Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer's Disease

Deciphering the role of miRNAs in Alzheimer's disease: Predictive targeting and pathway modulation - A systematic review

Alzheimer's Disease (AD), a multifaceted neurodegenerative disorder, is increasingly understood through the regulatory lens of microRNAs (miRNAs). This review comprehensively examines the pivotal roles of miRNAs in AD pathogenesis, shedding light on their influence across various pathways. We delve into the biogenesis and mechanisms of miRNAs, emphasizing their significant roles in brain function and regulation. The review then navigates the complex landscape of AD pathogenesis, identifying key genetic, environmental, and molecular factors, with a focus on hallmark pathological features like amyloid-beta accumulation and tau protein hyperphosphorylation. Central to our discussion is the intricate involvement of miRNAs in these processes, highlighting their altered expression patterns in AD and subsequent functional implications, from amyloid-beta metabolism to tau pathology, neuroinflammation, oxidative stress, and synaptic dysfunction. The predictive analysis of miRNA targets using computational methods, complemented by experimental validations, forms a crucial part of our discourse, unraveling the contributions of specific miRNAs to AD. Moreover, we explore the therapeutic potential of miRNAs as biomarkers and in miRNA-based interventions, while addressing the challenges in translating these findings into clinical practice. This review aims to enhance understanding of miRNAs in AD, offering a foundation for future research directions and novel therapeutic strategies.

Exploration of the shared gene signatures and molecular mechanisms between Alzheimer's disease and intracranial aneurysm

Although Alzheimer's disease (AD) and intracranial aneurysm (IA) were two different types of diseases that occurred in the brain, ruptured IA (RIA) survivors may experience varying degrees of cognitive dysfunction. Neither AD nor IA is easily recognizable by an early onset so that the incidence of adverse clinical outcomes would be on the rise. Therefore, we focused on the exploration of the shared genes and molecular mechanisms between AD and IA, which would be significant for the efficiency of co-screening and co-diagnosis. Two GEO datasets were selected for the weighted gene co-expression network analysis (WGCNA) and differentially expressed gene screening, obtaining 78 overlapped genes. Next, 9 hub genes were identified by the protein-protein interaction network, including PIK3CA, GAB1, IGF1R, PLCB1, PGR, PDGFRB, PLCE1, FGFR3, and SYNJ1. The interactions among the hub genes, miRNA, and TFs were also explored. Meanwhile, we performed GO and KEGG pathway enrichment analyses for the results of WGCNA and hub genes, which showed that the Ras signaling and Rap1 signaling were the main shared pathogenesis. In conclusion, the present bioinformatics analysis revealed that AD and IA had the shared genes and molecular mechanisms, and these outcomes were associated with inflammation and calcium homeostasis, which could provide research clues for further studies.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Comorbidity Genes of Alzheimer's Disease and Type 2 Diabetes Associated with Memory and Cognitive Function

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction network identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed and relevant evidence summarized. Gene information will be useful in the discovery of biomarkers and the identification of tentative therapeutic targets for AD and T2DM.

Unveiling the Genetic Complexity of Teratozoospermia: Integrated Genomic Analysis Reveals Novel Insights into lncRNAs' Role in Male Infertility

Male infertility is a global health issue, affecting over 20 million men worldwide. Genetic factors are crucial in various male infertility forms, including teratozoospermia. Nonetheless, the genetic causes of male infertility remain largely unexplored. In this study, we employed whole-genome sequencing and RNA expression analysis to detect differentially expressed (DE) long-noncoding RNAs (lncRNAs) in teratozoospermia, along with mutations that are exclusive to teratozoospermic individuals within these DE lncRNAs regions. Bioinformatic tools were used to assess variants' impact on lncRNA structure, function, and lncRNA-miRNA interactions. Our analysis identified 1166 unique mutations in teratozoospermic men within DE lncRNAs, distinguishing them from normozoospermic men. Among these, 64 variants in 23 lncRNAs showed potential regulatory roles, 7 variants affected 4 lncRNA structures, while 37 variants in 17 lncRNAs caused miRNA target loss or gain. Pathway Enrichment and Gene Ontology analyses of the genes targeted by the affected miRNAs revealed dysregulated pathways in teratozoospermia and a link between male infertility and cancer. This study lists novel variants and lncRNAs associated for the first time with teratozoospermia. These findings pave the way for future studies aiming to enhance diagnosis and therapy in the field of male infertility.

