Background: Alzheimer's disease (AD) occurs in the elderly and pre-elderly, characterized by decline of memory, cognitive dysfunction, impairment of learning capacity, and motor dysfunction. Recently a competitive endogenous RNA (ceRNA) network has been found to be related to AD progression, but there is still little understanding of the ceRNA regulatory network in AD. This study aims to explore the important regulatory mechanisms of ceRNA regulatory networks containing long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) in AD.
Methods: Data from the gene expression omnibus (GEO) database were used for the analysis. To study enrichment function for the upregulated and downregulated mRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Metascape database, respectively. Based on the STRING database and Cytoscape software 3.9.1, a protein-protein interaction (PPI) network was constructed. The hub genes in this network were identified utilizing the CytoHubba plugin in Cytoscape. The TargetScan, miRWalk, and miRDB were selected to calculate the regulatory interaction between miRNAs and the hub genes. LncRNAs were predicted using RNA22. Additionally, circRNA prediction was executed using the circBank database.
Results: 711 downregulated and 670 upregulated overlapping mRNAs were identified between AD and control samples. 32 downregulated and 340 upregulated miRNAs were obtained from AD samples compared with control samples. 78 upregulated and 205 downregulated circRNAs were screened. 275 upregulated lncRNAs and 209 downregulated lncRNAs were found between AD samples and control samples. The PPI network constructed consists of 1016 nodes and 13,946 edges. Ten hub genes were selected to identify target miRNAs and ceRNAs. On the basis of the ceRNA hypothesis, a circRNA/lncRNA-miRNA-mRNA network was established. It included five lncRNAs (TRHDE-AS1, SNHG10, OIP5-AS, LINC00926 and LINC00662), 26 circRNAs, five miRNAs (hsa-miR-3158-3p, hsa-miR-4435, hsa-let-7d-3p, hsa-miR-330-5p and hsa-miR-3605-3p), and ten mRNAs (, , , , , , , , , and ). and were found to be significantly associated with AD pathology in tau and Aβ line AD models by the AlzData database. The study discovered the significance of several lncRNA-miRNA-mRNA axes and circRNA-miRNA-mRNA axes that included and .
Conclusions: ceRNAs were found to be important regulators in the development of AD and provide potential biological therapy targets for AD management.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.31083/j.jin2206136 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Metabolic reprogramming induced by PSMA4 overexpression facilitates bortezomib resistance in multiple myeloma.
Ann Hematol
January 2025
Department of Hematology, Navy Medical Center of PLA, Naval Medical University, No. 338 West Huaihai Road, Changning District, Shanghai, 200052, China.
Multiple myeloma(MM) remains incurable with high relapse and chemoresistance rates. Differentially expressed genes(DEGs) between newly diagnosed myeloma and secondary plasma cell leukemia(sPCL) were subjected to a weighted gene co-expression network analysis(WGCNA). Drug resistant myeloma cell lines were established.
View Article and Find Full Text PDFEnhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection.
Transl Oncol
January 2025
Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA. Electronic address:
Background: Colorectal cancer (CRC) presents significant challenges in chemotherapy response prediction due to its molecular heterogeneity. Current methods often fail to account for the complexity and variability inherent in individual tumors.
Methods: We developed a novel approach using matched CRC tumor and organoid gene expression data.
Development of machine learning models for diagnostic biomarker identification and immune cell infiltration analysis in PCOS.
J Ovarian Res
January 2025
Reproductive Medicine Center, Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as hyperandrogenemia, oligo or anovulation and polycystic ovarian, significantly impacting quality of life. However, the practical implementation of machine learning (ML) in PCOS diagnosis is hindered by the limitations related to data size and algorithmic models.
View Article and Find Full Text PDFTranscriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto's thyroiditis.
Sci Rep
January 2025
Department of Medical Ultrasound, The Second Affiliated Hospital, Xi'an Jiaotong University, 157 Xiwu Road, Xi'an, 710004, China.
While ultrasonography effectively diagnoses Hashimoto's thyroiditis (HT), exploring its transcriptomic landscape could reveal valuable insights into disease mechanisms. This study aimed to identify HT-associated RNA signatures and investigate their potential for enhanced molecular characterization. Samples comprising 31 HT patients and 30 healthy controls underwent RNA sequencing of peripheral blood.
View Article and Find Full Text PDFMolecular mechanisms of libido influencing semen quality in geese through the hypothalamic-pituitary-testicular-external genitalia axis.
Poult Sci
December 2024
Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, PR China; State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, PR China. Electronic address:
Libido plays a crucial role in influencing semen quality, yet the underlying regulatory mechanisms remain unclear. As a central axis in male goose reproduction, the hypothalamic-pituitary-testicular-external genitalia (HPTE) axis may contribute to the regulation of this process. In this study, we established a rating scale for goose libido based on average number of massages to erection (ANM) and the erection type, and evaluated semen quality across the entire flock.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!