Biomarker profiling to determine clinical impact of microRNAs in cognitive disorders

Alzheimer’s disease (AD) and post-stroke cognitive impairment (PSCI) are the leading causes of progressive dementia related to neurodegenerative and cerebrovascular injuries in elderly populations. Despite decades of research, patients with these conditions still lack minimally invasive, low-cost, and effective diagnostic and treatment methods. MicroRNAs (miRNAs) play a vital role in AD and PSCI pathology. As they are easily obtained from patients, miRNAs are promising candidates for the diagnosis and treatment of these two disorders. In this study, we performed complete sequencing analysis of miRNAs from 24 participants, split evenly into the PSCI, post-stroke non-cognitive impairment (PSNCI), AD, and normal control (NC) groups. To screen for differentially expressed miRNAs (DE-miRNAs) in patients, we predicted their target genes using bioinformatics analysis. Our analyses identified miRNAs that can distinguish between the investigated disorders; several of them were novel and never previously reported. Their target genes play key roles in multiple signaling pathways that have potential to be modified as a clinical treatment. In conclusion, our study demonstrates the potential of miRNAs and their key target genes in disease management. Further in-depth investigations with larger sample sizes will contribute to the development of precise treatments for AD and PSCI.

After gene set enrichment analyses (GSEA), we restricted our display to the top 10 Gene Ontology (GO) terms per category and the principal 10 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Fig. 1C,D; Table S3 and S4).Target genes were significantly enriched in the biological processes of mesenchymal and fibrocyte proliferation, neurogenesis, mRNA processing, stem cell proliferation, and nervous system development.They were located in multiple organelles, including cytoplasmic stress particles, transcription inhibitor complexes, nuclide spots, nuclear specks, integral components of the endoplasmic reticulum membrane, and apical plasma membrane.In terms of molecular function, target genes were involved in protein cysteine S-acyltransferase activity, protein serine muscle enzyme activity regulation, insulin-like growth factor 1, and growth factor-binding hormone.The results of KEGG analysis suggested that target genes mainly participated in the phosphatidylinositol 3-kinase (PI3K)-Akt, MAPK, and FoxO signaling pathways, as well as in small cell lung cancer and prostate cancer (Fig. 1D).
We then constructed a protein-protein interaction (PPI) network of DE-miRNA target genes, comprising 364 gene/protein nodes and 655 non-redundant interacting edges (Fig. 2A).Analysis in MOCDE and CytoHubba yielded nine key genes: ATM, MYC, PTEN, CDKN1B, CCNB1, HIF1A, CDK2, BMI1, and IGF1R.Analysis of interactions between DE-miRNAs and key genes (Fig. 2B) revealed that a single DE-miRNA has multiple targets, while one gene can affect several downstream signaling pathways.

Bioinformatics analysis of AD and PSCI
We identified 2242 miRNAs that were common across AD and PSCI groups (Table S2; Fig. 3A).Of these, 22 were DE-miRNAs (10 upregulated and 12 dysregulated) (Fig. 3B).Using biogenic analysis, we then conducted GSEA on DE-miRNA target genes (Table S5 and S6); the top 10 GO terms per category and top 10 KEGG pathways are shown (Fig. 3C,D).Target genes participate in the positive regulation of cellular catabolic processes, nucleartranscribed mRNA poly(A) tail shortening and viral life cycle, negative regulation of cellular macromolecule biosynthetic processes, and cellular amide metabolic processes.Additionally, they are involved in the cellular  (C) Gene Ontology (GO) enrichment results between AD and NC.P values are on the X-axis (significance of associations between gene sets and GO terms).The Y-axis displays GO terms.(D) The KEGG [47][48][49] pathway analysis between AD and NC.Gene ratio is proportion of genes in a pathway to total genes analyzed (x-axis).
Signaling pathways are on the y-axis.Bubble size indicates the number of genes enriched in a given pathway.
Bubble color reflects significance level (bluer is smaller).P-values are log-transformed.
components of growth cones, asymmetric synapses, neuron-to-neuron synapses, and postsynaptic specialization.Finally, the target genes play a role in regulating enzyme inhibitor activity, protein kinase activity, kinase activity, protein phosphatase inhibitor activity, DNA-binding transcription activator activity, and molecular adaptor activity.The results of KEGG analysis indicated that these genes are mainly involved in cell catabolism, senescence, and endocytosis, as well as the Hippo and p53 signaling pathways.Key target genes of these DE-miRNAs are CDKN1A, CDK6, and CCND2 (Fig. 4A,B).