A blood mRNA panel that differentiates Alzheimer's disease from other dementia types

BACKGROUND

Messenger RNAs (mRNAs) have been reported to be associated with Alzheimer's disease (AD). In this study, we investigated whether plasma-based mRNAs could distinguish AD from cognitively normal controls and other types of dementia, including vascular dementia (VaD), Parkinson's disease dementia (PDD), behavioral variant frontotemporal dementia (bvFTD), and dementia with Lewy body (DLB).

METHODS

Plasma mRNA expression was measured in three independent datasets. Dataset 1 (n = 40; controls, 20; AD, 20) was used to identify the differentially expressed mRNAs. Dataset 2 (n = 122; controls: 60; AD: 62) was used to develop a diagnostic AD model using an mRNA panel. Furthermore, we applied the model to Dataset 3 (n = 334; control, 57; AD, 58; VaD, 55; PDD, 54; bvFTD, 55; DLB, 55) to verify its ability to identify AD and other types of dementia.

RESULTS

Dataset 1 showed 22 upregulated and 21 downregulated mRNAs. A panel of six mRNAs distinguished AD from the control group in Dataset 2. The panel was used to successfully differentiate AD from other types of dementia in Dataset 3.

CONCLUSIONS

An AD-specific panel of six mRNAs was created that can be used for AD diagnosis.

Transcriptome analysis preliminary reveals the immune response mechanism of golden cuttlefish (Sepia esculenta) larvae exposed to Cd

As a well-known marine metal element, Cd can significantly affect bivalve mollusk life processes such as growth and development. However, the effects of Cd on the molecular mechanisms of the economically important cephalopod species Sepia esculenta remain unclear. In this study, S. esculenta larval immunity exposed to Cd is explored based on RNA-Seq. The analyses of GO, KEGG, and protein-protein interaction (PPI) network of 1,471 differentially expressed genes (DEGs) reveal that multiple immune processes are affected by exposure such as inflammatory reaction and cell adhesion. Comprehensive analyses of KEGG signaling pathways and the PPI network are first used to explore Cd-exposed S. esculenta larval immunity, revealing the presence of 16 immune-related key and hub genes involved in exposure response. Results of gene and pathway functional analyses increase our understanding of Cd-exposed S. esculenta larval immunity and improve our overall understanding of mollusk immune functions.

Genetic Mapping of Behavioral Traits Using the Collaborative Cross Resource

The complicated interactions between genetic background, environment and lifestyle factors make it difficult to study the genetic basis of complex phenotypes, such as cognition and anxiety levels, in humans. However, environmental and other factors can be tightly controlled in mouse studies. The Collaborative Cross (CC) is a mouse genetic reference population whose common genetic and phenotypic diversity is on par with that of humans. Therefore, we leveraged the power of the CC to assess 52 behavioral measures associated with locomotor activity, anxiety level, learning and memory. This is the first application of the CC in novel object recognition tests, Morris water maze tasks, and fear conditioning tests. We found substantial continuous behavioral variations across the CC strains tested, and mapped six quantitative trait loci (QTLs) which influenced these traits, defining candidate genetic variants underlying these QTLs. Overall, our findings highlight the potential of the CC population in behavioral genetic research, while the identified genomic loci and genes driving the variation of relevant behavioral traits provide a foundation for further studies.

