Alzheimer’s disease (AD) is a neurodegenerative disorder characterized primarily by a decline in cognitive function. However, the etiopathogenesis of AD is unclear. N6-methyladenosine (m6A) is abundant in the brain, and it is interesting to explore the relationship between m6A and AD causes. In this paper, the gene expression of METTL3 and NDUFA10 were found to correlate with the Mini-mental State Examination (MMSE), which is a clinical indicator of the degree of dementia. METTL3 is involved in post-transcriptional methylation and the formation of m6A. NDUFA10 encodes the protein with NADH dehydrogenase activity and oxidoreductase activity in the mitochondrial electron transport chain. The following three characteristics were observed in this paper: 1. The lower the expression level of NDUFA10, the smaller the MMSE, and the higher the degree of dementia. 2. If the expression level of METTL3 dropped below its threshold, the patient would have a risk of AD with a probability close to 100%, suggesting a basic necessity for m6A to protect mRNA. 3. The lower the expression levels of both METTL3 and NDUFA10, the more likely the patient would suffer from AD, implying the coherence between METTL3 and NDUFA10. Regarding the above discovery, the following hypothesis is presented: METTL3 expression level is downregulated, then the m6A modification level of NDUFA10 mRNA is also decreased, thereby reducing the expression level of NDUFA10-encoded protein. Furthermore, the abnormal expression of NDUFA10 contributes to the assembly disorder of mitochondrial complex I and affects the process of the electron respiratory chain, with the consequent development of AD. In addition, to confirm the above conclusions, the AI Ant Colony Algorithm was improved to be more suitable for discovering the characteristics of AD data, and the SVM diagnostic model was applied to mine the coherent effects on AD between METTL3 and NDUFA10. In conclusion, our findings suggest that dysregulated m6A leads to altered expression of its target genes, thereby affecting AD’s development.

BackgroundAlzheimer’s disease (AD) is the most common form of age-related neurodegenerative disease. Unfortunately, due to the complexity of pathological types and clinical heterogeneity of AD, there is a lack of satisfactory treatment for AD. Previous studies have shown that microRNAs and transcription factors can modulate genes associated with AD, but the underlying pathophysiology remains unclear.MethodsThe datasets GSE1297 and GSE5281 were downloaded from the gene expression omnibus (GEO) database and analyzed to obtain the differentially expressed genes (DEGs) through the “R” language “limma” package. The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected. Next, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis for the key gene modules were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, for the GSE150693 dataset, the “R” package “survivation” was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method. We also performed regression analysis using least absolute shrinkage and selection operator (Lasso)-Cox to construct and validate prognostic features associated with the four key genes using different databases. We also tried to find drugs targeting key genes through DrugBank database.ResultsGO and KEGG enrichment analysis showed that DEGs were mainly enriched in pathways regulating chemical synaptic transmission, glutamatergic synapses and Huntington’s disease. In addition, 10 hub genes were selected from the PPI network by using the algorithm Between Centrality. Then, four core genes (TBP, CDK7, GRM5, and GRIA1) were selected by correlation with clinical information, and the established model had very good prognosis in different databases. Finally, hsa-miR-425-5p and hsa-miR-186-5p were determined by COX regression, AD transformation status and aberrant miRNAs.ConclusionIn conclusion, we tried to construct a network in which miRNAs and transcription factors jointly regulate pathogenic genes, and described the process that abnormal miRNAs and abnormal transcription factors TBP and CDK7 jointly regulate the transcription of AD central genes GRM5 and GRIA1. The insights gained from this study offer the potential AD biomarkers, which may be of assistance to the diagnose and therapy of AD.

Alzheimer’s disease (AD) is a progressive neurological disease that worsens with time. The hallmark illnesses include extracellular senile plaques caused by β-amyloid protein deposition, neurofibrillary tangles caused by tau protein hyperphosphorylation, and neuronal loss accompanying glial cell hyperplasia. Noncoding RNAs are substantially implicated in related pathophysiology, according to mounting data. However, the function of these ncRNAs is mainly unclear. Circular RNAs (circRNAs) include many miRNA-binding sites (miRNA response elements, MREs), which operate as miRNA sponges or competing endogenous RNAs (ceRNAs). The purpose of this study was to look at the role of circular RNAs (circRNAs) and microRNAs (miRNAs) in Alzheimer’s disease (AD) as possible biomarkers. The Gene Expression Omnibus (GEO) database was used to obtain an expression profile of Alzheimer’s disease patients (GSE5281, GSE122603, GSE97760, GSE150693, GSE1297, and GSE161435). Through preliminary data deletion, 163 genes with significant differences, 156 miRNAs with significant differences, and 153 circRNAs with significant differences were identified. Then, 10 key genes, led by MAPT and AP2M1, were identified by the mediation center algorithm, 34 miRNAs with obvious prognosis were identified by the cox regression model, and 16 key circRNAs were selected by the database. To develop competitive endogenous RNA (ceRNA) networks, hub circRNAs and mRNAs were used. Finally, GO analysis and clinical data verification of key genes were carried out. We discovered that a down-regulated circRNA (has_circ_002048) caused the increased expression of numerous miRNAs, which further inhibited the expression of a critical mRNA (AP2M1), leading to Alzheimer’s disease pathology. The findings of this work contribute to a better understanding of the circRNA-miRNA-mRNA regulating processes in Alzheimer’s disease. Furthermore, the ncRNAs found here might become novel biomarkers and potential targets for the development of Alzheimer’s drugs.

BackgroundAlzheimer’s disease (AD) is the most common cause of dementia and cognitive decline, while its pathological mechanism remains unclear. Tauopathies is one of the most widely accepted hypotheses. In this study, the molecular network was established and the expression pattern of the core gene was analyzed, confirming that the dysfunction of protein folding and degradation is one of the critical factors for AD.MethodsThis study analyzed 9 normal people and 22 AD patients’ microarray data obtained from GSE1297 in Gene Expression Omnibus (GEO) database. The matrix decomposition analysis was used to identify the correlation between the molecular network and AD. The mathematics of the relationship between the Mini-Mental State Examination (MMSE) and the expression level of the genes involved in the molecular network was found by Neural Network (NN). Furthermore, the Support Vector Machine (SVM) model was for classification according to the expression value of genes.ResultsThe difference of eigenvalues is small in first three stages and increases dramatically in the severe stage. For example, the maximum eigenvalue changed to 0.79 in the severe group from 0.56 in the normal group. The sign of the elements in the eigenvectors of biggest eigenvalue reversed. The linear function of the relationship between clinical MMSE and gene expression values was observed. Then, the model of Neural Network (NN) is designed to predict the value of MMSE based on the linear function, and the predicted accuracy is up to 0.93. For the SVM classification, the accuracy of the model is 0.72.ConclusionThis study shows that the molecular network of protein folding and degradation represented by “BAG2-HSC70-STUB1-MAPT” has a strong relationship with the occurrence and progression of AD, and this degree of correlation of the four genes gradually weakens with the progression of AD. The mathematical mapping of the relationship between gene expression and clinical MMSE was found, and it can be used in predicting MMSE or classification with high accuracy. These genes are expected to be potential biomarkers for early diagnosis and treatment of AD.