Bioinformatics analysis of PSCI and PSNCI
We identified 2238 common miRNAs across the PCI and PSNCI groups (Table S2; Fig. 5A).Of these, 22 DE-miRNAs were found (11 upregulated and 11 dysregulated) (Fig. 5B), but data for hsa-miR-10401-5p was absent from the TargetScan Human database, resulting in 21 DE-miRNAs used for target gene prediction (Table S7 and S8).Only the top 10 GO terms and the top 10 KEGG pathways are displayed (Fig. 5C,D).The results of GO analysis showed that target genes were enriched in protein dephosphorylation-related biological progression.They are localized to the synaptic vesicle membrane, presynaptic active zone cytoplasmic component, and cell cortex region.Additionally, they participate in metabolic progresses related to small GTPase binding, GDP binding, GTPase activity, acidic amino acid transmembrane transporter activity, and hormone binding.

Discussion
By collecting data from patients with different dementia subtypes and comparing plasma miRNA expression, our research successfully identified novel miRNAs related to AD and PSCI.Moreover, we linked those miRNAs to their target genes, providing considerable clarity on the molecular mechanisms underlying AD and PSCI.
With the development of genome-wide association studies (GWAS) and single-cell sequencing technologies, miRNAs are increasingly relevant as biomarkers for non-invasive diagnosis and treatment.Different miRNA combinations have been investigated for their suitability in diagnosing cognitive disorders, including AD and PSCI 50 .Similarly, our target gene prediction and GSEA identified 14 DE-miRNAs.Of these, hsa-miR-548as-5p, hsa-miR-5186, and hsa-miR-449b-5p were differentially expressed across all three patient groups.Functional analysis with KEGG demonstrated that DE-miRNAs participate in AD and PSCI progression through their target genes in the cell cycle, cell senescence processes, as well as FoxO, PI3K, Hippo, and p53 signaling pathways.We also identified several novel DE-miRNAs, including hsa-miR-548as-5p, hsa-miR-519c-3p, hsa-miR-6883-5p, hsa-miR-5186, hsa-miR-548ak, and hsa-miR-147a.The remaining DE-miRNAs have been implicated in infectious diseases 51 , endocrine diseases 52 and neoplastic diseases [53][54][55][56] , but not in CNS diseases or any cognitive disorders.Notably, changes in cellular developmental pathways do not yield the same outcome in every disease;  [47][48][49] pathway analysis between AD and PSCI.Gene ratio is proportion of genes in a pathway to total genes analyzed (x-axis).Signaling pathways are on the y-axis.Bubble size indicates the number of genes enriched in a given pathway.Bubble color reflects significance level (bluer is smaller).P-values are log-transformed.in cancers, they cause rapid cell growth and proliferation, whereas in CNS disorders, they accelerate neuron aging and death.Understanding the specific characteristics of neoplastic diseases will be useful for producing novel interventions and targets in AD and PSCI 57 .
In AD, Aβ deposition and tau neurofibrillary entanglement are classic pathological changes that lead to amyloid precursor deposition, autophagy, apoptosis, and mitochondrial dysfunction.Interestingly, post-stroke degenerative gene expression in hippocampal neurons cause similar pathological changes 58 , suggesting that gene expression patterns may be the source of mechanistic overlap between AD and PSCI.In particular, p53 regulation of miRNAs is responsive to stressors, thus influencing gene stability and protein function; both Aβ deposition and tau hyperphosphorylation in the CNS can be attributed to p53 [59][60][61][62] .Indeed, the p53-miRNA interaction has been widely researched as a target for treating neurodegenerative diseases.Our study identified hsa-miR-548as-5p, hsa-miR-5186, hsa-miR-548ak, hsa-miR-147a, hsa-miR-6868-3p, and hsa-miR-449b-5p as part of the p53-related signaling pathway.
Research on other diseases and related signaling mechanisms have helped us understand the pivotal roles of several DE-miRNAs in the occurrence of AD and PSCI.First, hsa-miR-187-5p, hsa-miR-3157-5p, and hsa-miR-519c-3p-upregulated in the AD group-are implicated in the PI3K signaling pathway.This is a lipid kinase that participates in the intracellular signaling cascade, as well as cell silencing, survival, and growth under physiological and disease states 63,64 .Mesenchymal stem cell-derived exosomal miR-223 can protect neurons from apoptosis through protein tyrosine phosphatase (PTEN), which targets the PI3K/Akt pathway 65 .Additionally, hsa-miR-4655-5p and hsa-miR-3157-5p are involved in regulating mitogen-activated protein kinase (MAPKs) pathways.In mammals, MAPKs include c-Jun NH2-terminal kinase (JNK), p38 MAPK, and extracellular signal-regulated kinase (ERK).The pathway is implicated in cancer and neurodegenerative diseases through its regulation of cell proliferation, differentiation, and intrinsic immunity 66 .In particular, p38 MAPK affects microglia-and astrocyte-mediated neuroinflammation.This process has been linked to RNase III enzyme Drosha, which accelerates miRNA maturation.Evaluation of postmortem tissues from AD patients and AD rat models suggests that Aβ oligomers induce p38 MAPK-dependent phosphorylation of Drosha, leading to its bidirectional distribution between the neuronal cytoplasm and nucleus.Drosha overexpression can protect neurons from Aβ oligomeric-induced apoptosis 67 .Furthermore, MAPK can affect glucose metabolism in the CNS, promote catecholamine uptake, regulate neuronal growth, and alter neurotransmitter GABA expression via modulating insulin signal transduction pathways 68 .Triggering these processes can potentially improve cognitive function in patients with AD and PSCI.
Key genes were screened to further explore the mechanisms underlying AD and PSCI.The tumor suppressor PTEN is regulated by hsa-miR-519c-3p and mediates disease progression through activating the PI3K/serinethreonine kinase (AKT) signaling pathway.Pathological tau protein and intracellular cholesterol enrichment www.nature.com/scientificreports/trigger premature PTEN activation, accelerating the microglia toxicity that occurs when synapses or neurons are exposed to phosphoserine apoptotic signals 69,70 .In vitro and in vivo AD models have confirmed that PTEN The KEGG [47][48][49] pathway analysis between PSCI and PSNCI.Gene ratio is proportion of genes in a pathway to total genes analyzed (x-axis).Signaling pathways are on the y-axis.Bubble size indicates the number of genes enriched in a given pathway.Bubble color reflects significance level (bluer is smaller).P-values are log-transformed.