Neurodegenerative Diseases: From Dysproteostasis, Altered Calcium Signalosome to Selective Neuronal Vulnerability to AAV-Mediated Gene Therapy

Despite intense research into the multifaceted etiology of neurodegenerative diseases (ND), they remain incurable. Here we provide a brief overview of several major ND and explore novel therapeutic approaches. Although the cause (s) of ND are not fully understood, the accumulation of misfolded/aggregated proteins in the brain is a common pathological feature. This aggregation may initiate disruption of Ca⁺⁺ signaling, which is an early pathological event leading to altered dendritic structure, neuronal dysfunction, and cell death. Presently, ND gene therapies remain unidimensional, elusive, and limited to modifying one pathological feature while ignoring others. Considering the complexity of signaling cascades in ND, we discuss emerging therapeutic concepts and suggest that deciphering the molecular mechanisms involved in dendritic pathology may broaden the phenotypic spectrum of ND treatment. An innovative multiplexed gene transfer strategy that employs silencing and/or over-expressing multiple effectors could preserve vulnerable neurons before they are lost. Such therapeutic approaches may extend brain health span and ameliorate burdensome chronic disease states.

Effect of acute Cu exposure on immune response mechanisms of golden cuttlefish (Sepia esculenta)

Sepia esculenta is a common economic cephalopod that has received extensive attention due to the tender meat, rich protein content and certain medicinal value thereof. Over the past decade, multiple industries have discharged waste into the ocean in large quantities, thereby significantly increasing the concentration of heavy metals in the ocean. Copper (Cu) is a common heavy metal in the ocean. The increase of Cu content will affect numerous biological processes such as immunity and metabolism of marine organisms. High concentrations of Cu may inhibit S. esculenta growth, development, swimming, and other processes, which would significantly affect its culture. In this research, transcriptome analysis is used to initially explore Cu-exposed S. esculenta larval immune response mechanisms. And compared to control group with normally growing larvae, 2056 differentially expressed genes (DEGs) are identified in experimental group with Cu-exposed larvae. The results of DEGs functional enrichment analyses including GO and KEGG indicate that Cu exposure might promote inflammatory and innate immune responses in cuttlefish larvae. Then, 10 key genes that might regulate larval immunity are identified using a comprehensive analysis that combines protein-protein interaction (PPI) network and KEGG functional enrichment analyses, of which three genes with the highest number of protein interactions or involve in more KEGG signaling pathways are identified as hub genes that might significantly affect larval immune response processes. Comprehensive analysis of PPI network and KEGG signaling pathway are used for the first time to explore Cu-exposed S. esculenta larval immune response mechanisms. Our results preliminarily reveal immune response mechanisms of cephalopods exposed to heavy metals and provide valuable resources for further understanding mollusk immunity.

Relation of CDC42, Th1, Th2, and Th17 cells with cognitive function decline in Alzheimer's disease

OBJECTIVE

Cell division cycle 42 (CDC42) regulates neurite outgrowth, neurotransmitter, and T help (Th) cell-mediated neuroinflammation, while its clinical implication in Alzheimer's disease (AD) is not clear. The present study aimed to investigate the correlation of CDC42 with Th1, Th2, and Th17 cells, as well as CDC42' longitudinal change and relation to cognitive function decline in AD patients.

METHODS

150 AD patients were enrolled, then their blood Th1, Th2, and Th17 cells were quantified by flow cytometry at baseline; CDC42 was detected by RT-qPCR and MMSE score was assessed at baseline and during 3-year follow-up. Meanwhile, CDC42, Th1, Th2, and Th17 cells were quantified in 30 Parkinson's disease (PD) patients and 30 healthy controls (HCs).

RESULTS

CDC42 (p < 0.001) and Th2 cells (p < 0.001) were lowest in AD patients, followed by PD patients, highest in HCs; but Th1 cells (p = 0.001) and Th17 cells (p < 0.001) showed opposite trends. CDC42 was not related to Th1 cells (p = 0.134), positively correlated with Th2 cells (p = 0.023) and MMSE (p < 0.001), while negatively associated with Th17 cells (p < 0.001) in AD patients. CDC42 was only related to Th17 cells (p = 0.048) and MMSE (p = 0.048) in PD patients; and it was not linked with Th1, Th2, Th17 cells, or MMSE in HCs (all p > 0.05). During a 3-year follow-up, CDC42 was gradually declined in AD patients (p < 0.001), its decline was positively correlated with MMSE decline at 1 year (p = 0.004), 2 years (p = 0.005), and 3 years (p = 0.026).

