEN, CREBBP
hsa-miR-185-3pMAPK14, CDK2, CCND2
hsa-miR-326RAF1, SMAD3, IKBKB, SGK2
hsa-miR-30b-3pSETD7, SOD2, MAPK1
hsa-miR-146b-5pBRAF, NRAS, SMAD4, SOD2
hsa-miR-204-5pCCND2, IL7R, SOS1
hsa-miR-484PIK3CD, GRM1
hsa-miR-431-5pIRS2
hsa-miR-602FOXG1
hsa-miR-140-5pNLK, GABARAPL1, CSNK1E
hsa-miR-920PCK2
hsa-miR-542-5pIKBKB
hsa-miR-545-3pSTAT3, IRS1
hsa-miR-146b-3pRAF1
hsa-miR-185-5pMAPK14, CDK2, CCND2
Rap1 signalling pathwaysa-miR-106b-5pBRAF, PDGFRA, SIPA1L3, CRK, MAGI3, TIAM1, F2RL3, RAPGEF4, PFN2, F2R, FLT1, DOCK4, PDGFD, MAPK1, RAPGEF5, GRIN2B
hsa-let-7i-5pACTB, NRAS, IGF1R, FGF11, INSR, NGF, IGF1, PARD6B
hsa-miR-148b-3pNRAS, MRAS, PIK3R3, FLT1, KITLG, IGF1, CSF1, TEK, GRIN2B
hsa-miR-146b-5pBRAF, PDGFRA, NRAS, SIPA1L1, GRIN2B
hsa-miR-431-5pPLCB1, RAPGEF2, PRKD1, RAPGEF5
hsa-miR-503-5pCRK, IGF1R, RAPGEF2, PIK3R1, INSR, FGF2, AKT3, SIPA1L2, VEGFA, FGF7, PARD6B
hsa-miR-30e-5pMAGI2, ITGB1, EFNA3, IGF1R, FGF20, KRAS, RAPGEF4, PIK3CD, PFN2, RAPGEF2, FLT1, DOCK4, GNAQ, PDGFC, GNAI2, RASGRP3, GRIN2A, RAP1B, PDGFA, ITGB3
hsa-miR-1-3pACTB, MAGI2, MET, ADCY1, RAP1A, EGFR, CALM2, PFN2, RAPGEF2, FYB, IGF1, MAGI1, RAP1B, PDGFA
hsa-miR-1244LPAR3, RHOA, RAPGEF2, HGF, RAP1B
hsa-miR-582-3pRHOA, RAPGEF2, ANGPT1
hsa-miR-324-3pRAP1GAP
hsa-miR-920RAP1GAP, GNAO1, CSF1
hsa-miR-32-5pBRAF, PIK3CB, RAP1A, ADCY3, PIK3R3, GNAQ, PIK3CA, RAP1B
hsa-miR-22-5pMAGI2, PRKCA, PIK3CB, RAP1A, RAF1, BCAR1, CNR1, MAP2K1, GRIN2B
hsa-miR-30b-3pPRKCI, EFNA5, VAV2, FGF11, CDH1, RALB, MAPK1, GRIN2B
hsa-miR-185-3pPRKCA, FGF17, MAPK13, MAPK1
hsa-miR-147aFGF14, CRKL, ADCY2, CALM1, TIAM1, FGF11, PLCG1, ITGAM, PDGFB, LPAR5, PRKCB, PDGFC, FGFR2, GRIN2B
hsa-miR-545-3pCRK, PFN2, PDGFB, GNAQ, PDGFD
hsa-miR-484RAPGEF3, LAT, CALM1, RASGRP2, F2RL3, CDH1, PIK3CD, PDGFD, FGF8, GRIN1, FGF1, CSF1, PDGFA
hsa-miR-140-5pPDGFRA, ADCY7, RALA, EFNA4, DRD2, PFN2, RAPGEF6, FGF9, FYB, GNAQ, ADCY6
hsa-miR-326RAF1, MAGI3, MRAS, FGF9, FGF18, FGFR2, TLN1, FGF1, CSF1, GRIN2B
hsa-miR-185-5pADCY2, MAPK14, MAP2K6, SIPA1L1, CDC42, PRKCB, VEGFA, ADCY4, GRIN2B
hsa-miR-34c-3pMAGI3, MRAS, MAP2K6
hsa-miR-30c-1-3pMAP2K6, FGF11, GRIN2A, GRIN2B
hsa-miR-766-3pPIK3R5, RASGRP2, PRKCG, ADCY4, PLCB2, GRIN2B
hsa-miR-1205MET, RAPGEF1, CTNND1, PIK3R2, PIK3R5, RASGRP2, SPECC1L-ADORA2A, RAPGEF6, RASGRP3, MAPK1, PDGFRB, GRIN2B
hsa-miR-665RALA, FGF2, AKT3, MAGI1, KDR, GRIN2B
hsa-miR-204-5pFGF20, MAGI1, ADCY6
hsa-miR-425-5pCTNND1, FGF9, PRKCB, IGF1, GNAQ, GRIN2B
hsa-miR-132-3pCRK, ADCY3, MAGI3, KRAS, AKT3, PIK3CA, PRKD1, MAPK1, GRIN2B
hsa-miR-212-3pCRK, ADCY3, MAGI3, KRAS, AKT3, PIK3CA, PRKD1, MAPK1, GRIN2B
hsa-miR-1237-3pACTB, SPECC1L-ADORA2A, MAPK11, GNAI2, PIK3CA, FGF1, GNAI1, GRIN2B
hsa-miR-146b-3pRAF1, MLLT4, LPAR5, PRKCB
hsa-miR-671-5pTHBS1, RAPGEF6, KIT, ADCY4
hsa-miR-622PLCB1
hsa-miR-219a-1-3pIGF1, SIPA1L2
hsa-miR-602RASGRP2
hsa-miR-877-5pRASGRP3
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Watson, C.N.; Begum, G.; Ashman, E.; Thorn, D.; Yakoub, K.M.; Hariri, M.A.; Nehme, A.; Mondello, S.; Kobeissy, F.; Belli, A.; Di Pietro, V. Co-Expression Analysis of microRNAs and Proteins in Brain of Alzheimer’s Disease Patients. Cells 2022, 11, 163. https://doi.org/10.3390/cells11010163

AMA Style

Watson CN, Begum G, Ashman E, Thorn D, Yakoub KM, Hariri MA, Nehme A, Mondello S, Kobeissy F, Belli A, Di Pietro V. Co-Expression Analysis of microRNAs and Proteins in Brain of Alzheimer’s Disease Patients. Cells. 2022; 11(1):163. https://doi.org/10.3390/cells11010163

Chicago/Turabian Style

Watson, Callum N., Ghazala Begum, Emma Ashman, Daniella Thorn, Kamal M. Yakoub, Moustafa Al Hariri, Ali Nehme, Stefania Mondello, Firas Kobeissy, Antonio Belli, and Valentina Di Pietro. 2022. "Co-Expression Analysis of microRNAs and Proteins in Brain of Alzheimer’s Disease Patients" Cells 11, no. 1: 163. https://doi.org/10.3390/cells11010163

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