inhibition can effectively alleviate synaptic and cognitive damage 71 .In particular, the isoform PTENα can affect olfactory behavior in AD mice and regulate the endocytosis of olfactory bulb neurons through direct dephosphorylation of endocytosis proteins, a phenomenon also observed in patients with Parkinson's disease and DLB 72 .In addition to PTENα, PTEN-induced kinase 1 (PINK1) controls the specific clearance of dysfunctional or excess mitochondria through selective autophagy regulation 55 .PINK1 overexpression can reverse abnormal changes in mitochondrial dynamics, autophagy defects, and abnormal energy metabolism in the hippocampus.Other functions of PINK1 include upregulating antioxidant proteins to lower oxidative stress and inhibiting stress-induced neuronal apoptosis via the Nrf2 pathway 73 .In an AD mouse model, plasma PTEN-PDZ complex is relatively stable and crosses the BBB to become PTEN, resulting in synaptic protection and improved cognitive function.These two characteristics (plasma stability and BBB permeability) make the PTEN-PDZ complex a promising candidate for parenteral (intravenous and intramuscular) and naso-cerebral administration 74 .
Transcription factors regulate miRNA production in response to external stimuli and are especially active in the brain 75 .Under ischemia and hypoxia, transcription factors inactivate neurons within a few minutes, causing serious brain dysfunction.Sensitivity to damage increases with increasing age and greater impairment to the energy transport capacity of cells.In this study, HIF1A (regulated by hsa-miR-519c-3p) encodes the alpha subunit of hypoxia-inducible factor-1 (HIF-1).As a key transcription factor, HIF-1 is an important mediator  www.nature.com/scientificreports/ of cellular and systemic homeostasis.HIF-1α upregulates BACE1 and γ-secretase in ischemic anoxic mouse models and primary neuron culture, inducing Aβ production during brain tissue hypoperfusion or hypoxia 76 .Likewise, the mTOR-HIF1α complex induces acute inflammation in microglia exposed to Aβ. Metabolic reprogramming from oxidative phosphorylation to glycolysis eventually causes abnormal metabolism, cytokine secretion, and phagocytosis 77,78 .In addition to its effect on Aβ, HIF-1α also influences tau neurofibrillary tangles under chronic hypoxia.In SD mouse hippocampal neurons after hypoxia treatment, an increase in HIF-1α is accompanied by tau protein hyperphosphorylation, along with a decrease in protein phosphatase 2A and leucin carboxymethyltransferase 1.These molecular changes are linked to cognitive impairment among SD mice and were verified in primary hippocampal neurons and C6/tau cells 79 .Thus, inhibiting HIF-1α may alleviate hypoxiarelated cognitive impairment.Moreover, HIF1α could also cause remission of some PSCI symptoms, specifically irreversible white matter and neuronal damage.The mechanism involves HIF1α activation of the PKA pathway to regulate angiogenesis induced by vascular endothelial growth factor and erythropoietin 72,80 .
Another important target gene we identified here is the neuronal gene BMI1 81 .Whole-genome sequencing on samples from patients with late-onset AD revealed that BMI expression decreased, in conjunction with changes to Aβ and P-tau 82 .BMI1 inhibits the transcription of tau-associated tubulin, and knocking out BMI1 in neurons leads to Aβ secretion/deposition, P-Tau aggregation, and neurodegenerative changes 83 .A clinical cohort study comprising 1565 patients with AD found a significant correlation between BMI1 expression and Aβ1-42 levels in cerebrospinal fluid 83 .Taken together, available data strongly implicate BMI1 as a potential therapeutic target for AD.
The widespread use of miRNAs as a non-invasive screening method has revealed that a single miRNA or miRNA complex in plasma possesses differential diagnostic efficacy.A key obstacle in identifying relevant target miRNAs is the considerable heterogeneity in miRNA expression.Our study identified several miRNAs that are entirely novel or have only been minimally documented in a few publications associated with nonneurological disorders.Thus, little data are available on whether they can truly serve as biomarkers of PSCI or AD.In AD, bidirectional interactions between neuroinflammatory OS and the proteins Aβ and tau could also influence miRNA expression.Moreover, key miRNA synthesis enzymes such as Drosha, Dicer, and AGO2 are downregulated by stressful conditions such as hypoxia, causing aberrant miRNA expression that may interfere with diagnostic accuracy [84][85][86] .
One notable limitation of plasma miRNA sequencing is that it only reflects miRNA expression of the disease stage at sampling.While such data can be applied to differential diagnosis and prognosis, it obviously does not reflect miRNA expression patterns throughout the disease course.In this study, miRNA expression represented the post-symptomatic state because only clinically diagnosed participants were included.However, DE-miRNAs have considerable potential for diagnosis before requiring patients to undergo expensive and invasive examinations such as PET scans and lumbar punctures.Beyond an earlier diagnosis, DE-miRNA data also provides more information to clinicians regarding specific disease types, suggesting major benefits in addressing current shortcomings.To overcome the obstacles of heterogeneous miRNA expression, samples at different stages of disease progression are needed to identify common regulatory miRNAs that can then be therapeutically manipulated 87 .
Therapies based on miRNAs are currently in the form of mimics or inhibitors to influence downstream signaling pathways.However, further research is needed to determine optimal drug delivery in humans.In the future, anti-microRNA (AM) strategies could be implemented for the amelioration and clinical management of AD.This novel therapeutic approach restores multiple miRNA-regulated gene targets via the use of selectively stabilized AM species.Moreover, only a relatively small amount of miRNA families need to be modulated or neutralized to re-establish neuronal homeostasis in the AD-affected brain 88 .
In conclusion, through high-throughput sequencing and bioinformatics, we identified novel miRNAs, their key target genes, and highlighted potential mechanisms of their involvement in AD and PSCI.Importantly, our findings provided an empirical basis for using miRNAs as peripheral biomarkers and therapeutic targets.