INTERPRETATION

CDC42 might have the potency to serve as a biomarker for estimating AD risk and progression.

Recognition of a Novel Gene Signature for Human Glioblastoma

Glioblastoma (GBM) is one of the most common malignant and incurable brain tumors. The identification of a gene signature for GBM may be helpful for its diagnosis, treatment, prediction of prognosis and even the development of treatments. In this study, we used the GSE108474 database to perform GSEA and machine learning analysis, and identified a 33-gene signature of GBM by examining astrocytoma or non-GBM glioma differential gene expression. The 33 identified signature genes included the overexpressed genes COL6A2, ABCC3, COL8A1, FAM20A, ADM, CTHRC1, PDPN, IBSP, MIR210HG, GPX8, MYL9 and PDLIM4, as well as the underexpressed genes CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C, SHANK2 and VIPR2. Protein functional analysis by CELLO2GO implied that these signature genes might be involved in regulating various aspects of biological function, including anatomical structure development, cell proliferation and adhesion, signaling transduction and many of the genes were annotated in response to stress. Of these 33 signature genes, 23 have previously been reported to be functionally correlated with GBM; the roles of the remaining 10 genes in glioma development remain unknown. Our results were the first to reveal that GBM exhibited the overexpressed GPX8 gene and underexpressed signature genes including CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C and SHANK2, which might play crucial roles in the tumorigenesis of different gliomas.

Identification of Prognostic Markers of N6-Methylandenosine-Related Noncoding RNAs in Non-Small-Cell Lung Cancer

Background

Non-small-cell lung cancer (NSCLC) is a major type of lung carcinoma that threatens the health and life of humans worldwide. We aimed to establish an n6-methyladenosine (m6A)-relevant ncRNA model to effectively evaluate the outcome of patients.

Methods

m6A-Related ncRNAs (lncRNA/miRNA) were acquired from the UCSC Xena database. Pearson's correlation analysis among 21 m6A regulatory factors and ncRNAs were implemented to explore m6A-relevant ncRNAs. Weighted gene co-expression network analysis (WGCNA) identified hub modules of gene associated with prognosis of NSCLC patients. Univariate Cox regression analysis identified 80 m6A-related ncRNAs. Least absolute shrinkage and selector operation (LASSO) filtered out redundant factors and established a risk score model (m6A-NSCLC) in the TCGA training data set. Validation of prognostic ability was performed using testing data sets from the TCGA database. We also conducted a correlation analysis among the risk score and different clinical traits. Both univariate and multivariate Cox analyses were combined to verify prognostic factors which have independent value, and a nomogram on the basis of m6A-NSCLC risk scores and clinical traits was constructed to assess the prognosis of patients. In addition, we screened differentially expressed genes (DEGs) based on different risk scores and performed enrichment analysis. Finally, 21 m6A regulators were detected to be differentially expressed between two risk groups.

Results

An m6A-NSCLC risk model with 18 ncRNAs was constructed. By comparison with low-risk patients, high-risk score patients had poor prognosis. The distribution of risk score in the tumor size and extent (T), number of near lymph nodes (N), clinical stage, sex, and tumor types was significantly different. The risk score could act as an independent prognostic factor with the nomogram assessing overall survival in NSCLC. DEGs inherent to cell movement and immune regulation were involved in NSCLC development. Furthermore, 18 of 21 m6A regulators were differentially expressed, implying their correlation to survival prognosis.

Conclusion

The m6A-NSCLC could be effectively utilized for evaluation of prognosis of patients.

Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study

Type 2 diabetes mellitus (T2DM) affects the formation of carotid atherosclerotic plaques (CAPs) and patients are prone to plaque instability. It is crucial to clarify transcriptomics profiles and identify biomarkers related to the progression of T2DM complicated by CAPs. Ten human CAP samples were obtained, and whole transcriptome sequencing (RNA-seq) was performed. Samples were divided into two groups: diabetes mellitus (DM) versus non-DM groups and unstable versus stable groups. The Limma package in R was used to identify lncRNAs, circRNAs, and mRNAs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) network creation, and module generation were performed for differentially expressed mRNAs. Cytoscape was used to create a transcription factor (TF)-mRNA regulatory network, lncRNA/circRNA-mRNA co-expression network, and a competitive endogenous RNA (ceRNA) network. The GSE118481 dataset and RT-qPCR were used to verify potential mRNAs.The regulatory network was constructed based on the verified core genes and the relationships were extracted from the above network. In total, 180 differentially expressed lncRNAs, 343 circRNAs, and 1092 mRNAs were identified in the DM versus non-DM group; 240 differentially expressed lncRNAs, 390 circRNAs, and 677 mRNAs were identified in the unstable versus stable group. Five circRNAs, 14 lncRNAs, and 171 mRNAs that were common among all four groups changed in the same direction. GO/KEGG functional enrichment analysis showed that 171 mRNAs were mainly related to biological processes, such as immune responses, inflammatory responses, and cell adhesion. Five circRNAs, 14 lncRNAs, 46 miRNAs, and 54 mRNAs in the ceRNA network formed a regulatory relationship. C22orf34-hsa-miR-6785-5p-RAB37, hsacirc_013887-hsa-miR-6785-5p/hsa-miR-4763-5p/hsa-miR-30b-3p-RAB37, MIR4435-1HG-hsa-miR-30b-3p-RAB37, and GAS5-hsa-miR-30b-3p-RAB37 may be potential RNA regulatory pathways. Seven upregulated mRNAs were verified using the GSE118481 dataset and RT-qPCR. The regulatory network included seven mRNAs, five circRNAs, six lncRNAs, and 14 TFs. We propose five circRNAs (hsacirc_028744, hsacirc_037219, hsacirc_006308, hsacirc_013887, and hsacirc_045622), six lncRNAs (EPB41L4A-AS1, LINC00969, GAS5, MIR4435-1HG, MIR503HG, and SNHG16), and seven mRNAs (RAB37, CCR7, CD3D, TRAT1, VWF, ICAM2, and TMEM244) as potential biomarkers related to the progression of T2DM complicated with CAP. The constructed ceRNA network has important implications for potential RNA regulatory pathways.

Molecular Insight Into the Therapeutic Potential of Long Non-coding RNA-Associated Competing Endogenous RNA Axes in Alzheimer's Disease: A Systematic Scoping Review

Alzheimer’s disease (AD) is a heterogeneous degenerative brain disorder with a rising prevalence worldwide. The two hallmarks that characterize the AD pathophysiology are amyloid plaques, generated via aggregated amyloid β, and neurofibrillary tangle, generated via accumulated phosphorylated tau. At the post-transcriptional and transcriptional levels, the regulatory functions of non-coding RNAs, in particular long non-coding RNAs (lncRNAs), have been ascertained in gene expressions. It is noteworthy that a number of lncRNAs feature a prevalent role in their potential of regulating gene expression through modulation of microRNAs via a process called the mechanism of competing endogenous RNA (ceRNA). Given the multifactorial nature of ceRNA interaction networks, they might be advantageous in complex disorders (e.g., AD) investigations at the therapeutic targets level. We carried out scoping review in this research to analyze validated loops of ceRNA in AD and focus on ceRNA axes associated with lncRNA. This scoping review was performed according to a six-stage methodology structure and PRISMA guideline. A systematic search of seven databases was conducted to find eligible articles prior to July 2021. Two reviewers independently performed publications screening and data extraction, and quantitative and qualitative analyses were conducted. Fourteen articles were identified that fulfill the inclusion criteria. Studies with different designs reported nine lncRNAs that were experimentally validated to act as ceRNA in AD in human-related studies, including BACE1-AS, SNHG1, RPPH1, NEAT1, LINC00094, SOX21-AS1, LINC00507, MAGI2-AS3, and LINC01311. The BACE1-AS/BACE1 was the most frequent ceRNA pair. Among miRNAs, miR-107 played a key role by regulating three different loops. Understanding the various aspects of this regulatory mechanism can help elucidate the unknown etiology of AD and provide new molecular targets for use in therapeutic and clinical applications.