Participants
The study selected 18 patients admitted to the Department of Neurology at the First Hospital of Jilin University between 2021 and 2022.Based on etiology and MoCA, patients were divided into three groups (n = 6 per group): AD, PSCI, and post-stroke non-cognitive impairment (PSNCI).Six normal controls (NC) were also included.
Patients were included in the PSCI or PSNCI group if they were 50-80 years of age, met the WHO diagnostic criteria for acute ischemic stroke (AIS), and could complete the relevant scale assessment.They were further subdivided into PSNCI if MoCA ≥ 22 and PSCI if MoCA < 22 89 .Patients were included in AD group if they met the NINCDS-ADRDA diagnostic criteria and confounding factors were excluded.Finally, spouses of the patients were included as NC, matched by age, sex, education and previous medical history.
Individuals were excluded if they exhibited other CNS conditions that cause cognitive impairment, including metabolic encephalopathy, Parkinson's syndrome, Huntington's disease, subdural hematoma, normal cranial pressure hydrocephalus, brain tumors, and brain trauma.Displaying other systemic conditions linked to cognitive decline also resulted in exclusion; these included hypothyroidism, vitamin (B12, folic acid, niacin) deficiency, severe anemia, liver or renal insufficiency, hyponatremia, hypocalcemia, neurosyphilis, HIV infection, and alcohol/drug abuse.Finally, disorders or any other characteristics that negatively impacted the ability to perform tasks and scale operations led to exclusion.These included mental disorders (e.g., depression and schizophrenia), nuclear magnetic contraindications, refusal of blood collection, serious diseases of vital organs, and sensory impairments (e.g., blindness, aphasia, deafness).