Identification of hub genes associated with cognition in the hippocampus of Alzheimer's Disease

Alzheimer’s Disease (AD) is a neurodegenerative disease featured by cognitive impairment. This bioinformatic analysis was used to identify hub genes related to cognitive dysfunction in AD. The gene expression profile GSE48350 in the hippocampus of AD patients aged >70 years was obtained from the Gene Expression Omnibus (GEO) database. A total of 96 differentially expressed genes (DEGs) were identified, and subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses; a protein–protein interaction (PPI) network was constructed. The DEGs were enriched in synapse-related changes. A protein cluster was teased out of PPI. Furthermore, the cognition ranked the first among all the terms of biological process (BP). Next, 4 of 10 hub genes enriched in cognition were identified. The function of these genes was validated using APP/PS1 mice. Cognitive performance was validated by Morris Water Maze (MWM), and gene expression by RT-qPCR, Cholecystokinin (CCK), Tachykinin precursor 1 (TAC1), Calbindin 1 (CALB1) were downregulated in the hippocampus. These genes can provide new directions in the research of the molecular mechanism of AD.

A 3-Gene-Based Diagnostic Signature in Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a chronic neurodegenerative disease. In this study, potential diagnostic biomarkers were identified for AD.

METHODS

All AD samples and healthy samples were collected from 2 datasets in the GEO database, in which differentially expressed genes (DEGs) were analyzed by using the limma package of R language. GO and KEGG pathway enrichment was conducted basing on the DEGs via the clusterProfiler package of R. And, the PPI network construction and gene prediction were performed using the STRING database and Cytoscape. Then, a logistic regression model was constructed to predict the sample type.

RESULTS

Bioinformatic analysis of GEO datasets revealed 2,063 and 108 DEGs in GSE5281 and GSE4226 datasets, separately, and 15 overlapping DEGs were found. GO and KEGG enrichment analysis revealed terms associated with neurodevelopment. Then, we built a logistic regression model based on the hub genes from the PPI network and optimized the model to 3 genes (ALDOA, ENC1, and NFKBIA). The values of area under the curve of the training set GSE5281 and testing set GSE4226 were 0.9647 and 0.7857, respectively, which implied the efficacy of this model.

CONCLUSION

The comprehensive bioinformatic analysis of gene expression in AD patients and the effective logistic regression model built in our study may provide promising research value for diagnostic methods of AD.

A Signature of Five Long Non-Coding RNAs for Predicting the Prognosis of Alzheimer's Disease Based on Competing Endogenous RNA Networks

Long non-coding RNAs (lncRNAs) play important roles in the pathogenesis of Alzheimer's disease (AD). However, the functions and regulatory mechanisms of lncRNA are largely unclear. Herein, we obtained 3,158 lncRNAs by microarray re-annotation. A global network of competing endogenous RNAs (ceRNAs) was developed for AD and normal samples were based on the gene expressions profiles. A total of 255 AD-deficient messenger RNA (mRNA)-lncRNAs were identified by the expression correlation analysis. Genes in the dysregulated ceRNAs were found to be mainly enriched in transcription factors and micro RNAs (miRNAs). Analysis of the disordered miRNA in the lncRNA-mRNA network revealed that 40 pairs of lncRNA shared more than one disordered miRNA. Among them, nine lncRNAs were closely associated with AD, Parkinson's disease, and other neurodegenerative diseases. Of note, five lncRNAs were found to be potential biomarkers for AD. Real-time quantitative reverse transcription PCR (qRT-PCR) assay revealed that PART1 was downregulated, while SNHG14 was upregulated in AD serum samples when compared to normal samples. This study elucidates the role of lncRNAs in the pathogenesis of AD and presents new lncRNAs that can be exploited to design diagnostic and therapeutic agents for AD.