Statistical analysis
Statistical analyses were performed in IBM SPSS Statistics version 25 (IBM, Armonk, NY, USA) and R version 4.3.2(R Foundation for Statistical Computing, Vienna, Austria).Sociodemographic and neuropsychological characteristics were subjected to descriptive analyses.Continuous variables were presented as mean ± SD or median (interquartile range), and categorical variables were presented as frequencies (percentages).Betweengroup differences were compared with chi-square tests, one-way ANOVA, or Kruskal-Wallis rank sum test.
Significance was set at P < 0.05.

Microarray assay of miRNA
Human tissue specimens were collected from the Department of Biobank, Division of Clinical Research, for RNA extraction using TRIzol reagent (Invitrogen, Carlsbad, CA, USA).MiRNA sequencing was performed using an Illumina NextSeq 500 sequencing platform (San Diego, CA, USA).Double-stranded cDNA was synthesized using the dUTP method and high-fidelity PCR polymerase to ensure strand specificity.Constructed libraries were assessed in an Agilent 2100 Bioanalyzer and subjected to quantitative real-time PCR (qRT-PCR).
Based on quantification results, libraries were mixed in equal amounts and used for sequencing.Singlestranded DNA (1.8 pM) generated from denaturing with 0.1 M NaOH were loaded onto the reagent cartridge.Sequencing was performed for 50 cycles on a NextSeq system using the NextSeq 500/550 V2 kit (#FC-404-2005, Illumina).The Solexa pipeline v1.8 (Off-Line Base Caller v1.8) was used for image analysis and base calling.Sequencing quality was examined with FastQC.
Expression profiles of miRNAs were calculated based on mapped read counts.Differentially expressed miRNAs (P < 0.05, |log2FC|≥ 1) were screened in R package edgeR 90 , then matched to reference sequences in miRDeep2 91 .The expression of miRNAs was represented as counts per million mapped reads (CPM) after transformation.
Functional analyses with GO 95 and KEGG 96 were visualized using R packages ClusterProfiler and ggplot2.Significant enrichment (P < 0.05 with Benjamin-Hochberg corrections) was determined with Fisher's exact test.Next, the PPI network of target genes was generated in STRING (https:// cn.String-db.org/) and visualized in Cytoscape (v3.9.1) 97 .Target genes were processed in MCODE (node score cutoff: 0.2, K-Core: 2, Max.Depth: 100) and CytoHubba (MCC algorithm) to identify key genes (those that passed both sets of conditions).Finally, a network of signaling pathways involving DE-miRNAs and their key genes was constructed.

Ethical approval and informed consent
All methods were performed in accordance with the relevant guidelines and regulations (For ex.Declaration of Helsinki).The studies involving human participants were reviewed and approved by the ethics committee of The First Hospital of Jilin University (ethics number: 19K023-003).All subjects provided written informed consent to participate in this study.The sequencing information of all participants has been filed with the China Center for Human Genetic Resources Management.

263 Figure 1 .
Figure 1.Bioinformatics analysis comparing Alzheimer's disease (AD) and normal controls (NC).(A) Sequencing cluster map between the AD and NC groups.Different colors indicate relative expression levels (log2-transformed).Blue, below-mean expression; red, above-mean expression.The colored bar at the top of the panel represents participating groups (blue represents AD and red represents NC).The colored bar on the right side of the panel indicates divisions based on K-means.(B) Volcano plot of DE-miRNAs between AD and NC groups.On the x-axis, the dotted line indicates DE-miRNAs that satisfied the |log2FC|≥ 1 cut-off.On the y-axis, the dotted line indicates DE-miRNAs that satisfied P < 0.05.Red, highly expressed; purple, lowly expressed.(C)Gene Ontology (GO) enrichment results between AD and NC.P values are on the X-axis (significance of associations between gene sets and GO terms).The Y-axis displays GO terms.(D) The KEGG[47][48][49] pathway analysis between AD and NC.Gene ratio is proportion of genes in a pathway to total genes analyzed (x-axis).Signaling pathways are on the y-axis.Bubble size indicates the number of genes enriched in a given pathway.Bubble color reflects significance level (bluer is smaller).P-values are log-transformed.

Figure 2 .
Figure 2. Target genes of DE-miRNAs between AD and NC.(A) STRING visualization of the protein-protein interaction (PPI) network.The darker the color, the larger the font, and the larger the background area, the greater the weight of this gene in the PPI network and its correlation with other genes.(B) DE-miRNAs plus their key target genes.

Figure 3 .
Figure 3. Bioinformatics analysis comparing Alzheimer's disease (AD) and post-stroke cognitive impairment (PSCI).(A) Sequencing cluster map between the AD and PSCI groups.Different colors indicate relative expression levels (log2-transformed).Blue, below-mean expression; red, above-mean expression.The colored bar at the top of the panel represents participating groups (blue represents AD and red represents PSCI).The colored bar on the right side of the panel indicates divisions based on K-means.(B) Volcano plot of DE-miRNAs between AD and PSCI groups.On the x-axis, the dotted line indicates DE-miRNAs that satisfied the |log2FC|≥ 1 cut-off.On the y-axis, the dotted line indicates DE-miRNAs that satisfied P < 0.05.Red, highly expressed; purple, lowly expressed.(C) Gene Ontology (GO) enrichment results between AD and PSCI.P values are on the X-axis (significance of associations between gene sets and GO terms).The Y-axis displays GO terms.(D) The KEGG[47][48][49] pathway analysis between AD and PSCI.Gene ratio is proportion of genes in a pathway to total genes analyzed (x-axis).Signaling pathways are on the y-axis.Bubble size indicates the number of genes enriched in a given pathway.Bubble color reflects significance level (bluer is smaller).P-values are log-transformed.

Figure 4 .
Figure 4. Target genes of DE-miRNAs between AD and PSCI.(A) STRING visualization of the protein-protein interaction (PPI) network.The darker the color, the larger the font, and the larger the background area, the greater the weight of this gene in the PPI network and its correlation with other genes.(B) DE-miRNAs plus their key target genes.

Figure 5 .
Figure 5. Bioinformatics analysis comparing post-stroke cognitive impairment (PSCI) and normal poststroke non-cognitive impairment (PSNCI).(A) Sequencing cluster map between the PSCI and PSNCI groups.Different colors indicate relative expression levels (log2-transformed).Blue, below-mean expression; red, abovemean expression.The colored bar at the top of the panel represents participating groups (blue represents PSCI and red represents PSNCI).The colored bar on the right side of the panel indicates divisions based on K-means.(B) Volcano plot of DE-miRNAs between PSCI and PSNCI groups.On the x-axis, the dotted line indicates DE-miRNAs that satisfied the |log2FC|≥ 1 cut-off.On the y-axis, the dotted line indicates DE-miRNAs that satisfied P < 0.05.Red, highly expressed; purple, lowly expressed.(C) Gene Ontology (GO) enrichment results between PSCI and PSNCI.P values are on the X-axis (significance of associations between gene sets and GO terms).The Y-axis displays GO terms.(D) The KEGG[47][48][49] pathway analysis between PSCI and PSNCI.Gene ratio is proportion of genes in a pathway to total genes analyzed (x-axis).Signaling pathways are on the y-axis.Bubble size indicates the number of genes enriched in a given pathway.Bubble color reflects significance level (bluer is smaller).P-values are log-transformed.

Figure 6 .
Figure 6.Target genes of DE-miRNAs between PSCI and PSNCI.(A) STRING visualization of the proteinprotein interaction (PPI) network.The darker the color, the larger the font, and the larger the background area, the greater the weight of this gene in the PPI network and its correlation with other genes.(B) DE-miRNAs plus their key target genes.

Figure 7 .
Figure 7. Venn diagrams representing DE-miRNAs and target genes common across all three groups with cognitive impairments.(A) DE-miRNAs between patients with different cognitive impairments (AD, PSCI, PSNCI) and healthy controls (NC).Red circle, AD and NC; orange circle, AD and PSCI; green circle, PSCI and PSNCI.(B) Differentially expressed target genes between patients with different cognitive impairments and healthy controls.Blue circle, AD and NC; purple circle, AD and PSCI; yellow circle, PSCI and PSNCI.